Knowledge that Transforms

This paper investigates how dual-purpose firms—organizations that pursue both profit and social objectives—strategically design their product lines in markets where traditional profit-maximizing firms may imitate their behavior. We find that consumer heterogeneity plays a non-monotonic effect on the ability of dual-purpose firms to differentiate themselves from profit maximizers, which, in turn, influences firms’ product offering strategies and consumer welfare. When consumer heterogeneity is moderate, dual-purpose firms can credibly signal their social commitment without incurring additional signaling costs, leading to costless separation and efficient product differentiation. However, when consumer heterogeneity is either sufficiently low or sufficiently high, dual-purpose firms may exert additional efforts to provide consumers with their preferred (efficient) qualities or come closely approaching them, effectively distinguishing themselves from profit maximizers. This results in a positive surplus even for low-valuation consumers, which further increases as consumer heterogeneity becomes even lower or higher. The driving force is the stronger incentive for profit maximizers to mimic dual-purpose firms. Yet, these signaling efforts come at a cost, diminishing the firm’s overall utility and making differentiation harder to sustain in highly prosocial markets. Our findings also highlight that the societal value of publicly disclosing a firm’s social orientation is context-dependent. In some cases, mandated transparency may actually diminish consumer welfare relative to market-based self-signaling mechanisms. These insights offer strategic guidance for dual-purpose firms and inform policy decisions on when to support disclosure versus letting market forces facilitate differentiation.

In the presence of supplier quality risk, offering suppliers a higher procurement price or increasing inspection effort are two approaches to motivate a supplier to invest in higher quality. In this paper, we consider three sourcing and inspection strategies for a brand with two branch shops facing supplier quality risk: joint, mixed, and independent strategies. Under the joint strategy, the brand jointly sources for its two shops by offering a uniform procurement price and inspects both shops in a centralized way (joint sourcing and joint inspection). Under the mixed strategy, the brand jointly sources for its two shops by offering a procurement price, and then each shop decides its own inspection probability (joint sourcing and independent inspection). Under the independent strategy, each shop decides its own procurement price and inspection probability (independent sourcing and independent inspection). We find that among the three strategies, the joint strategy mostly relies on the inspection tool to motivate suppliers’ high-quality effort, and the mixed strategy mainly utilizes the procurement price tool by offering the highest price for suppliers’ high quality. We further find that, no matter whether the brand is purely profit-driven or cares about the quantity of low-quality products sold to the market, the mixed strategy is always preferred to the independent strategy. The preference between mixed and joint strategies is jointly determined by the fixed cost incurred in joint sourcing and inspection accuracy. For intermediate fixed cost, if the inspection accuracy is sufficiently high, the mixed strategy is preferred to the joint strategy; otherwise, the joint strategy is preferred to the mixed strategy. Interestingly, when a brand becomes more socially responsible (i.e., it cares more about the quantity of low-quality products sold to the market), it may switch from a reliable supplier to a risky supplier, leading to more low-quality products being sold to the market. This is because with a greater level of social responsibility, the brand’s higher inspection efforts become more credible, which motivates the risky supplier’s higher quality effort.

Multi-echelon stochastic inventory models with known demand distributions have long underpinned supply chain coordination, yielding first-best policies in centralized systems and contract mechanisms that induce decentralized agents to implement these policies. We revisit the classic two-echelon inventory model in an online learning setting with unknown demand, which necessitates rethinking both inventory control and coordination strategies. This setting poses three key challenges: (i) the overall loss function may be non-convex, limiting the applicability of standard online convex optimization methods; (ii) the multi-echelon structure creates information asymmetry, as the upstream agent observes only order quantities—potentially distorted by downstream learning—rather than true consumer demand; and (iii) realized inventory levels may exceed desired targets, further complicating learning dynamics. To address these challenges, we develop algorithms that combine online optimization with low-switching mechanisms and augmented loss functions, enabling effective learning despite these complexities. In the centralized setting, our algorithm converges to the first-best policy with low regret. In the decentralized setting, we design an adaptive coordination mechanism that yields favorable individual regret guarantees while learning the optimal contract, thereby incentivizing agents to implement the first-best policy and minimizing overall system regret. Numerical experiments demonstrate that our approach consistently outperforms standard benchmarks such as explore-then-exploit and vanilla online gradient descent, highlighting its robustness and practical relevance for supply chain coordination under demand uncertainty.

Dockless bike sharing has been widely recognized for its potential environmental benefits by bridging the last-mile gap and encouraging a shift toward greener modes of travel. However, these benefits depend on large-scale bike adoption rather than mere service presence. Unfortunately, this adoption is often undermined by limited bike supply or unfavorable operating contexts. Leveraging the staggered rollout of Ofo and Mobike across Chinese cities, we employ a quasi-experimental stacked difference-in-differences (DID) design to identify how air quality changes following the entry of dockless bike sharing. The twin-platform setting enables two complementary analyses. First, treating the two platforms as a unified provider, we find that bike introduction significantly improves urban air quality by 3.82%, corresponding to a 3.13-point reduction in the Air Quality Index (AQI). These results remain robust across extensive alternative specifications and validation checks. Second, distinguishing between the two platforms reveals a supply-side mechanism. Dual-platform entry reduces AQI by 6.47 points, which is more than twice the baseline estimate. In contrast, single-platform entry has no significant impact. We also examine the contextual determinants that may influence bike-riding demand. Heterogeneity analyses indicate larger air quality improvements in cities with higher domestic migrants, larger well-educated populations, denser public transit networks, and stronger bike-lane infrastructure, but smaller improvements where terrain is steep or rainfall is frequent. Overall, our study moves beyond average effects by showing how platform market structure and local demand conditions shape the realized environmental benefits of dockless bike sharing.

In the competitive landscape of modern business, tacit knowledge (TK) sharing has emerged as a critical enabler of innovation and transformation, particularly within the realm of operations management. In this context, the efficient flow of TK is essential for optimizing processes, promoting technology development, and sustaining competitive advantage. While the role of workplace friendship in facilitating TK sharing has been well-documented, there is a common misconception that close personal relationships alone suffice for effective knowledge transfer. However, in the complex dynamics of operational environments, these relationships can be unexpectedly fragile and prone to disruption. Even employees with strong workplace friendships may encounter inevitable challenges such as competition over limited resources, conflicts of interest, and diminished incremental benefits of knowledge exchange among functionally overlapping colleagues. Grounded in social information processing (SIP) theory, this study proposes a moderated mediation model and explores the negative moderating effects of competitive climate and functional similarity on the relationship between workplace friendship and TK sharing and how these factors subsequently impact innovation performance. Through a mixed-method design, including a main field study and a supplementary experiment involving 1,809 participants across various industries, our findings underscore that the positive association between workplace friendship and TK sharing will be diminished or even dissolved when employees perceive higher levels of competitive climate and functional similarity. By adopting a SIP perspective, this research elucidates how contextual factors in operational environments can overshadow the influence of workplace friendship on TK sharing. It also reveals the intricate interplay (i.e., mutual reinforcement) between the attitudes of TK holders and recipients, which is crucial for ensuring the seamless exchange of knowledge that drives operational excellence in highly competitive workplaces. These insights are vital for operations managers aiming to cultivate workforce diversity, regulate peer competition, inspire employee enthusiasm and engagement, and initiate cross-functional technology development projects. Such strategies foster effective and sustainable TK sharing, ultimately contributing to superior innovation performance in operational settings.

This study assesses concentration of the top 50 universities (T50 concentration) in premier business journals and its correlation with journal impact metrics. We use 2008–2022 data from the Web of Science and Clarivate Analytics, focusing on 46 business journals from the University of Texas at Dallas (UTD 24) and Financial Times (FT 50) lists to document several findings. First, we find that the top 50 institutions, representing less than 0.5% of business schools globally, contribute a disproportionately large share of about 53% publications in these outlets. Second, our longitudinal analyses indicate that T50 concentration has remained relatively stable over the 15-year period. Finally, we find evidence of a positive and statistically significant association between T50 concentration and Article Influence Score, although we do not find any statistically significant relationship with other journal impact metrics. Importantly, we find a decline in the publication and citation share of articles authored exclusively by top-50 author teams, and a rise in share for mixed author teams from top-50 and non-top-50 universities. These findings provide data-driven insights to inform debates relating to merit and elitism in editorial and review processes in top journals.

The indirect productivity gains related to information technology (IT), known as IT externalities, in inter-firm contexts have been extensively studied. However, the impact of IT investments within a business unit (BU) of a multibusiness firm on the productivity of other BUs remains unclear. Additionally, the conditions that facilitate such intra-firm externalities are not well understood. Research on resource externalities within multibusiness firms typically focuses on capacity-sharing benefits, where unused capacity in one unit can be utilized by another. IT resources, however, often lack capacity-sharing potential due to their full utilization or contractual limitations. Despite this, IT resources can generate non-rivalrous intangibles, such as internally developed applications, expertise, and consulting know-how, which can be shared within the firm to create externalities. This study investigates whether IT centralization (ITC), as a vertical coordination mechanism, is effective in harnessing IT externality potential arising from IT portfolio similarities (ITPSs), a form of horizontal coordination, across BUs. Utilizing data from 8,374 unique units within 866 firms from 2005 to 2020, we find that BUs must meet two conditions—higher ITPS and higher levels of ITC—to realize greater intra-firm IT externality benefits. Furthermore, these benefits accrue from IT investments made by units with a sufficient number of IT employees. Interestingly, BUs with limited access to IT employees gain more from pooled IT investments. Our findings suggest that concurrent vertical and horizontal coordination, along with access to human talent for creating knowledge, code, and expertise from digital resources, are crucial for maximizing digital resource externalities.

Organizations lose billions of dollars due to inadequate customer service. To improve service, and enhance customer satisfaction, frontline employees’ (FLEs) use of relational communication may be key. During online customer service chats, FLEs provide key information and offer solutions, but they also can build customer relationships through conversations. In this article, we establish how relational perceptions get evoked in conversations and what influences they have for the outcomes of customer service interactions. Accordingly, we present an empirical field study that illustrates how FLEs influence customer satisfaction by mirroring or complementing four key themes, in line with relational communication theory: intimate communication, task orientation, assertiveness, and composure. Our results indicate that FLEs should mimic customers’ use of intimate communication and task orientation, complement their assertiveness, and exhibit high levels of composure. Moreover, FLEs should emphasize their task orientation at the conversation's outset, gradually incorporate more intimate communication as it progresses, and adopt assertiveness late in the service chat. These insights, corroborated by four experimental studies, underscore the significance of FLEs’ relational communication. Our findings highlight the value of training FLEs to tailor their word choices adeptly, and leverage the potential benefits of text-monitoring tools, which can help FLEs increase relational perceptions and satisfaction among their customers.

We study simulation-assisted service system design, where stochastic simulation is used to select the best design from a finite set of structural or parametric alternatives. Since high-fidelity simulation can be prohibitively time-consuming, we adopt a multifidelity approach that combines expensive high-fidelity runs with cheaper, coarser low-fidelity runs to estimate system performance and compare designs. This research is motivated by the design of an integrated electric vehicle (EV) fast-charging station. We formulate the design problem under the fixed-budget ranking and selection framework, in which the simulation budget is allocated across fidelity levels and design alternatives to maximize the probability of correct selection of the best design. We derive an asymptotic solution, develop a selection algorithm that satisfies the resulting optimality conditions, and establish its consistency and asymptotic optimality. We further demonstrate the algorithm’s empirical performance through an EV fast-charging station case study and a set of synthetic examples. These theoretical and empirical results provide actionable guidance on when and how multifidelity simulation can improve best-design selection in complex service system design problems.

Popularity bias reflects the positive impact of popularity information on consumer choices. There are two common display formats for popularity information on online retail platforms: the total-based cumulative sales format, where the total sales of a product are displayed since its launch, and the period-based cumulative sales format, where only sales within a specific recent period are shown. Both types of popularity information are continuously updated. This paper focuses on the multiproduct dynamic pricing problem with popularity bias. We employ the widely used multinomial logit model to investigate the impact of popularity bias on consumer choices. In particular, we examine how popularity bias affects marginal revenue, pricing decisions, and market shares. Moreover, we highlight that ignoring popularity bias can lead to a suboptimal outcome. As the multiproduct dynamic pricing problem suffers from the curse of dimensionality, we propose a semi-myopic pricing policy, which is computationally tractable, and demonstrate its asymptotic optimality under both formats. Our numerical simulations further indicate that ignoring popularity bias can result in substantial revenue losses, while the semi-myopic pricing policy consistently outperforms other heuristics under both formats. Finally, empirical tests on real data provide a comprehensive procedure for identifying the most appropriate choice models, which offer practical insights for implementation.