Knowledge that Transforms

To address climate change, firms that pursue carbon neutrality might adopt various CO 2 abatement technologies, including purchasing carbon credits; investing in third-party CO 2 capture and storage; or redesigning products, processes, buildings, and energy sources to abate CO 2 emissions. Existing research yields mixed evidence of the effects of such actions, especially technological choices, on shareholder value though. Building on the Natural Resource-Based View, this article seeks to establish the relationship of different CO 2 abatement technologies with shareholder value, using multiple event studies of firm-level carbon neutrality announcements and technology-level CO 2 abatement announcements issued in the United States between 2014 and 2024. Of the various CO 2 prevention (insetting) technologies, product and production technologies effectively create shareholder value, by establishing a difficult-to-imitate source of competitive differentiation, whereas investments in green transportation, renewable energy, and buildings exert neutral effects on shareholder value. Among the various CO 2 control (offsetting) technologies, investments in CO 2 capture and storage technologies destroy shareholder value. Although purchasing carbon credits has a neutral effect in general, they can destroy shareholder value when announced in isolation, unbundled from other abatement technologies. These nuanced findings indicate that CO 2 control technologies do not undermine shareholder value, as long as they offer an inexpensive means to adjust investments in offsets over time, to complement the insetting efforts. This research thus expands sustainable operations literature by clarifying the relationship between CO 2 abatement technologies and shareholder value, while also providing initial cues of some underlying mechanisms. These insights in turn can inform managers and policymakers seeking to decrease risk while also accelerating climate transitions.

Firms commonly use audits to ensure their suppliers comply with social responsibility standards. Yet audits are imperfect: a type I error occurs when a compliant supplier is mistakenly flagged as non-compliant, and a type II error occurs when a non-compliant supplier is overlooked. We study an assembly network in which a firm sources inputs from two suppliers and designs an audit program under a limited budget. For each audited supplier, the firm determines the audit accuracy and the associated type I and type II error rates. Upon a failed audit, the firm decides whether to rectify the supplier or substitute it with a compliant one. We show that the firm's audit strategy depends on the relative costs of substitution and rectification, as well as the allocated budget. When both mitigation costs are high or one of them is low, the firm always prioritizes addressing a specific type of error, regardless of the budget level. When both mitigation costs are moderate and substitution is cheaper than rectification, the budget level becomes pivotal. With a higher budget, the firm may prioritize reducing type I errors and substituting flagged suppliers to leverage cost advantages of substitution. With a tighter budget, the firm instead prioritizes reducing type II errors and relies on rectification to avoid potential reputational damage from type I errors. Interestingly, even in a symmetric network, the firm may allocate the budget unevenly: assigning more resources to one supplier to take advantage of low-cost substitution, while allocating less to the other and relying on rectification when needed. Surprisingly, as the budget increases, the firm may shift from auditing both suppliers to auditing only one, improving audit accuracy for the audited supplier while accepting a fixed expected loss from the unaudited one. Moreover, a larger budget may increase the probability that a non-compliant final product reaches the market, because the firm may switch from eliminating type II errors to controlling type I errors. The optimal audit program differs sharply from the one that ignores type I errors, which can result in inefficient budget allocation, misguided mitigation efforts, and avoidable reputational and economic losses. Our findings highlight the importance of jointly managing both types of audit errors and strategically adapting the audit scope, intensity, and corrective actions in socially responsible sourcing.

We examine a firm’s personalized pricing (PP) strategies in markets where consumers are uncertain about product quality. In such markets, prices serve not only as a tool for price discrimination but also as a means of conveying quality information to consumers. We reveal that a firm faces a tradeoff between adopting PP to better price discriminate among consumers and not adopting it to signal its high quality. We find that a high-quality firm should adopt PP only when its product quality is known to either a very small or a very large fraction of consumers, and when its high-quality product, on average, offers either very low or very high additional value to consumers relative to a low-quality product. Moreover, the high-quality firm may charge consumers personalized prices less than their willingness to pay to signal its quality in equilibrium, deviating from first-degree price discrimination when consumers are informed about quality. Counterintuitively, when more consumers know product quality or when the high-quality product provides higher average value to consumers, consumer surplus and social welfare may decrease, but a low-quality firm’s profit may increase. Furthermore, the firm’s profit can be lower when its personalized pricing leverages more information about consumer characteristics. Randomized experiments provide evidence that a personalized price is a weaker signal of objective product quality than a uniform price.

Businesses selling in online marketplaces often enhance their offerings with short delivery times, but short delivery times typically result in low delivery-time conformance—the probability of on-time delivery, often published in the form of delivery-time ratings. As a result, short delivery times may simultaneously enhance and undermine the perceived value of online offerings. Marketplace operators can moderate this trade-off between delivery time and delivery-time conformance by making delivery-time ratings available or not. We (i) characterize the conditions for the emergence of the trade-off, (ii) identify the mechanisms that may allow managers to preempt the trade-off, and (iii) outline the conditions under which it is profitable for online retailers to solicit and publish delivery-time ratings. We model and compare alternative scenarios for the supply chain of a retailer and a supplier that offer a product in an online marketplace and determine prices and promised delivery times. We develop analytic models and support them with data provided by Cainiao’s logistic network. Differences of outcomes across scenarios reveal that the aforementioned trade-off can be preempted by simultaneously making delivery-time ratings available and coordinating the prices and promised delivery times. Disclosure of ratings can reduce delivery times but is profitable only if the disclosure increases system-wide value. Confirming this finding, analyses of Cainiao’s data indicate that the availability of delivery-time ratings can reduce the average promised delivery time by 3.9 h (or 7.4%). Several extensions show that our results hold for different contexts, including both drop-shipping and retailer-managed fulfillment models. In conclusion, our results suggest when and how supply chains in online marketplaces can use pricing to make the offering of shorter delivery times a dominant strategy.

While online reviews are critical in e-commerce, the strategic behavior of reviewers is understudied, especially in business-to-business (B2B) markets. We investigate buyers' strategic behaviors when leaving reviews: whether B2B buyers withhold reviews from high-performing suppliers to temper their growth and maintain future bargaining power. We collect the entire review and transaction histories of 4,605 suppliers on Alibaba.com, the largest B2B global sourcing platform, covering 62,529 reviews and 455,593 transactions from February to November in 2017 and 2018. To identify causal effects, we employ a generalized difference-in-differences approach exploiting the 2018 US-China trade war as a natural experiment. This event created a sudden and exogenous shock to supplier transaction volumes, allowing us to measure the response of non-US buyers to changes in supplier performance. We find strong evidence of strategic review withholding. A one-unit increase in a supplier's transaction volume significantly reduces a buyer's probability of leaving a review by 0.9 percentage points. We also find no effect on the numerical ratings and sentiment of reviews that are posted. This indicates that buyers respond to a supplier's success by withholding reviews, but they do not harm the seller's reputation by leaving negative reviews. This core mechanism was directly confirmed by a comprehensive survey we conducted with B2B professionals. Our findings demonstrate that B2B buyers act as strategic agents who manipulate information for competitive advantage. This presents a crucial insight for platform governance: information transparency is a double-edged sword. While intended to reduce information asymmetry, publicly disclosing performance metrics like transaction histories can enable strategic behavior that distorts the reputation system. Platforms must therefore balance the benefits of transparency against the potential for such strategic manipulation when designing their information disclosure policies.

Billions of dollars are exchanged between companies through accounts payable in today's business landscape. Given its immense scale and critical role in business operations, effectively managing accounts payable and working capital is essential for organizations. In this study, we examine the financial supply chain problem, where a company seeks to minimize total payments toward accounts payable received from its upstream partners (e.g., suppliers) while leveraging cash inflows from downstream partners (e.g., distributors, wholesalers, retailers, and customers). This is accomplished by optimizing payment decisions based on payment terms and capitalizing on interest gains from cash on hand over time. Unlike prior studies, we investigate this problem in a more realistic setting where information about incoming invoices and cash inflows is uncertain. We formulate the problem as a stochastic dynamic program and identify the structural properties of an optimal policy. The optimal policy reveals payment priorities among invoices and establishes thresholds for maintaining cash on hand. We further find that payment priorities can be deterministic or stochastic, depending on the problem's state and random parameters. Additionally, we identify all instances where payment priorities are deterministic. Our study provides valuable managerial insights and practical implications derived from the structural properties of the optimal policy. Notably, some of these insights challenge well-known heuristics and seemingly intuitive practices. Lastly, we develop a simple heuristic based on the identified structural properties and demonstrate that it outperforms other widely used methods for solving large-scale practical problems.

This Part II paper analyzes the industrial transformation and policy governance of Operations and Supply Chain Management (OSCM) in China. Chinese supply chains are changing fast. The system is shifting from labor-based cost advantages to technology-driven value. Key markers include high-density automation, industrial artificial intelligence, and green operations. Concurrently, national policies serve as critical institutional drivers. This study highlights a distinctive state-led governance innovation known as the "Chain Chief System" (SCLS). The SCLS mechanism combines government administrative coordination with lead enterprise leadership. We utilize an innovative Institutional-Dynamic-Organizational (IDO) model developed in Part I paper to evaluate this triadic ecosystem. Finally, we provide a balanced assessment of operational performance trade-offs under embedded state governance. Our findings reveal how institutional buffering reduces systemic risk costs while introducing compliance burdens and affecting market agility. This study offers key insights for global managers navigating supply chain resilience and uncertain industrial environments.

This paper analyzes the academic evolution and educational progress of Operations and Supply Chain Management (OSCM) in China. The field has transformed over recent decades. It has transitioned from a knowledge-importing phase to indigenous innovation. We show that China has built a massive educational infrastructure and research system. This study proposes an innovative Institutional-Dynamic-Organizational (IDO) model to systematically evaluate this trajectory. Historical bibliometric data analysis from top-tier journals reveals a sharp shift in research momentum. Publications by China-based scholars in frontier domains grew dramatically. This academic evolution analytics offers profound contributions and actionable insights for Western economies and global supply chain systems.

Recent innovations have driven a steady increase in online marketplace transactions. To remain competitive, numerous marketplace platforms and independent data providers offer Competitive Intelligence Services (CIS), enabling sellers to explore not only their market potential but also that of their competitors. In this article, we employ a two-period game-theoretic approach to competitive learning to analyze the impact of CIS on participants with varying market shares in an online marketplace. In the presence of noisy demand signals, the online platform benefits from offering CIS to both sellers. This is because high demand noise makes demand exploration difficult for each seller in the first period. Consequently, price competition under poor knowledge of the price-demand relationship in the second period leads to a lower payoff for each seller as well as the platform. However, as demand uncertainty decreases, the platform prefers to induce CIS subscription exclusively for the seller with the larger market share. This scenario leads to signal-jamming behavior between the sellers, which results in a win-win-win situation for both sellers and the platform provider. Finally, we consider various model extensions and discuss the managerial implications for the design and regulation of competitive intelligence services in online marketplaces.

This study examines contests within a multi-tier organization, where the owner offers a prize for subordinate branches to compete, and branch managers subsequently set rewards to motivate their agents. Our main goal is to explore how the owner can steer managerial decisions to better align with her overarching objectives—maximizing agent effort—without undermining managerial autonomy. Drawing on behavioral economics, we propose that the owner can strategically disclose contest-related information to agents to trigger their fairness concerns, prompting managers to voluntarily offer higher rewards and thereby motivating greater effort. Our experimental results largely support this behavioral prediction. Compared to the baseline where agents observe only their own reward, revealing one additional piece of contest information, opponent's reward or managers’ prize, prompts managers to offer larger rewards. However, disclosing both pieces of information does not lead to a further increase in rewards. For agents, receiving a reward higher than their opponent's boosts effort, whereas a perceived unfair reward relative to managers’ prize leads to a significant effort decline. Moreover, when agents know both the prize and the opponent's reward, the vertical fairness concern (tied to the prize) exerts a stronger influence on effort than the horizontal fair concern (based on the opponent's reward).