Knowledge that Transforms

Specialized investment platforms significantly influence financial service operations by enhancing collective intelligence and uncovering trading opportunities. However, the large volume of data, particularly the prevalence of noise, hinders effective decision-making. Opinion leaders constitute a distinct and influential group of users on these platforms, and the question of whether their presence can help financial institutions make better decisions remains open. To address this key operational question, we focus on two indicators in investment platforms: investor disagreement and prediction accuracy. Our findings reveal that the presence of opinion leaders leads to reduced investor disagreement and increased accuracy in predicting future stock returns. This effect is more pronounced when opinion leaders participate earlier in a discussion or contribute posts that are more innovative, longer, or express vivid opinions. Our subsequent experiment further reveals the psychological and cognitive mechanisms behind these findings: the presence of opinion leaders enhances investors’ cognitive abilities by providing them with more information and boosts their confidence in the information they acquire. These results contribute to information cascade theory by demonstrating how varied communication patterns result in different qualities of crowd decision-making. Additionally, we contribute to the operations management literature by highlighting the crucial role of opinion leaders in enhancing both financial services and social media operations. We suggest that financial institutions develop trading strategies based on opinion leaders’ posts and that platforms tailor features to leverage opinion leaders’ participation.

Electronic health record (EHR) clinical notes constitute a core documentation subsystem through which physicians externalize clinical assessments and care plans in narrative form. Yet clinical documentation is time intensive and is frequently viewed as an administrative burden, leaving limited evidence on whether and how physician note usage improves inpatient operational efficiency and downstream quality outcomes. This study examines the impact of lead physician note activity on discharge efficiency and post discharge outcomes using granular EHR audit logs from a major U.S. teaching hospital. Leveraging more than 6.8 million audit log access events across 30 services, we model discharge as a time to event outcome at an hourly resolution and address endogeneity in documentation intensity using an instrumental variable strategy. We find that increased lead physician note activity predicts faster discharge. Specifically, a 1% increase in lead physician note activity measured 12 h earlier is associated with approximately a 0.075% decrease in expected remaining time to discharge, and the result is directionally consistent under alternative lag specifications. We further test an operational mechanism grounded in knowledge transfer and care team coordination. Higher lead physician note activity is followed by greater near-term nurse information access, operationalized as nurse non-note chart activity within a short response window after physician documentation, and this timely information access is associated with shorter time to discharge in the mediated survival specification. In an additional analysis, we model time to readmission within 30 days using a time to event framework and find that greater lead physician documentation during the index stay is associated with a longer expected time to readmission, providing complementary evidence that documentation practices are linked to downstream outcomes. Collectively, the results advance healthcare operations and information systems research by identifying physician note activity as an operationally consequential form of clinical work and by clarifying a coordination mechanism through timely care team information access in the EHR. This study contributes to healthcare operations management literature and practical implications include optimizing EHR note utilization, implementing tailored training programs, and investing in user-friendly EHR infrastructure to balance efficiency and quality of care improvements and time constraints.

Recently, leading manufacturers have started adopting information and communication technologies (ICTs) to assist operators in capturing assembly operational errors. These manufacturers choose to develop internal ICTs and decide whether to implement proactive experiential learning to continuously improve the rate of recognizing assembly operational errors, or acquire external ICTs from third-party technology providers that increase the rate of recognizing assembly operational errors but fail to achieve experiential learning. These two ICT adoption models may cause different effects on assembly operations, which subsequently affects manufacturers’ profitability. However, there is a limited body of relevant research that focuses on this topic. Hence, our study aims to bridge this gap by examining a manufacturer’s decisions for the ICT adoption, internal experiential learning, and subsequent pricing strategy. First, we reveal that it is sometimes beneficial for the manufacturer to forgo ICTs rather than adopt ICTs. This is because the lower recognition capability leads to a weaker scale effect, reducing the positive effect of ICTs on operational error losses. Next, we find that even if the internal learning effect is stronger, the manufacturer may still choose to acquire external ICTs. Moreover, as the operational error loss increases, the manufacturer or third-party technology provider may sometimes invest less in ICTs. Further, our results show that the ICT adoption benefits consumer surplus and social welfare, and external ICTs sometimes generate more social welfare but less consumer surplus than internal ICTs. Additionally, we find that the ICT adoption changes the relationship between the assembly operational accuracy and product demand. Following the ICT adoption, our study offers invaluable insights to managers on how ICTs can yield better assembly operational outcomes.

Many firms, such as American Airlines, Patagonia, Google, and Apple, have publicly stated their goal of becoming carbon neutral at some point in the future. These firms are pursuing multiple emissions reduction initiatives within their value chains, such as the use of renewable energy, zero-emissions vehicles, and low-carbon materials and supplies. But despite these efforts, some residual emissions cannot be further reduced, necessitating the purchase of carbon offsets, which increase firms’ costs. While environmentally conscious consumers may be willing to pay a higher price for a low-emissions product or service, a significant segment of climate change–disengaged consumers is not willing to do so, as demonstrated by multiple studies. In this article, we identify when it is optimal for a profit-maximizing firm to offer its consumers carbon-reduction offsets for purchase with the product, in addition to potentially purchasing offsets at the firm level. Through a stylized analytical model, we show that it is not optimal for firms to both buy offsets at the firm level and offer them to consumers for purchase at the same time. Rather, when the offset cost is low enough, the firm buys enough offsets to compensate for all of its emissions at the firm level, resulting in a higher overall product price; when the offset cost is in the middle range, the firm offers offsets to consumers for purchase at an offset price lower than offset cost, ensuring that green consumers buy offsets; and when the offset cost is high, offsets cannot be optimally used in any way. The thresholds depend on the green-segment size and disutility from emissions (at the consumer-consumption and firm levels), and on the product’s own carbon footprint. Providing offsets for purchase to consumers allows consumers to self-select into their preferred product/price bundle, thus providing the firm with a market-segmentation and price-discrimination mechanism that increases profits and reduces the firm’s carbon footprint.

Online knowledge-sharing platforms such as Quora and Zhihu (a leading knowledge-sharing platform in China) often use both peer voting and followers to encourage user knowledge contributions. However, these platforms diverge in whether they highlight followers, upvotes, or both as reputation symbols. To better understand the consequences of these platform design choices, we conduct a field experiment with 1,696 focal users on Zhihu where we exogenously increase upvotes or/and followers for treated users over a 53-day intervention period. We monitor focal users’ activities for 303 days, covering both pre- and post-intervention periods. We further use a large language model to gauge the quality of 12,998 answers contributed by these users during this time. We find that increasing upvotes significantly boosts users’ answer contributions (in volume, total length, and quality), while increasing followers has mostly no overall effect on contributions. Additionally, increasing upvotes can encourage contribution for up to 100 days, particularly among lower-reputation (i.e., with fewer upvotes or followers), less active, female users, and those answering soft topic questions. In contrast, increasing followers only reduces contribution volume (not length or quality) for higher-reputation, more active, and male users. Moreover, while peer voting primarily only affects answer contributions, the follower treatment leads to negative spillovers, reducing users’ upvoting on fellow users’ answers, following fellow users, or purchasing Zhihu Lives hosted by fellow users. Our research suggests the possibility that users on knowledge-sharing platforms may associate peer voting with contributions, treating it as peer recognition that offers intrinsic motivation. In contrast, users may interpret additional followers as a symbol of status enhancement, which could, in some cases, dampen their motivation to contribute. Overall, our study suggests that platforms aiming to foster active, high-quality contributions should highlight upvotes rather than followers as a reputation symbol. To our knowledge, this study is among the first field experiments to identify and compare the causal effects of peer voting and followers on user contribution to knowledge-sharing platforms.

Creating curated guides or lists (e.g., Top 100 Places to Eat) is a common operational strategy employed by platforms as part of their reputation systems to assist customers in decision-making. Prior research has largely highlighted the effects of such guides on customer word-of-mouth (WOM) volume and valence. Moving beyond these aggregate WOM metrics, we extend this stream to examine how being listed in a guide (BLG) influences customers’ likelihood of providing ratings and rating composition across valence types (compliments, complaints, and neutral ratings), drawing on the zone of tolerance framework. Using large-scale empirical data, we find that BLG increases customers’ likelihood of rating but reduces overall valence, with a higher proportion of complaints and neutral ratings and a lower proportion of compliments. The effect is particularly pronounced among merchants with additional high-quality signals (e.g., higher prices, greater popularity, and superior prior ratings). Evidence from both observational data and a randomized experiment further reveals the underlying mechanisms, showing that BLG increases customer involvement and raises desired expectations more dramatically than adequate ones. These triangulated findings contribute to the literature by providing in-depth insights into how and why BLG affects customer rating behaviors and thus, merchants’ WOM. Our findings underscore the importance of strategic operations management for platforms when designing and managing their reputation systems, with significant implications for technology management.

The pricing of limited capacity is a pivotal issue in many business contexts such as event ticket sales and online flash sales. Prior studies overlook consumer deliberation behavior in the presence of supply limits, leading to ineffective pricing decisions. This motivates investigation of consumer deliberation behavior under supply limits, and the capacity-constrained firm’s pricing and operational strategies. We first show that severe supply limits inhibit consumers’ costly deliberation, while moderate supply limits enhance consumer deliberation as the availability enhancement effect of deliberation reaches its highest potency. Accordingly, the firm with intermediate capacity tends to adopt a normal pricing strategy, that is, pricing as in the market of informed consumers. However, when the capacity is relatively low (sufficiently high), the firm can charge a relatively high (low) price to induce (deter) consumer deliberation, that is, transgressive (regressive) pricing; when the capacity is sufficiently low, the firm should adopt a prior value pricing strategy because consumers never deliberate. Second, on the supply side, it is not always necessary to fully utilize the limited capacity in supplying products, and for firms with high capacity, a low-quality strategy may be preferable even if quality improvement is costless. Finally, extensions to imperfect learning and heterogeneous deliberation costs show the robustness of our baseline model and offer new insights. Interestingly, the presence of demand uncertainty may benefit firms. Our study sheds light on the pricing of limited capacity and elucidates several strategic decisions when considering consumer deliberation behavior.

This study examines the composition of attributes among members of the Production and Operations Management Society (POMS). We analyze POMS membership data from 2017 to 2023 and conference registration data from 2011 to 2024. Specifically, the study seeks to: (1) describe the current composition of POMS members and conference registrants in terms of gender, academic rank, and country of affiliation, and (2) provide recommendations for improving representation within professional societies.

We deal with a retailer’s multi-product inventory system where customers randomly seek acceptable substitutes if their initial requests are not satisfied. Unsuccessful quests for substitutes would result in lost sales. Motivated by a consulting project as well as other real practices, we also choose to deal with further challenges posed by initially unknown base demand distributions and substitution probabilities; moreover, we aim to develop an offline learning method that (i) takes advantage of given data derived from bygone decisions rather than of any learning-while-doing opportunity, (ii) makes inferences about base-demand distributions and pairwise substitution probabilities without knowing whether there have been demand arrivals after inventory depletions, and (iii) is unhindered by the lack of knowledge about the assortments faced by customers and their purchase-or-no-purchase decisions at their individual arrivals. To address these challenges, we propose an innovative approach based on the Kaplan-Meier estimator that circumvents unrealistic data requirements. Our substitution probability estimates employ carefully designed weighting schemes to facilitate rigorous theoretical analysis through tools such as the Cauchy-Schwartz inequality. Both one-time substitution scenarios and more complex Markov-chain substitution patterns would be accommodated. Using large deviation tools, we establish provably optimal convergence rates of our estimates on top of consistency. The precision in parameter estimates would translate into accuracy in replenishment decisions. For inventory management, we take advantage of a submodularity property to obtain an exact algorithm for the two-product case and a good heuristic for the general multi-product problem. Computational studies based on simulated and actual data confirm the merits of our approach.

In the life sciences industry, pharmaceutical firms often collaborate with biotechnology firms to access technological expertise and R&D capabilities, enabling them to accelerate innovation and bring products to market more effectively. In this study, we examine the collaboration experiences of biotechnology startups and pharmaceutical firms in 287 biotech–pharma partnerships and analyze how firms’ experience impacts the likelihood of product commercialization. Experience is defined as the number of times each firm has participated in supply-based activities in biotech–pharma partnerships. We also conjecture that products developed through biotech–pharma partnerships are more likely to progress toward commercialization if both firms have similar levels of experience. However, when the partnering firms’ experience gap increases and they collaborate to develop the product jointly, we expect progress toward commercialization to become less likely. Our results show that both the biotechnology startup's experience and the pharmaceutical firm's experience increase the likelihood of progression toward commercialization. Our results also confirm that co-development moderates the relationship between experience gaps and the progression toward commercialization. Based on our findings, partnering experience and experience gaps are critical considerations as pharmaceutical firms engage in co-development projects with biotechnology startups. By focusing on partnering experience, our study contributes to the technology management literature and research on interfirm partnerships, specifically, in the biotechnology and pharmaceutical industries.