ABSTRACT Despite the widely recognized importance of integrative practices in commercial supply chain operations, their application in humanitarian supply chains (HSCs) remains underexplored. HSCs differ from commercial supply chains in their relational dynamics and integration processes, operating under extreme uncertainty, demand volatility, infrastructural disruption, and resource scarcity across a complex web of actors. Using an embedded single‐case design, this study assesses supplier integration (SI) in humanitarian operations in a leading international humanitarian organization (IHO), as well as the IHO's relationships with eight key suppliers. Drawing on the relational lenses of power, trust, and commitment, distinct integration patterns were identified across different supplier types. The findings highlight humanitarian‐specific contextual constraints and are used to inform a typology of SI feasibility encompassing feasible, selectively feasible, and low‐feasibility integration. This typology provides a foundation for guiding supplier relationship management in complex humanitarian contexts. This study offers a framework for understanding how relational and structural conditions shape integration in humanitarian settings and underscores the need for IHOs to adopt context‐appropriate supplier management approaches that reflect the unique dynamics of humanitarian operations.
Revenue-based lending (RBL) has emerged as a prominent alternative to traditional fixed-term debt for small and medium-sized enterprises (SMEs), whose survival often depends on operational resilience, defined as the ability to maintain essential business functions under demand volatility, liquidity shocks, and external disruptions. Because SMEs frequently face severe credit frictions, short cash-to-cash cycles, and limited access to traditional operational buffers, timely and predictable financing can create financial slack that helps them absorb short-term disruptions. In RBL, repayment is tied to realized revenue, but the resilience benefits of this model depend on a stable and responsive supply of investor capital. Drawing on behavioral operations, investor psychology, and OM research on financial slack, we examine whether investor-directed digital nudges can increase investor engagement and, through more timely and reliable capital flows, strengthen SME operational resilience. Collaborating with a major RBL platform in Asia, we conduct a large-scale randomized field experiment in which investors are randomly assigned to one of five messages: (i) cooperative social-value message (ii) competitive social-value message, (iii) precommitment message designed to mitigate time-inconsistent preferences, (iv) ease-and-convenience message designed to reduce perceived cognitive costs, or a plain message control that accounts for attention effects. Our results show that the ease-and-convenience-framed nudge significantly increases investor engagement, leading to more timely and predictable funding aligned with SMEs' operational needs. These improved capital flows reduce delinquency and shorten payoff durations, suggesting that investor-side nudges can strengthen borrower resilience by creating financial slack that helps SMEs maintain operational continuity under cash-flow pressure. Our empirical evidence combines an investor-side online experiment, a borrower-side mechanism experiment, and semi-structured interviews with SME borrowers. Together, these analyses show that ease-and-convenience messaging is especially effective for long-tenured, risk-averse, institutionally affiliated, and urban-based investors, while borrower-side evidence suggests that financial slack is the most strongly supported borrower-side mechanism through which timely and predictable funding helps SMEs absorb short-term shocks, meet urgent operating obligations, and maintain repayment continuity. By documenting how investor-side nudges generate cross-side benefits for SME borrowers through financial slack and buffering, this study advances behavioral operations research on context-dependent nudging, extends OM work on platform-mediated resilience, and offers guidance for fintech platforms seeking to stabilize capital flows to vulnerable SMEs.
A hierarchical structure over product attributes is a central input to many product assortment and inventory optimization models, yet no existing method can recover such structures from the aggregate sales data that retailers routinely collect. We introduce HASTERAD (hierarchical attribute substitution tree estimation via recursive attribute discovery), which recovers a substitution tree from standard SKU-store-week scanner data. Leveraging variation in prices and promotions, HASTERAD compares attribute-based substitution patterns and then recursively partitions the product space to recover a candidate hierarchy of attribute-level substitution. Applied to the IRI Marketing Data Set, it recovers a stable and economically interpretable tree. To quantify its economic value, we execute a three-step optimization process on synthetic data with known ground truth: we recover a tree with HASTERAD, estimate demand parameters on that tree, and optimize assortments. HASTERAD's chosen assortments forgo less than 11% of the profit available to a clairvoyant planner, even in demanding settings where a traditional flat demand model forgoes almost 100%. Performance remains strong whether the method is applied to a multi-store panel, where stockouts are unobservable, or to a single store's internal records, where data are sparse but stockouts are observed. These findings are robust across several alternative specifications. Our central empirical finding concerns model selection. A tree chosen by best in-sample fit, from an exhaustive search over simple hierarchies, fits the observed sales nearly as well as HASTERAD's recovered tree, yet prescribes assortments whose expected profits are substantially lower. Fit evaluates observed purchases, but assortment optimization depends on predicting demand under assortments not yet observed.
Carbon emissions trading schemes (CETS) are a prominent market-based instrument for addressing climate change, yet their effects on corporate disclosure remain insufficiently understood. Drawing on institutional theory, we develop a pressure-capability-cognition framework to examine how CETS influence firms' environmental and social (E&S) disclosure and how institutional pressures and internal organizational factors shape heterogeneous disclosure responses. Exploiting the staggered implementation of China's CETS between 2013 and 2024 as a quasi-natural experiment, we employ a multi-period difference-in-differences design using 16,348 firm-year observations from 1682 listed firms between 2010 and 2024. We report three principal findings. First, CETS significantly increase the level of firm E&S disclosure, indicating that the influence of CETS extends beyond environmental reporting to social responsibility disclosure. Second, institutional pressures generate distinct disclosure strategies. Under coercive participation, CETS exhibits a marginally significant positive effect on social disclosure but no significant effect on environmental disclosure, consistent with a legitimacy compensation mechanism whereby firms offset environmental legitimacy deficits through low-cost, highly visible symbolic reporting. By contrast, under non-coercive participation, CETS significantly promotes both environmental and social disclosure, suggesting that firms use disclosure as a strategic signaling device. Third, operational efficiency negatively moderates the CETS-disclosure relationship, whereas managerial environmental focus positively moderates this relationship, revealing that internal capabilities and managerial cognition condition the effectiveness of institutional pressures in shaping disclosure outcomes. These findings contribute to sustainable operations management research by demonstrating how institutional pressures interact with operational capabilities and managerial cognition to shape corporate sustainability disclosure.
