Purpose The platform economy has enabled extensive information sharing among manufacturers, retailers, and e-commerce platforms. While existing studies primarily examine bilateral exchanges of demand or supply data, the rise of platform-provided customer analytics allows manufacturers to better understand consumers’ reference quality and refine their product quality decisions. Therefore, this study aims to investigate whether such analytics truly enhance customer satisfaction without prompting excessive or misdirected investments in product quality. Design/methodology/approach This study investigates a manufacturer’s optimal choice of information sources for product quality decisions by integrating game-theoretical modeling with numerical analysis. The model captures a multi-echelon supply chain consisting of a manufacturer, a retailer, a platform and end consumers. Findings The analysis shows that the manufacturer’s strategic choice of information source is jointly determined by its perception of the consumer's reference quality, the platform’s commission rate, and the retailer’s information service fee. Moreover, the manufacturer exhibits a stronger incentive to acquire customer analytics when it opens a direct distribution channel – selling directly on the platform rather than exclusively through the retailer. When platform commission rates are relatively low, the manufacturer is more inclined to align with the platform by adopting direct sales and procuring analytics services from the platform. Originality/value First, this research extends the information-sharing literature by shifting the focus from demand information to reference-quality information. Second, it fills an important gap by examining scenarios in which both an e-commerce platform and an online retailer offer customer analytics services to the manufacturer. Finally, it provides insights into best practices for leveraging big data analytics-enabled customer analytics in product quality design, thereby enriching supply chain research under vertical information asymmetry.

Unmanned delivery is reshaping last-mile logistics and challenging traditional courier operations. This study employs a game-theoretical framework to examine two strategic delivery models: one offering conventional human delivery and self-pickup options, and another integrating unmanned delivery services. The analysis focuses on strategic decisions regarding the timing and conditions under which courier firms introduce unmanned delivery, as well as the resulting impact on consumer choices among available delivery modes. Findings indicate that unmanned delivery becomes profitable only when customer preferences are moderate—neither strongly favoring nor entirely indifferent to specific service types. This challenges the common assumption that expanding delivery options inherently enhances operational efficiency. Furthermore, the introduction of unmanned delivery, especially when supported by subsidies, may unintentionally suppress demand for traditional services due to internal competition among delivery modes. Although subsidies can stimulate demand for unmanned delivery, they do not necessarily improve overall profitability or ensure widespread adoption, as the crowding-out effect may offset potential gains. In certain contexts—such as rigid consumer preferences or limited public funding—subsidies aimed at promoting unmanned delivery may even reduce social welfare. Given the diminishing marginal returns of technology subsidies associated with larger incentives, small to moderate subsidies are more effective.

The growing commercialization of autonomous vehicles (AVs) is reshaping consumer service preferences and prompting ride-hailing platforms to redesign fleet structures that accommodate the coexistence of human-driven vehicles (HVs) and AVs. This study develops a queueing game framework that incorporates vehicle heterogeneity and consumer preference differences to systematically compare three fleet configuration strategies: the pure HV strategy (HVs only), the pure AV strategy (AVs only), and the hybrid strategy (both HVs and AVs). The analysis highlights how consumer mismatch losses, AV operating costs, and service rates jointly shape equilibrium outcomes. Results show that when consumer mismatch losses are moderate, the optimal fleet strategy transitions from pure AV to hybrid as AV operating costs increase — and may even revert unexpectedly to a pure HV configuration. From a multi stakeholder perspective, when mismatch losses are high and AV operating costs are relatively low, the hybrid strategy can generate win-win-win outcomes for consumers, drivers, and platforms. Moreover, increasing either the HV or AV service rate improves platform profitability and can attract more consumers, although an excessively high HV service rate may depress driver wages. Robustness checks — incorporating heterogeneous driver opportunity costs and fixed AV deployment costs—confirm that the central insights hold across broader settings. Overall, the findings offer practical guidance for platform managers and policymakers navigating the operational and strategic challenges of AV integration.

In the rapidly evolving generative artificial intelligence (GenAI) ecosystem, downstream application providers face persistent challenges in designing optimal pricing strategies. These challenges arise from dual cost pressures: application programming interface (API) costs imposed by upstream model platforms and psychological frictions experienced by end consumers. This study develops a game-theoretic model to analyze application providers' strategic choice between subscription-based and usage-based pricing under both monopolistic and duopolistic market structures. The analysis reveals that the optimal strategies are shaped by a nonlinear interplay among psychological costs, model API costs, and consumer valuation. Under low psychological-cost conditions, API costs exert an inverted U -shaped effect on competing providers' profits. By contrast, under high API-cost regimes, subscription-based and usage-based models exhibit asymmetric sensitivities to psychological costs: profits under usage-based pricing decline with rising psychological costs, whereas subscription-based profits stabilise. Fthermore, elevated API and psychological costs attenuate competitive intensity, driving duopolistic equilibria toward monopoly-like outcomes. These findings remain robust after incorporating a broad set of realistic market frictions. The results provide actionable insights for GenAI application providers balancing cost pass-through, user engagement, and competitive positioning in pricing decisions.

The growing integration of chatbots in e-commerce customer service presents opportunities and challenges for online retailers in shaping effective artificial intelligence (AI) strategies. This study evaluates human-only, AI-only, and human–AI collaboration strategies using an agent-based simulation model across varying levels of task complexity, service volume, and product margin. Results show that the AI-only strategy excels in low-volume, simple tasks due to its cost-effectiveness, while the human–AI collaboration strategy proves superior in managing high-volume or complex inquiries by scaling human involvement to meet demand. For high-margin products, this collaborative approach delivers the best service, whereas the AI-only strategy is optimal for low-margin items. Enhancing chatbots’ anthropomorphic qualities could further improve service performance, but only if technological advancements are sufficient. The findings provide actionable insights for optimizing AI deployment and fostering adaptive customer service.

As algorithm governance becomes increasingly crucial in gig economy ecosystems, understanding the behavioral impact of algorithmic transparency remains a key research gap. Using the stimulus-organism-response framework and uncertainty management theory, this study examines how platform algorithmic transparency affects gig workers’ in-role and extra-role service behaviors, with emotional labor acting as a mediator and work gamification as a moderator. Analyzing survey data from 325 ride-hailing drivers using partial least squares structural equation modeling (PLS-SEM), the findings reveal that greater algorithmic transparency enhances both in-role and extra-role behaviors. Emotional labor mediates this relationship: deep acting strengthens both behaviors, while surface acting primarily supports in-role behavior. Meanwhile, work gamification diminishes the positive effect of algorithmic transparency on extra-role service behavior. These insights clarify the mechanisms and boundary conditions of algorithmic transparency in gig work, offering practical guidance for designing platform algorithms that optimize worker performance and satisfaction.

Artificial intelligence (AI)-powered chatbots offer a cost-effective solution for customer service, but often fall short in delivering personalized or complex interactions. In response, many merchants invest in AI training and explore human-AI collaboration to leverage the strengths of both automation and human touch. However, this introduces a strategic tradeoff between service quality and operational efficiency. Using a game-theoretic framework, this study examines how merchants can optimally choose service strategies with AI involvement. Our analysis reveals a critical collaboration trap. Contrary to the prevailing belief that increased collaboration consistently enhances service, consumers' sensitivity to the identity of the service agent erodes profitability, and greater collaboration will worsen this effect by accelerating task delegation to AI, thereby amplifying negative consumer perceptions. Furthermore, the study shows that a moderate-cost collaboration trap emerges: human-AI collaboration underperforms compared to human-only service when labor costs are at intermediate levels. Collaboration yields benefits only when labor costs are either very low or prohibitively high. In competitive markets, merchants can gain a strategic advantage not by enhancing their own service quality, but by capitalizing on rivals' inefficient AI deployment. These findings challenge the assumption that more human-AI collaboration is always better and provide actionable insights for managing hybrid service strategies and optimizing AI investments.

Online merchants face a dilemma in deciding whether to disclose their use of AI-driven services, which offer costeffective, human-like interactions but may trigger customer resistance from those who prefer human engagement. The study employs a game-theoretic model to examine AI identity disclosure strategies, incorporating consumer preferences for algorithms and their ability to detect undisclosed AI usage. Findings indicate that nondisclosure is ineffective in markets with a high proportion of sophisticated consumers who can recognize AI involvement. However, in markets dominated by naive consumers, where AI identity is less detectable, disclosure remains the preferred strategy, particularly when AI service quality is perceived as low, to mitigate potential backlash. Comparing pure AI and human-AI collaboration service modes, merchants derive greater benefits from nondisclosure in collaboration settings. Furthermore, greater consumer aversion to AI does not necessarily push merchants toward nondisclosure, as its effectiveness depends on service quality rather than aversion alone. This study also highlights the dual role of AI anthropomorphism: it can increase consumer acceptance of nondisclosure while simultaneously making disclosure more appealing. Under nondisclosure, improvements in service quality generally enhance consumer surplus, but sophisticated consumers may gain less surplus than naive consumers. Lastly, mandating AI identity transparency does not always maximize social welfare, challenging the assumption that compulsory disclosure is universally beneficial. These findings offer valuable insights into human-AI collaboration and inform the design of algorithm transparency policies in AI-driven services.

Digital transformation (DT) is increasingly recognized as a critical enabler of organizational resilience (OR), yet the underlying mechanisms through which it operates remain underexplored. Drawing on the resource-based view (RBV), this study develops and tests a moderated mediation model to examine how DT enhances OR through strategic flexibility, and how this relationship is influenced by environmental dynamism. Using survey data from 289 Chinese enterprises undergoing digital transformation, the study employs hierarchical regression and partial least squares structural equation modeling (PLS-SEM) for empirical analysis. The results demonstrate that strategic flexibility partially mediates the DT-OR relationship, with the effect driven primarily by resource flexibility rather than coordination flexibility. Moreover, environmental dynamism negatively moderates the relationship between DT and strategic flexibility, suggesting that high environmental turbulence diminishes DT's positive impact on organizational adaptability. These findings extend the RBV by clarifying the resource-flexibility-resilience pathway and identifying key boundary conditions that constrain the outcomes of digital transformation.

The increasing use of artificial intelligence (AI) to enhance products and services has enabled personalized offerings and smarter functionalities through the analysis of consumer data. However, privacy concerns present significant challenges to the effective utilization and commercialization of AI-enabled products. To address these concerns, firms must carefully navigate consumer data privacy and develop appropriate data collection strategies to support future product intelligence, particularly with AI technologies like ChatGPT. This study examines two primary data collection approaches: the uniform policy strategy and the option menu strategy. A mathematical model is constructed to assess these strategies, considering factors such as information externalities and heterogeneous consumer privacy concerns. By comparing firm profits, consumer surplus, and social welfare under both strategies, the study finds that the option menu strategy becomes optimal when there are considerable differences in privacy concerns across consumer groups or when even smaller differences exist, but consumers place a high value on personalized services. These insights offer guidance to firms and policymakers in formulating appropriate data collection strategies for AI-enabled products.

During COVID-19, public health measures triggered shifts in consumer behavior, disrupting supply chains and challenging physical stores’ viability. To navigate this uncertainty, retailers must adjust their channel strategies. Considering consumer heterogeneity in both retailer and channel preferences, this study examines how two competing brick-and-mortar retailers integrate online channels amidst varying pandemic conditions. Square-Hotelling models indicate that perceived severity and isolation necessity outweigh coupon offerings and delivery charges in integration decisions. As severity rises, asymmetric adoption emerges first, followed by symmetric adoption, and then asymmetric adoption again. Enhanced health knowledge dissemination prolongs the transition process of channel integration equilibria. Retailers should align strategies with perceived pandemic severity and public health measures, ensuring preparedness for channel integration.

Redundancy and flexibility are important key drivers for achieving supply chain resilience. However, there is a lack of comparison between these two elements, and their effectiveness on supply chain resilience, as well as how their relationship affects it. To address these problems, this study synthesizes the literature on supply chain resilience, redundancy, and flexibility, and clarifies specific strategies for aligning redundancy and flexibility for supply chain resilience in practice. In particular, it identifies the dimensions of redundancy and flexibility, and delineates four supply chain statuses: vulnerable, robust, lean, and resilient. The understanding leads to a supply chain resilience management framework, which provides operational guidance for enterprises to evaluate supply chain vulnerability and improve operation status. Overall, the framework contributes to the literature with the roles that redundancy and flexibility play in achieving supply chain resilience in terms of their manifestations and interactions.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI