In the e-marketplace, consumers can know how products fit their preferences using artificial intelligence consumer experience tools (AI-CE), which have been adopted by the retailer and/or supplier. Motivated by e-commerce practices, the question of who provides AI-CE is optimal for the supply chain remains unclear. We evaluate the impacts of implementing AI-CE on the supply chain. The supplier sells two horizontally differentiated products. One is sold through the retailer, and one is sold directly on the supplier's own channel. Our analytical results show that the optimal AI-CE implementation strategy of the retailer and supplier depends on consumer sensitivity toward the privacy of using AI-CE. To realize a win-win outcome for the supplier and retailer, the retailer (supplier) should implement AI-CE, but the supplier (retailer) should not when consumer sensitivity toward privacy is low (high). Surprisingly, when consumer sensitivity toward privacy is moderate, neither the supplier nor the retailer should implement AI-CE. The reason is that joint implementation will eliminate the positive effect of comparative disadvantage and channel competition. Moreover, we find that the enhancement in the precision of AI-CE may either benefit or harm the supplier and retailer. Our study guides AI-CE implementation in e-commerce supply chains.

The boom of e-commerce has led to the rise of copycats but also encouraged more manufacturers to join these platforms for product distribution. Using a game-theoretic framework, we investigate whether a brand manufacturer faced with copycat competition and asymmetric demand information should enter the online channel, focusing on the impact of platform information sharing on brand manufacturer distribution mode choice in a multichannel retail environment. The main findings are as follows: First, when the platform does not share information, the brand manufacturer prefers the agency mode under the positive spillover effect if the commission rate is moderate. The platform’s sharing strategy increases the brand manufacturer’s preference for the agency mode. However, when the commission rate is sufficiently low or high, the distribution mode of the brand manufacturer is unaffected by the information-sharing strategy. Second, the platform may not always share information with the brand manufacturer in the agency mode, but rather depends on the competition intensity. Third, despite platform information sharing, the brand manufacturer may still choose the wholesale mode due to the positive spillover effect. This counterintuitive result occurs because the significant positive spillover effect mitigates the double marginalization effect of information sharing in the wholesale mode.

The use of generative AI has increased drastically in recent years, offering transformative potential in creativity, productivity, and decision-making across various domains. However, concerns are growing among practitioners and academics about its tendency to produce inaccurate or nonsensical outputs—a phenomenon known as generative AI hallucinations. While scholars have begun exploring this phenomenon, most of the existing research focuses on technical issues and solutions. This research takes a behavioral approach to examine the association between individual personality traits and tolerance of generative AI hallucinations. Drawing on Error Management Theory and the Big Five personality traits, we examine how individuals with different personality traits tolerate generative AI hallucinations. The study contributes to calls from management journals to investigate the challenges associated with generative AI use. Our findings provide important theoretical implications and offer actionable insights for engineering managers seeking to implement generative AI.

The construction and demolition waste management industry (CDWMI) plays a strategic role in energy conservation, emission reduction, and the development of a circular economy. Technological innovation, the cornerstone of efficient waste reduction, reuse, and recycling practices, has increasingly attracted global attention. The complexity and uncertainty of CDWMI innovation necessitate collaborative approaches. However, existing research primarily focuses on developing and applying specific technologies, overlooking macroscopic and quantitative measurements of collaborative innovation in the CDWMI. This research evaluates the overall state and dynamics of CDWMI collaborative innovation. First, we collected 1,050 cooperative patents and categorized the industry development into distinct stages using technology life cycle analysis. Next, we examined the network structure of collaborative innovation through social network analysis. Finally, we revealed network characteristics and evolutionary processes across different stages from geographical, technological, and organizational dimensions. The results indicate that geographical distance has no significant impact on partnership formation, the technological focus has shifted from recycled building materials to crushing technology, and core enterprises-dominated subgroups have emerged, exhibiting an island effect. This research offers an overview of the actual state of CDWMI collaborative innovation and its underlying causes, marking an advancement over existing research. The findings can assist policymakers in formulating evidence-based strategies to promote industry development.

In the era of artificial intelligence and fintech, improving the efficiency of financial analysis is essential for financial service providers. This article proposes a novel large language model-enhanced text mining workflow that leverages Internet-sourced text information to efficiently analyze supply chain finance business without requiring programming skills. We conduct a case study on the Chinese market for new energy buses—a rapidly growing sector due to government incentives and the push for sustainable urban transportation—using data from bidding websites and financial statements. The experimental results demonstrate that our LLM-enhanced workflow outperforms traditional methods, showcasing increased efficiency and practicality, especially for non-programming employees in supply chain financial services.

Fintech leverages blockchain and smart contracts for decentralized economic coordination. Its emerging applications in green finance and supply chain transparency hold promise for mitigating greenhouse gas (GHG) emissions. This study evaluates the global impact of Fintech development on GHG emissions. Based on cross-sectional regressions using data from 124 countries (2014–2018), the study finds that Fintech development significantly reduces GHG emissions—with each 1% increase in the Fintech development level, GHG emissions are reduced by 0.546%. This reduction is significant in low-, lower-middle-, and high-income countries, but not in upper-middle-income countries. Furthermore, the study finds that three components of Fintech activity—equity investments in Fintech companies, usage of Fintech credit, and digital payments—significantly reduce GHG emissions, with digital payments proving the most effective. This research broadens our understanding of Fintech's role in achieving global climate goals, demonstrating its substantial potential for sustainable development. Subgroup analyses further validate Fintech's key function within the Environmental Kuznets Curve (EKC) framework, highlighting how EKC moderates Fintech's emission reduction effects. Overall, these findings provide valuable insights for policymakers, financial institutions, and technology entrepreneurs aiming to mitigate the greenhouse effect through enhanced Fintech development.

Digital technologies have radically altered the nature of innovation and its development process. Innovations in the digital age are characterized by a reliance on a network of diverse actors who champion innovation. Despite the important role of these innovation actors' social networks in enabling interactions and collaboration for developing innovation, extant research lacks a synthesis of knowledge on innovation actors' social networks in the digital age. We provide a multi-disciplinary literature review on the social network of actors championing innovation and its interplay with digital technology based on a theoretical framework derived from social network research. Our analysis gives insights into the flows and structure of innovation actors' social networks. We find that digital technology has predominantly led to gradual changes in innovation actors' activities by functioning as a catalyst. Concerning the characteristics of innovation actors' networks, we find more fundamental changes in the agency of digital innovation, as innovation actors form communities and perform activities collectively. Building on the literature on digital technology and digital innovation, we identify promising avenues for future research. Overall, our literature review contributes by proposing a new conceptual model of innovation actors' social networks in the digital age and providing an agenda for future research.

As corporate social responsibility (CSR) gains increasing prominence in e-commerce, platforms are increasingly providing sales assistance to support their suppliers, such as JD's consumer-to-manufacturer initiatives, which help suppliers reduce their direct selling cost. However, the interplay between such sales assistance and the platform's CSR commitments remains poorly understood. While assistance can lower suppliers' operational cost, it may also reduce their profits in reselling channels, complicating the overall welfare implications. To systematically examine this interplay, we develop a game-theoretic model in which a CSR-oriented platform decides whether and how to assist a supplier, who subsequently chooses a distribution channel. Our results show that sales assistance benefits both parties only when the assistance is highly efficient and the commission rate is moderate. Otherwise, even well-intentioned assistance can harm the supplier due to high commission fees. Moreover, even if a platform with a high CSR level (i.e., one that pays more attention to suppliers' profits in addition to its own) exerts more assistance effort, a high commission rate can squeeze more profit, making sales assistance harmful to the supplier. Therefore, our study not only enriches the existing literature on channel structures by combining CSR and sales assistance but also provides valuable managerial insights. Specifically, it highlights that simply providing sales assistance is insufficient as it is necessary to simultaneously reduce commission fees for platform managers to achieve mutually beneficial outcomes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Managerial relevance statement:</i> This research provides crucial strategic guidance for engineering managers and policy makers navigating platform-driven supply chains. For engineering managers responsible for designing operational assistance systems, such as logistics optimization tools, supplier training modules, or data analytics platforms, our findings demonstrate that technical efficiency alone is insufficient. To create sustainable impact, these systems must be coupled with moderate commission structures and a nuanced understanding of channel selection dynamics. Specifically, managers should evaluate how assistance efficiency interacts with commission rates across different channel structures to avoid unintended consequences where platform support inadvertently diminishes supplier profitability. For policymakers and platform strategists, this study reveals that corporate social responsibility initiatives require careful calibration between operational support and financial pressures. Regulatory frameworks and platform governance models should encourage transparent pricing structures and balanced risk-sharing mechanisms. Wellintentioned assistance programs can inadvertently harm supplier viability when implementation overlooks these economic realities. Policy interventions could include establishing guidelines for fair commission practices or creating certification standards for effective supplier development programs. This paper also contributes to the following SDGs: SDG 8, SDG 9, SDG 12.

The real-time and extensive interactive interfaces of social media platforms have reshaped the internal and external knowledge synergy modes of enterprises, bringing many opportunities for enterprises to overcome inertia and realize breakthrough innovation. Introducing the organizational inertia perspective, in this article, we explore the differentiated mechanisms of two social media affordances for breakthrough innovation in enterprises. The results show that internal synergy-oriented social media use (ISM) promotes breakthrough innovation by reducing routine rigidity. Meanwhile, external interaction-oriented social media use (ESM) enhances breakthrough innovation by overcoming resource rigidity. In addition, internal entrepreneurial orientation and external competitive pressure play a moderating role. The higher the entrepreneurial orientation is, the greater the negative effect of ISM on routine rigidity, and the greater the negative effect of ESM on resource rigidity. The higher the competitive pressure is, the less negative the effect of ISM on routine rigidity and the less negative the effect of ESM on resource rigidity. This study extends the existing literature on the nexus between social media use and breakthrough innovation through the lens of organizational inertia. Furthermore, by elucidating the underlying pathways and contingent factors, this research offers a guidance for enterprises to optimize their social media technologies.