ABSTRACT 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.
ABSTRACT 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.
This study investigates the implications of common analysts covering both sides of a buyer-supplier dyad for suppliers' operational efficiencies. Drawing on the knowledge sharing perspective, we consider a positive association between common analysts and supplier operational efficiency. Using 20,359 buyer-supplier dyads collected from Compustat during the period 1994 to 2017, we empirically find that the existence of common analysts significantly enhances suppliers' operational efficiencies. Additionally, the positive association is amplified when the supplier's bargaining power is lower or the buyer's demand uncertainty is higher. We further examine the underlying mechanisms through which common analysts improve supplier operational efficiency by showing that common analysts transmit buyer-related information to suppliers via both private and public channels and help suppliers reduce bullwhip effects. Finally, compared with other relational ties, common analysts uniquely mitigate suppliers' bullwhip effects and, in turn, have a stronger positive impact on their operational efficiencies. Collectively, our evidence highlights the distinctive and important role of common analysts in transferring operational information along supply chains and improving supply base outcomes.
The increasing cost of last-mile delivery has motivated omnichannel grocery retailers to implement crowdshipping. Crowdshipping is a platform-based last-mile delivery model that relies on engaging drivers who are independent contractors to quickly accept delivery tasks. While studies often focus on the impact of monetary incentives, we draw on perspectives from the service operations literature to explore the moderating effect of three theoretical mechanisms (effort efficiency, uncertainty, and utility), on the curvilinear relationship between monetary incentives and driver engagement. Specifically, we test how delivery density, delivery type (attended vs. unattended), and time of the day interact with monetary incentives to influence driver's task acceptance response time. Econometric analyses of a dataset comprising about two million observations from a US Fortune 100 grocery retailer's crowdshipping platform confirm the "diminishing negative" effect of remuneration on task acceptance time. More importantly, we reveal that task operational characteristics affect how monetary incentives influence task acceptance time. While task density and unattended deliveries amplify the curvilinear relationship, evening tasks flatten it and are accepted slower than those scheduled in the daytime. Our findings suggest that drivers evaluate tasks based on perceived effort, uncertainty, and utility, offering valuable insights for platforms and future research.
Qualitative research has evolved into a valued scientific approach to generate novel theoretical contributions in operations management (OM). However, a limited understanding of what constitutes methodological rigor in qualitative research has resulted in a proceduralized application of research practices and templates, hindering further progress. After tracing the origins of discipline-specific rigor in OM-closely linked to the inductive (positivistic) case study method-we present an alternative perspective: rigor as researchers' demonstration of reflexivity in deliberate reasoning processes that infer theoretical insights from data. Based on a review of 87 qualitative research articles in three leading empirical OM journals (Journal of Operations Management, Production and Operations Management, and Journal of Supply Chain Management), published between 2015 and 2024, we trace the (limited) evolution of rigor in qualitative OM research across three time brackets. We then compare the current state of rigor in qualitative OM research with that of three leading management journals (Academy of Management Journal, Organization Science, and Strategic Management Journal) by examining the most recent time bracket (2023-2024). Through the analysis of discipline-specific rigor developments and the cross-field comparison, we identify areas of progress alongside persistent rigor gaps. Based on these gaps, we develop a series of probing questions to enhance researchers' reflexive reasoning, illustrated with examples from both fields. Our contribution provides a reflexivity-based framework that enables researchers to move beyond procedural compliance toward rigorous qualitative research, ultimately yielding OM theories with greater explanatory power.
In the digital age, industrial firms are facing significant challenges in achieving digitally-enabled safety management (DSM, i.e., leveraging digital technologies to mitigate industrial accidents) due to rigid institutional structures and underutilized digital technology resources. To address this, we develop a research model integrating institutional theory and resource-based view theory, and position safety management capabilities (i.e., the abilities of a firm to sense, seize, and reconfigure its safety operational routines and deploy new ones for DSM) as a pivotal process mechanism translating institutional forces (i.e., institutional isomorphism and top management support for DSM) and digital technology resources (i.e., digital technologies that are available and utilized for DSM) into improved safety performance. Using time-lagged survey data in combination with secondary data from 216 industrial firms in China, our findings reveal that institutional isomorphism and top management support exert cascading influences on safety management capabilities. The alignment of digital technology resources with institutional forces significantly facilitates the development of safety management capabilities, emphasizing their synergistic role in fostering adaptive and strategic safety practices. Furthermore, safety management capabilities serve as the critical intermediary enabling institutional and resource factors to drive superior safety outcomes. Our results advance the theory of safety management and provide actionable insights for industrial firms to effectively operationalize DSM.
Recall delays expose consumers to prolonged risk and undermine a firm's long-term performance and reputation. Building on agency theory's conceptualization of principal-agent relationships, we theorize that large institutional investors play an important monitoring role wherein their ownership encourages faster recalls. We then build on agency theory's core dimension of information asymmetry to examine whether R&D intensity and device class moderate this influence. Using a matched sample of 2932 medical device recalls involving severe defects across 69 firms from 2002 to 2020, we find that greater ownership by large institutional investors is associated with faster recalls such that a 1% increase in ownership yields a 24-day reduction in time-to-recall. This relationship is weakened by increases in R&D intensity and for high-risk devices. Our study highlights the importance of considering how operations management phenomena are influenced by large institutional investors, and we identify both firm- and device-level pathways that moderate this influence. While managers, policymakers, and regulators may be wary of the influence that large institutional investors have, our findings offer a previously unidentified benefit: faster recalls.
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.
Supplier exploitation, including financial squeezing, payment delays, and non-contractual demands, is a pervasive form of corporate misconduct. This multi-method study examines how investors interpret supplier exploitation amid competing ethical and financial considerations. Using an event study of 233 enforcement actions by the Korea Fair Trade Commission (KFTC), we find a significant negative investor reaction, with firms losing an average of 1.24% in market value. This negative market reaction is stronger for firms receiving greater media attention but weaker for more profitable buyers. To explore the underlying behavioral mechanisms, we conduct an incentivized vignette experiment with experienced investors. The experiment reveals that anticipated public moral judgment (viewing exploitation as wrongful) and profit-driven considerations (viewing exploitation as rational misconduct) jointly shape investor reactions. Specifically, buyer profitability tips the balance by reducing the weight placed on moral concerns while increasing the emphasis on financial considerations. Supplemented by practitioner interviews, this study provides novel evidence on the overall negative, yet complex economic consequences of supplier exploitation.
In supply chains, firms often become aware of illegal actions committed by their partners, prompting the critical question: when and why do those who know become those who act? Drawing on industry examples of supply chain fraud, we introduce the concept of supply chain guardianship to study how firms respond to potential fraud committed by their supply chain partners. We identify key influences on supply chain guardianship and refine these insights into hypotheses, which we test across four behavioral experiments (n = 1000). Study A finds that the tone at the top of an organization can promote supply chain guardianship and that state moral disengagement is negatively correlated with it. Study B manipulates process moral disengagement and finds that it reduces guardianship behavior. Although the network position of the supply chain guardian emerges as important in industry examples, we do not find a significant effect in the experiments. We replicate and validate these findings in Studies C and D. This study offers an initial foundation for a behavioral theory of interfirm fraud responses in supply chains and offers practical insights into how firms can increase supply chain guardianship across macro-, meso-, and microlevels.
This study offers a new application of signaling theory to better understand the role of equity linkages and political influence on buyer-supplier relationships (BSRs). We examine the signaling effects of a politician's personal equity investment in a buyer firm on the financial performance of the supplier firms located in the politician's constituency, that is, "constituent suppliers." Our results show that constituent suppliers' financial performance increases with the home politician ownership of their principal buyers, consistent with home politicians' incentives to benefit their constituencies and gain the support of local voters through promoting supplier-buyer relationships. Interestingly, such positive signaling effects on the financial performances of constituent suppliers vary with interdependence relationships between the home politician, investee buyer, and constituent supplier. The positive performance effect is stronger when investee buyers have greater political engagement, when politician owners hold more powerful congressional positions, and when the economic condition of the politician's constituency is poorer. In contrast, the positive performance effect is attenuated when buyers have higher economic dependence and switching costs vis-& agrave;-vis their suppliers. Our mechanism analyses reveal that the likelihood of increased sales as well as lowered operating and sales support costs partially mediate the relationship between supplier financial performance and home politician ownership in buyers, representing investee buyers' munificence. The investee buyers are also more likely to select firms located in their politician shareholders' constituencies as new suppliers, another mechanism of promoting business relationships with constituent firms. We further find that buyers with politician shareholders from their suppliers' constituencies receive more valuable government contract awards, and smaller firms exhibit significant performance gains. A series of endogeneity checks based on exogenous shocks and alternative measures highlight the robustness of our results. Our findings suggest that politicians' stock ownership in buyer firms signals secondary stakeholder influence and information sharing that shapes BSRs, promoting profitable business relationships for constituent suppliers.
The increasing availability of secondary data on supply chain relationships has created new opportunities for empirical research in supply chain management. Datasets from sources including Bloomberg SPLC, FactSet, and CompuStat may support empirical analyses of decision-making, strategic behaviors, governance mechanisms, and dynamics of inter-organizational relationships of supply chains. However, the complexity and interconnectedness of supply chains and the inconsistent quality and coverage of supply chain data sources present empirical challenges, which have limited the scope and depth of current empirical research on supply chains. To address these challenges, this study investigates and compares the three commonly used supply chain databases and introduces a data-focused roadmap for supply chain research using secondary data sources. This roadmap presents a process featuring data source selection, unit-of-analysis decisions, and appropriate econometric treatments, for example, endogeneity, selection bias, and correlated errors in supply chains, which contributes to the supply chain management literature by improving consistency, generalizability, and reliability in empirical supply chain research.
Continuous Improvement (CI) initiatives are central to operational excellence, emphasizing bottom-up, team-based problem-solving. In practice, however, they are embedded within hierarchical systems that require managerial oversight. This duality introduces a structural tension between empowerment and control, giving rise to a behavioral dynamic that we conceptualize as the fa & ccedil;ade of conformity (FC) in CI, a defensive impression-management behavior where team members outwardly express agreement with CI decisions while privately withholding dissent. We argue that FC in CI affects operational performance. We further theorize that collective team identification (CTI), a shared sense of belonging and commitment to goals, moderates this negative relationship. We test our theory in two complementary studies: a laboratory experiment involving 71 teams (284 participants) simulating ad-hoc CI, and a field study of 330 structured CI projects within a large financial services firm. Across both settings, we find that FC has a negative impact on operational performance, while a strong CTI mitigates this effect. We conduct extensive robustness and supplementary analyses to validate our results. This research contributes to behavioral operations and continuous improvement bodies of knowledge by introducing FC in CI as a distinct behavioral failure mode and identifying CTI as a boundary condition, providing managers with guidance on recognizing and addressing FC to safeguard CI efforts and investments.
Knowing the challenges of collaborating with a competitor in developing new technologies, firms sometimes still choose a competitor instead of a noncompeting technology provider. To explore why, this study adopts an inter-organizational trust view to explain the formation of a technology development outsourcing relationship. Using a vignette-based experiment with procurement managers, results show how three product- and competitor-related factors: product newness, competitor market size, and product substitutability, affect a purchasing manager's intention to choose a competitor. The post hoc analysis confirms the two sources of inter-organizational trust, competence and integrity, in explaining the influences of the three factors. Using results from two waves of interviews, a behavioral experiment and a rational agent math model, this study explains competitor selection behaviors in a triadic context with a noncompeting provider as the default option. Contributing to the interface of technology outsourcing, co-opetition in innovation, and supplier selection literature, these findings can help managers assess product and competitor attributes in deciding whether to collaborate with a competitor in a technology development outsourcing context.
When binary classification models are wrong, managers face misclassification costs. Although false positive outcomes imply unnecessary mitigation efforts, false negative outcomes imply overlooking the class of interest. Humans calibrate these ai models supporting operational systems by adjusting the decision threshold that translates prediction probability into either class. Results of our controlled laboratory experiment show that, despite all relevant information being available, decision makers systematically deviate from the optimal cost-efficient threshold. We observe a significant interaction effect of class and cost imbalance on this deviation, which increases in high-stakes settings where more extreme thresholds are optimal. When unit costs are different, we find that participants anchor on the threshold where expected misclassification costs for false alarms and missed hits are equal, whereas mean anchoring cannot explain the pull-to-center behavior sufficiently. Surprisingly, we confirm that this impulse balance equilibrium also serves as attractive anchor in our setting, where decisions are made ex ante without loss aversion. To debias decision makers, simulated responses with behavior-aware costs show that subjects are nudged to make choices closer to the optimum. Managers should be aware of this boundedly rational behavior and complementary debiasing techniques, as sub-optimal threshold setting results in 53% higher misclassification costs, on average.
Not every R&D project will succeed, necessitating a careful selection of which R&D projects to pursue and which to terminate. Timely termination decisions free up scarce resources for more promising projects. Yet, prior research has yielded inconclusive results on how firms terminate exploratory versus exploitative R&D projects. Given that early-stage R&D project termination is a decision made under high uncertainty, we adopt a behavioral perspective to reconcile the inconsistent findings. We propose that a firm's preference for terminating exploratory versus exploitative projects depends on three sources of contextual feedback: parallel projects, prior collaboration experience, and firm performance. We test our hypotheses using drug development projects from pharmaceutical firms over 11 years, finding that exploratory projects are less likely to be terminated relative to exploitative ones when the number of parallel projects is limited, when they are conducted with an existing partner, or when firm performance falls below aspiration.
Substantial empirical evidence shows that suppliers in emerging economies can enhance their technological capabilities through direct learning from technologically advanced foreign competitors. However, suppliers in emerging markets may struggle to learn directly from knowledge about competitors’ products that are not widely available on the consumer market. We draw on insights from existing literature, explorative interviews, and anecdotal evidence to hypothesize that firms may resort to indirect learning channels by leveraging downstream customers as knowledge conduits. Using the Chinese manufacturing industry's import data from 2001 to 2015, our quantitative study reveals the innovation premium of such an indirect learning channel that we term as ‘ second-order between-supplier learning ’: suppliers’ technological capabilities improve significantly when they supply to domestic customers who have imported from their technologically more advanced foreign competitors. Through a qualitative study, we develop a theoretical framework to account for the mechanisms underlying this indirect learning channel, outlining the motivations , contents , and contingencies that shape the effectiveness of second-order between-supplier learning. Our findings contribute to supply chain management and organizational learning literature by building a theory of second-order between-supplier learning. Our findings could also inform suppliers in emerging markets about technological development and guide policymakers on cross-border supply chain management.
What are the distinct configurations of contract characteristics associated with the success of inter-organizational outsourcing projects across different technology paradigms? We examine information technology outsourcing contracts between 1991 and 2009 to address this question by using a relatively new approach based on qualitative comparative analysis. We consider four technology paradigms: pre-Internet (1991-1996), pre-Dotcom (1997-2000), post-Dotcom (2001-2005), and Cloud Computing (2006-2009). We discuss issues related to adverse selection and moral hazard and identify five key contract characteristics that determine contract success: new contract, existing organizational relationship, long contract duration, fixed price, and competitive bidding. Our analyses document two key findings. First, we show that configurations of contract characteristics for success and failure of outsourcing projects are different across technology paradigms. Second, we identify three themes in configurations associated with outsourcing success-economic imperative, conservative relational, and conservative imperative. These themes extend prior work that draws on transaction cost economics, social exchange theory, and relational exchange theory and identify an increasing emphasis on the relational component to manage contracting risk for outsourcing success over time. From a managerial perspective, we provide context-sensitive causal recipes to choose configurations of contract characteristics, considering technology paradigms. Together, our findings provide new insights for developing cumulative knowledge for understanding the determinants of success of interorganizational outsourcing projects while opening new avenues for further theorizing and empirical testing.
