Knowledge that Transforms

Trust is among the most critical factors in buyer–supplier relationships. In an effort to understand the origins of trust, power has become the focus of a burgeoning body of literature. However, research on the association between power and trust has been plagued by inconsistencies in terms of whose power and trust are being examined, which has led to confusion and hindered cumulative progress. We address this issue by disentangling the effect of the focal organization ( actor effect ) from the effect specific to its partner ( partner effect ) and accounting for both simultaneously. We further theorize and show that the partner's level of self-promotion communication about his or her own achievements and credentials moderates both actor and partner effects, thus adding knowledge about a contingency that accounts for a considerable degree of variation in the linkage between power and trust. Using multi-informant, dyadic survey data paired with archival information scraped from firms’ webpages, we find that (i) actors low (vs. high) in power tend to place more trust (ii) while simultaneously eliciting higher levels of trust from their partners; however, (iii) these effects differ markedly depending on the partner's level of self-promotion communication. Our study offers a novel, integrative perspective on power and trust, and we elaborate on its important implications for understanding buyer–supplier relationships.

Platform firms are increasingly investing in value-added services (VAS), believing these investments in VAS universally contribute to their success. Although many studies have examined platform firms’ core services (e.g., intermediation), systematic analyses of VAS provision strategies remain limited. In this article, we develop a game-theoretic model to analyze (1) the conditions under which platform firms benefit from providing VAS and (2) to which side(s) platform firms should provide VAS. We offer several insights. First, variable service costs are a critical yet previously neglected factor in governing platform firms’ VAS provision. Second, a comparison of equilibrium outcomes across three VAS provision strategies—one-sided unidirectional, two separate unidirectional, and bidirectional—reveals that the optimal strategy is jointly determined by variable service costs, cross-network externalities, and the asymmetry in the benefits of bidirectional VAS to the two platform sides. Specifically, when asymmetry is low, bidirectional VAS is optimal. When asymmetry is high, platform firms should provide unidirectional VAS to one side, considering the second side only when variable service costs are low or cross-network externalities are significant. Third, we explore the implications of VAS provision strategies for platform firms’ investment and pricing decisions. For example, when platform firms provide two separate unidirectional VAS, these investments are complementary. VAS could incentivize platform firms to offer price subsidies to one side of the platform. Finally, optimal VAS provision strategies do not always increase consumer surplus.

Modern decision-support applications build on planning parameters such as lead time, price, yield, etc., which are maintained as master data. The accuracy of master data significantly influences the viability of such applications. However, the maintenance of master data is considered a tedious and error-prone task. In this study, we explore the effectiveness of machine learning techniques to improve the accuracy of plan lead times. We apply both unsupervised and supervised learning methods for creating lead time prediction models. We test our approach using historical data of a global equipment manufacturer. In a numerical analysis the calculated plan lead times are over 30% more accurate than current plan lead times in terms of mean-squared-error (MSE). This increased accuracy of plan lead times reduces inventory investment by approximately 7%.

Reverse factoring (RF) is a highly relevant form of supply chain finance. Whereas extant analytical studies indicate how RF should enable buyers to extend payment terms, sufficient empirical evidence is lacking. To hypothesize on payment term extensions, we study three motives—financing cost, fairness, and standardization. Our empirical evidence indicates the presence of all three. It extends former analytical work on RF adoption revolving around the financing cost motive only.

It is well established that a manufacturer generally benefits from encroachment with a profitable direct channel and may also benefit from using encroachment as a threat (i.e., without sales in the direct channel); the retailer may also benefit from both encroachment strategies. Our study provides new insights into the manufacturer encroachment literature by considering the upstream manufacturer's production economies of scale. Contrary to conventional wisdom, we show that an increasing level of economies of scale may reduce the manufacturer's profit if the manufacturer encroaches. Furthermore, we find that under strong economies of scale, refraining from encroachment may be the optimal strategy, even if encroachment could increase the manufacturer's wholesale profit. This finding suggests that a manufacturer may choose not to encroach solely due to profit losses in direct selling. Interestingly, we also find that the manufacturer can benefit from encroachment by maintaining an unprofitable direct channel with sales, provided that the level of economies of scale is below a threshold. Moreover, our findings reveal that the retailer can benefit from manufacturer encroachment only when the level of economies of scale remains below this threshold, and that an increasing level of economies of scale reduces the likelihood that the retailer can benefit.

Managing the performance of intralogistics operations, that is logistics operations within facilities such as manufacturing plants, order fulfillment warehouses, ports and terminals, and retail stores, is critical in fulfilling customer expectations. Traditional decision-making for intralogistics operations is based on historical data, typically collected over long-range intervals with significant processing delays. However, nowadays, Internet of Things (IoT) applications are used to gather detailed real-time data to make dynamic decisions. These new data sources provide challenges and opportunities for operations management. We provide an overview of prominent IoT technologies in four domains: Manufacturing, warehousing, ports and terminals, retail, and other emerging areas. We discuss four prominent research questions (cutting across multiple application domains) that can be addressed using new data sources, along with the methodological approach and managerial insights that may result. In particular, IoT can improve the tracking and tracing of objects, equipment, and humans and provide rapid alerts, allowing managers to make real-time decisions and improve asset use, uptime, and profitability.

Livestream e-commerce has emerged as a novel way to promote and sell products. This channel differs from existing promotion channels like TV/online video advertising because viewers voluntarily consume the content on this channel and are highly engaged due to social interactivity with the livestreamer and other viewers. A key design aspect of product promotions is the duration for which a product is presented during a livestream session. In this article, we empirically study the impact of product presentation duration by analyzing a unique dataset from two of the largest livestream shopping platforms in China. We find that when the product duration is longer, product revenue is higher. However, as the average presentation time increases, the session revenue decreases. The role of presentation duration in driving sales may differ between official (single-brand) livestreams sponsored by brands and third-party (multi-brand) livestreams. On analyzing the heterogeneous effects between official and third-party livestreams, we find that the positive impact on product-level sales is largely driven by third-party livestreams. On the other hand, both official and third-party livestreams can improve session-level sales by reducing the average product duration in a session. Thus, in the context of third-party livestreams, we observe a tension between the incentives of the brands (advertisers) whose goal is to drive an individual product’s sales and third-party livestreamers whose goal is to maximize total sales in a session. Our findings have useful implications for the design of e-commerce livestreams.

Artificial intelligence (AI) is transforming healthcare operations. Nevertheless, particularly in the context of preventive care, little is known about how laypeople perceive and accept AI and change their behavior accordingly. Grounded in a solid theoretical framework of trust, this study bridges this gap by exploring individuals’ acceptance of AI‐based preventive health interventions and following health behavior change, which is critical for preventive care providers’ operational and business performance. Through a randomized field experiment with 15,000 users of a mobile health app complemented by a survey, we first show that the use and disclosure of AI in preventive health interventions improve their effectiveness. However, individuals are less likely to accept and achieve the health behavior change suggested by AI than when they receive similar interventions from health experts. We also observe that the effectiveness of AI‐based interventions can be improved by combining them with human expert opinions, increasing their algorithmic transparency, or emphasizing their genuine care and warmth. These results collectively suggest that, different from conventional technologies, AI's deficient affective trust, rather than comparable cognitive trust, play a decisive role in the acceptance of AI‐based preventive health interventions. This study sheds light on the literature on the role of new‐age information technologies in behavioral operations management, consumer marketing, and healthcare as well as the role of trust in technology acceptance. Valuable practical implications for more effective management of AI for preventive care operations and promotion of consumers’ health behavior are also provided.

The proliferation of fake news, more recently multimodal fake news, poses a significant threat to individuals, organizations, and society. While online social media platforms have employed automated methods to combat fake news, they face two notable challenges: the scarcity of labeled data and the diversity of news domains. To enhance the effectiveness and efficiency of online platforms in mitigating the spread of fake news, this study proposes MT-GPD (multimodal deep transfer learning with gating network, model patch, and domain classifier) for cross-domain fake news detection. MT-GPD integrates three novel design artifacts as auxiliary mechanisms for enhancing multimodal deep transfer learning, including a gating network that captures the relative importance of textual and visual components of individual news articles for dynamic fusion; a customized model patch that balances detection performance and computational efficiency; and a domain classifier that adapts multimodal representations to a target news domain. We evaluate the performance of MT-GPD using news datasets spanning four different domains. The results demonstrate the efficacy and robustness of MT-GPD, providing strong evidence for the impacts of the proposed auxiliary mechanisms on improving fake news detection performance.

How we manage operations—the domain of Operations Management (OM)—has important implications for the practice of diversity, equity, and inclusion (DEI) in organizations. Conversely, DEI goals have important implications for organizations’ OM practices. We outline the two-way links between DEI and OM to offer future research opportunities. In particular, we examine interactions between OM and DEI across four broad themes: (1) Workforce, (2) Supply Chains, (3) Health and Society, and (4) Technology, Platforms, and Innovation. We conclude with a discussion of DEI in OM as it relates to research and teaching. This article is a collaborative effort with the Senior Editors involved in the special issue of Production and Operations Management on “DEI in Operations and Supply Chain Management.”