Knowledge that Transforms

We consider a two-echelon decentralized supply chain where the probability of selling nondefective products depends on the supplier’s quality improvement and the buyer’s inspection efforts. Products assembled from defective components cause a recall that leads to external failure costs for the buyer. Both players’ efforts are not contractible and it is a challenge to align inspection and quality-improvement efforts in a decentralized supply chain. We find that a procurement contract can provide preventive incentives that induce high inspection efforts and enhancing incentives that enable the supplier to credibly commit to high-quality components. However, these contractual incentives only align two players’ efforts under certain conditions. Interestingly, our findings suggest that introducing a product recall (PR) insurance requirement into a standard procurement contract provided by the buyer can complement enhancing incentives and lead to quality improvement in components. More importantly, as PR insurance can improve the flexibility of risk-sharing arrangements between the supplier and the buyer, we show that it can also serve as a coordinating role in improving the performance of a decentralized supply chain.

The proliferation of mobile commerce channels has fundamentally reshaped retail ecosystems, particularly in digital markets like China where smartphone adoption approaches saturation among internet users. While extant literature has extensively examined the impact of new channel introductions (e.g., online, offline, mobile) on firm performance, less attention has been paid to consumer behavioral nuances within established digital interfaces. Addressing this gap, our study pioneers a comparative analysis of purchasing dynamics across two dominant yet technologically distinct channels: native apps versus light-app channels (e.g., WeChat mini-programs). While both channels share core mobile attributes (e.g., small screen sizes, on-the-go accessibility), their divergent technological architectures (Swift, Kotlin, and Java vs. HTML, CSS, WXML, and WXSS) create systematically differentiated consumer experiences. Through econometric analysis of 185,437 transaction records from a multichannel B2C platform, we reveal that consumers tend to spend more, purchase more items, and exhibit a lower likelihood of product returns when shopping through the light-app channel compared to native apps. More importantly, these behavioral divergences are moderated by product categories, price levels, and discount depths. Our findings contribute to the multichannel retailing literature by providing new insights into consumers’ behavioral differences between the two popular, yet distinct, mobile channels. Based on these insights, we suggest that multichannel retailers should prioritize channel convenience and accessibility and reconsider their investments in mobile native apps. Additionally, retailers should tailor assortments, pricing, and discount strategies to each channel to effectively engage consumers and stimulate purchases. Our research also emphasizes the importance of aligning marketing, operations, and finance strategies in multichannel retailing.

We study multi-period risk-averse inventory control in a data-driven setting. In this problem, a risk-averse retailer makes periodic decisions on inventory levels based only on historical demand observations without full knowledge of the demand distribution. We adopt the popular nested formulation for risk-averse programs to formulate this multi-period problem and its data-driven counterpart under a coherent risk measure. Our objective is to study the sample complexity bound such that with high probability, the data-driven policy is near-optimal, that is, the relative error of risk under the data-driven policy compared with the optimal risk is arbitrarily small. Analysis of this problem is inherently challenging, because the multi-period nature requires solving the risk-averse program and its data-driven version recursively backward in time, while the (empirical) risk-to-go functions in this process do not have closed-form derivatives for most risk measures, which renders existing first-order methods for the risk-neutral newsvendor model invalid. In this study, we develop a zeroth-order framework to establish the complexity bound on sample sizes to guarantee near-optimality of the data-driven policy with given accuracy levels. Instead of using first-order derivative information on the risk-to-go function, our analysis directly examines the class of functions that underpins each cumulative risk function and derives maximum inequalities for this functional class by computing the covering numbers. Finite-sample complexity bounds are then used to establish asymptotic properties of the estimated risk, including consistency and convergence rate. Computationally, the time complexity for solving the data-driven policy, which is essentially an empirical dynamic programming (EDP) estimator of the optimal policy, increases exponentially in the length of the planning horizon. To speed up computation, we propose an approximation scheme that recursively approximates the empirical cumulative risk function with a convex piecewise linear function and then minimize it to obtain a modified data-driven inventory policy. We show that with proper control for approximation error, the modified data-driven policy is also near-optimal, and it has the same order of sample complexity bound as that for the original EDP policy.

Industry 4.0 technologies have been regarded as powerful means to enhance a firm's competitiveness in the Internet of Things environment. However, implementing Industry 4.0 technologies calls for considerable capital expenditure and might interrupt normal production in the short term. This study conducts an empirical analysis of the impact of investing in Industry 4.0 technologies based on a sample of 563 investment announcements of publicly listed firms on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2013 to 2018. Using the event study method, we find empirical evidence that these investment announcements lead to positive stock market reactions and improved financial performance. In particular, we empirically evaluate firms’ short‐ and long‐term stock prices and financial measures by considering the type of investments (i.e., digital or physical investments), whether the investment is product‐oriented or manufacturing process‐oriented, and whether the technologies are applied within a firm or across the supply chain. Our empirical findings hold true when a firm's strategic decision is accounted for and remain robust through various tests. Furthermore, we propose a decision framework for firms to balance the tradeoff between short‐term disruption and long‐term benefits resulting from an investment in Industry 4.0. Specifically, we develop a two‐period model and investigate when and to what extent a firm should invest in Industry 4.0 technologies. Empirical and modeling analyses provide managerial insights for firms that grapple with the net benefit of investment in Industry 4.0.

This research investigates the operational and competitive effects of Price Transparency Regulation (PTR) in healthcare through both theoretical and empirical lenses. We leverage the launch of Maine’s price transparency website as a natural experiment and construct a Difference-in-Differences model to identify causal effects. Our analysis links patient flow patterns, negotiation dynamics, and revenue management strategies, offering a unique opportunity to unpack the multifaceted impacts of PTR. We quantify three mechanisms. First, PTR reshapes patient flow: With access to price information, patients become more price sensitive and switch to lower-cost providers, explaining nearly half of the observed 2.74–percentage point reduction in average prices. Second, PTR reduces the negotiated claim prices by intensifying market competition (the competition effect) and through its interaction with noncompetitive factors such as provider bargaining power. Competitive factors, particularly insurer size and provider appeal, explain 80.7% of these supply-side reductions, while noncompetitive factors account for the remaining. Third, the benefits are unevenly distributed: Larger insurers capture greater reductions, while providers with strong patient appeal or broad service portfolios preserve pricing leverage. Together, these findings show that PTR reduces costs through both consumer switching and negotiation dynamics but also amplifies existing market imbalances, privileging larger stakeholders.

Crowdshipping, a rapidly growing approach in Last-Mile Delivery (LMD), relies on independent crowdworkers to fulfill delivery orders. Building a sustainable network of crowdshippers is crucial for the long-term success of such systems, as participation is primarily driven by fair compensation. This is especially important for workers who rely on crowdwork as their main source of income, making equitable pay not just a matter of fairness but of financial well-being. In this study, we address several key questions that gig-economy platforms concerned with fair pay may ask: How can equity be measured? What are the associated cost implications? And how can potential drawbacks be managed? Our main contribution is the development of a practical, equity-oriented framework tailored to crowdshipping within an LMD environment. Inspired by the real-world operations of several crowdshipping platforms, the framework operates in real time and is built around a bi-objective optimization model that balances equity and cost. This allows us to systematically explore trade-offs and identify the equity measures that most effectively capture this balance. We demonstrate that even a modest reduction in cost efficiency (e.g., 2.5%) can lead to substantial improvements in equity; potentially up to 65%. Our results provide actionable insights for practitioners, including guidance on selecting appropriate equity measures. We also find that the best equity outcomes occur when the crowdshipper pool is kept relatively small. Furthermore, we quantify the performance loss of high- and low-performing crowdshippers as the pool size increases, offering valuable insights for workforce planning and management. Along similar lines, we demonstrate that our framework remains effective in managing vehicle shortages in dynamic environments while achieving comparable levels of equity improvement.

We study how one store’s performance affects others within a large branded network. Stores influence each other both negatively, through cannibalization, and positively, via agglomeration as well as knowledge and reputation spillovers. This study causally quantifies these peer effects at the dyadic level. To address the complexity of estimating peer effects across a large number of stores, we develop a semi-parametric model that balances parsimony with the flexibility to capture heterogeneity between store pairs. To address endogeneity, we use instrumental variables based on local weather, nearby large events, and staffing policies. Using data from 792 stores of a national fast-food chain clustered around Chicago, we find a non-linear effect of distance on peer effects. On average within 2 miles, cannibalization dominates, reducing nearby store sales by $0.154 per $1 increase at a focal store. Between 2–3 miles, competition and positive spillovers offset each other, yielding no significant net effect. From 3–7 miles, positive spillovers dominate, boosting nearby sales by $0.050 per $1, but beyond 7 miles, peer effects disappear. We also find that stores under the same ownership experience stronger positive peer effects, and urban stores benefit more from spillovers due to higher population density. Moreover, stores located near younger stores experience reduced cannibalization. Finally, we calculate each store’s network contribution—the total change in network sales per $1 increase at a focal store — which ranges from –$2.763 to $9.540, with a mean of $2.691. Only 3.8% of stores exhibit negative network contributions, primarily in dense urban centers. Incorporating factors such as shared ownership, store experience, and market size further reduces these negative contributions. These findings provide actionable guidance for optimizing store network performance by amplifying positive spillovers and mitigating cannibalization.

Prior research on supply chain geographical complexity has predominantly treated geographical distance and geographical dispersion as interchangeable concepts. Further, nearly all empirical studies examining supply chain antecedents of product quality risks used samples from Western, educated, industrialized, rich, and democratic (WEIRD) countries. To improve the theoretical and practical insights from this stream of research, we theorize that, particularly for manufacturers in emerging markets, supply base geographical distance and dispersion affect product quality risks through monitoring and coordination challenges, respectively. Analyzing data from the Chinese automobile industry, we find that the geographical distance of firms from their supply base is not significantly related to automobile quality risks, but geographical dispersion is significantly and positively associated. In line with our arguments, we found that the relationship between geographical distance and quality risk is weakened when firms source from suppliers located in countries with strong regulatory quality, and that the positive relationship between geographical dispersion and quality risks is weakened by supplier quality certifications. Taken together, our findings provide a nuanced understanding of the relationship between supply chain geographical complexity and product quality risks, which leads to new theoretical and practical insights relevant for non-WEIRD countries.

Seru production systems have demonstrated excellent rapid response capabilities in both stable and uncertain environments. This study reveals that when compared to the Toyota Production System, the seru production method improves rapid response capabilities by 20% and 50% in stable and uncertain environments, respectively. The underlying reasons for this improvement were unclear, so this became a focus of this paper. Static and dynamic Just-In-Time Organization Systems are used to investigate both the flexibility and efficiency of seru production systems under stable and uncertain conditions. Findings show that the rapid response capability of a seru system is driven by the substitution effect of parallel serus . Efficiency is relatively easy to achieve in stable environments but is more challenging in unstable conditions. Therefore, this study explored methods to achieve high efficiency in seru systems under uncertain environments. A stochastic gradient algorithm and a dynamic allocation algorithm are proposed. Experimental results demonstrate that the proposed methods outperform traditional newsvendor models and can achieve near-optimal performance.

The literature has highlighted the value of the solution business model for manufacturing firms. In this research, we investigate the dark side of providing customer solutions and challenge an assumption held by extant research: That shifting from selling individual products to providing customer solutions enhances buyer–supplier relationships. Drawing on the frame problem view and expectation-(dis) confirmation theory, we examine the impact of provision of customer solutions on customer default. We also explore contingent factors that either magnify or mitigate this effect. Using a longitudinal dataset of 2,218 observations from 446 Chinese listed firms between 2012 and 2020, we find that provision of customer solutions increases the likelihood of customer default. Moreover, we show that this positive impact is stronger in industries with higher turbulence and for firms with greater advertising intensity, but weaker for firms with stronger operational capability. These findings suggest that provision of customer solutions can damage buyer–supplier relationships by reducing the likelihood of meeting customer expectations and increasing transaction disputes.