In this paper, I study the emerging political depolarization field in the contemporary US through a network analysis of civil society organizations working to overcome political divides. Because polarization can prevent us from not only solving other social problems, but the problem of polarization itself, I look specifically at whether organizations directly focused on bridging—who represent the core of the supposedly non-partisan depolarization field—practice what they preach by embodying non-partisanship and engaging in cross-partisan ties within their own interorganizational environment. Drawing on data compiled from the Princeton Bridging Divides Initiative, Twitter/X, and AllSides media, I analyze organizations’ partisanship and use Exponential Random Graph Models (ERGMs) to assess the likelihood for bridging organizations to form ties across political differences within the political depolarization field. I find that while bridging-focused organizations are left of center overall, they are relatively less left-leaning than their nonbridging counterparts. And, while bridging organizations show no difference in forming ties across political differences in general, they are more likely to connect with politically different organizations when those organizations have a nonbridging focus. I argue that this pattern reflects the strength of the non-partisan ideal at the core of the bridging movement, where bridging organizations apply stricter partisan standards to fellow bridging organizations than to nonbridging organizations for whom political difference is expected and unremarkable. I discuss the implications of these findings for the prefigurative commitments of the bridging movement and for tensions between civic and partisan logics within the broader depolarization field.
PurposeThe design of multi-tier supply chain networks is a central but underexplored matter in the literature. How firms determine the optimal number of supplier tiers can impact their resilience and visibility. This study addresses this gap by considering the concept of reachability of the focal firm. It examines how network structure, based on centrality metrics, affects firm performance and resilience.Design/methodology/approachThe study uses a two-phase, mixed methods design. In study 1, a reachability analysis of the US pharmaceutical supply network helped determine the optimal number of upstream tiers given random disruption scenarios. This analysis identifies a convergence tier, a point after which added visibility has diminishing returns. In study 2, the impact of focal firm's position in the network on firm performance is investigated. This study uses degree and betweenness centrality as structural indicators to test the relationship between financial performance and financial resilience using econometric methods.FindingsThe findings from Study 1 show that reachability decreases non-linearly as the number of tiers increases. This indicates that there is a finite and quantifiable range of visibility that maximizes the focal firm access (reachability) to the suppliers under random disruptions. In Study 2, it was found that betweenness centrality, which reflects brokerage positions within the network, significantly enhances financial performance. Additionally, degree centrality, which indicates the number of direct supplier connections, has a positive impact on financial resilience.Originality/valueThis study presents a data-driven approach to determine the optimal monitoring depth in complex supply networks, linking it to firm-level financial outcomes. By combining reachability analysis and centrality metrics, it connects network design with organizational resilience. This work offers a quantitative framework to help firms build resilient, disruption-ready supply networks.
With the ongoing deployment of AI algorithms, managers do not know whether existing demand planning processes account for possible differences in human behavior when using AI-based systems in comparison to legacy model-based systems. This study examines how human behavior may differ when performing demand forecasting tasks due to the disclosure of algorithm type (AI or model) along with associated algorithm performance (low and improving). Using signaling theory, we hypothesize that algorithm type and performance influence user forecast adjustment behavior. We find support for these predictions across two laboratory experiments and a large quasi-natural field experiment with approximately 575,000 observations from a multinational retailer. We find no significant direct effect of algorithm type independent of performance in the lab. In contrast, in the field, users implement significantly greater adjustments for AI-based algorithms compared to model-based algorithms. Across both contexts, algorithm performance, whether low or improving, has a significant direct effect on user adjustments, with users adapting their behavior to the algorithm's performance. Finally, we find that in the lab and the field, users' responses to low performance are amplified when the forecasts originate from AI-based algorithms. Our findings underscore the nuance and complexity in which users interact with AI-based algorithms compared to model-based algorithms and demonstrate the value of signaling theory for understanding human-AI collaboration.
Justice system prevention research has focused heavily on risk factors, overlooking how positive psychological assets may protect vulnerable youth. This study addresses that gap by testing whether optimism mediates the relationship between childhood adversity and arrest in emerging adulthood, and whether this pathway differs by sex. Using data from 2,990 participants in the Future of Families and Child Wellbeing Study, adversity was measured at age nine, optimism at fifteen, and arrest at twenty-two. Logistic regression and mediation analyses showed that optimism partially mediated the adversity-arrest link, with stronger effects for males. Findings highlight optimism as a modifiable target for prevention, suggesting that fostering positive future expectations may reduce justice involvement, especially among high-risk boys.
Base of the pyramid (BoP) scholarship emphasizes the potential for enterprises to generate profits by enhancing the well-being of the impoverished, often by selling socially-beneficial products (SBPs). Yet, many such BoP enterprises have experienced mixed results. We argue that this may stem from insufficient attention to how BoP consumers value SBPs and other products. We frame SBPs as a type of human capital investment and thus use human capital theory to study how the consumption of SBPs does or does not create value for consumers. Specifically, we use a novel data set from rural India to measure value creation in terms of changes in well-being. We test competing hypotheses about how consumer resource (dis)advantages influence well-being changes associated with SBP consumption. Our results show support for both the logics of cumulative advantage and diminishing marginal returns. Further analysis indicates that the source of the resource (dis)advantage, whether it is acquired or ascribed, explains these results. These findings help to humanize the BoP by showing how well-being changes depend on the details of consumers’ lives. The fact that differences in starting levels of human capital influence subsequent value creation offers insights that could lead to better enterprise performance in impoverished contexts. The paper closes by exploring ethical issues associated with BoP enterprises and encouraging SBP consumption.
