Within the domain of live-streaming e-commerce, the application of artificial intelligence (AI) technologies presents novel growth opportunities. Live-streaming sales by AI streamers have attracted widespread attention as a new form. To promote the application of AI streamers in live-streaming e-commerce, this paper mainly addresses the following question: How do AI streamers in live-streaming e-commerce influence consumer trust and purchase intention? Based on this, guided by the stimulus–organism–response (SOR) theory, we construct a research model to study the impact of AI streamers’ characteristics and scenario fit on purchase intention through trust mediation. Subsequently, 410 valid samples of consumers who watch AI streamers promoting products are collected through an online questionnaire. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are integrated to investigate both the influence mechanisms and configurational pathways affecting purchase intention. This study reveals that AI streamers’ image characteristics (cuteness, vitality) and scenario fit are positively associated with purchase intention. However, AI streamers’ ability characteristics (professionalism, responsiveness) are not positively associated with purchase intention. In addition, consumer trust partially mediates the relationship between these key factors and purchase intention. Further, consumer innovativeness significantly negatively moderates the effect of AI streamers’ characteristics and scenario fit on consumer trust. Moreover, the influence mechanisms diverge substantially between incremental and breakthrough products.

Considering the potential impacts of digitalization and greening in manufacturing on industrial chain and supply chain (ICSC) resilience, this paper develops a multidimensional evaluation index system. The system includes immune resistance, adaptive resilience, autonomous control, innovation competitiveness, and development sustainability. Using the entropy weight method, we measure manufacturing ICSC resilience across provinces and cities in the Yangtze River Economic Belt from 2017 to 2022 and further comprehensively analyze its spatiotemporal evolution and key influencing factors. The findings indicate that though the overall ICSC resilience in the region is relatively high, significant disparities exist between provinces and cities. The average resilience index value of the Yangtze River Delta region remained above 0.4, while that of other provinces was generally below 0.2. The spatial distribution of resilience shifted significantly during the study period, with marked improvements observed in all the regions. The number of high-value areas increased from three to nine, while only two areas had relatively lower values. Furthermore, the financing environment and the degree of digitization exhibited a strong positive correlation with resilience, whereas price fluctuations and excessive government intervention exerted adverse effects. Finally, this paper proposes corresponding policy recommendations to enhance ICSC resilience.

In this paper, we study a restaurant’s take-out model choice and the coordination of an online take-out supply chain. To this end, we first derive the restaurant’s optimal price and/or platform’s commission rate under the restaurant’s three possible take-out models: do not provide online take-out service (NTO model), provide take-out service by joining an online take-out platform (TOF model), or provide online take-out service by itself (TOH model). We investigate the restaurant’s optimal take-out model choice. We then derive the optimal decisions of price and the take-out model under centralization, and study the online take-out supply chain coordination problem. We find that, first, the restaurant may not always benefit from providing online take-out service. It will be beneficial only if the incremental demand generated by take-out service is high. Second, under the centralized supply chain, the TOF model is always better than the TOH model. Meanwhile, when the incremental demand is high, the restaurant should choose the TOF model; otherwise, the NTO model is better. Third, we find that the restaurant’s take-out price and model choice decisions under a decentralized supply chain are both inconsistent with that under the centralized supply chain. Last, we design a sales reward contract which can achieve the price and model choice coordination as well as win-win outcomes for all supply chain members.

The main research question asked in this paper is whether and when a dual-channel retailer (retailer in short) should adopt the “buy online and pick up in store” (BOPS) strategy. To answer this question, we first derive the optimal price decision using the non-BOPS and BOPS strategies. Subsequently, we compare the performance of retailers under non-BOPS and BOPS scenarios. Our main findings are that under the monopoly scenario, retailers may not always benefit from the BOPS strategy. Retailers will benefit only if the offline operational costs are low and the degree of customer acceptance of the online channel is high. However, the BOPS strategy cannot improve dual-channel retailers’ market share. Furthermore, under a Stackelberg game scenario with e-retailers as leaders, when the value of a product is medium and the transaction costs of the offline channel are high, retailers can use the BOPS strategy to enhance their market share. If the degree of customer acceptance of the online channel is also high, retailers can further improve their profits by using the BOPS strategy. Overall, these findings not only provide decision support for retailers, but also enrich the theories on dual-channel retailing in operations management.

Promoting carbon emission reduction in road freight transportation is important to achieve low-carbon development. The carbon trading mechanism is an effective market mechanism to promote carbon emission reduction. The digital and networked features of the online freight platform (OFP) service supply chain (SSC) not only help the platform reduce carbon emissions but also facilitate the government’s achievement of efficient and economic supervision of carbon emissions. Therefore, this paper proposes two types of carbon trading mechanism based on the OFP SSC to investigate the carbon emission reduction decision of the OFP, namely an absolute emission cap-based allocation (AC) model and an intensity-based allocation (IC) model. By using game theory, we then analyze the optimal solutions of the OFP SSC under the non-participation in carbon trading market (NC model), the AC model, and the IC model. By comparing these decisions, we explore the impact of the carbon trading mechanism on the OFP SSC. Results show the following: (1) Carbon trading mechanisms reduce OFP emissions, particularly under IC models with high free allowances. (2) High initial allowances and low service costs under the carbon trading mechanism enhance the OFP’s profit. (3) The carbon trading mechanism can reduce the carbon emissions of the road freight sector when initial allowances are sufficient or the off-platform trucker’s carbon emission coefficient is low. The study concludes that the IC model optimizes emission cuts while maintaining platform profitability. From a managerial perspective, the government should adopt dynamic allowance policies and incentivize the OFP’s participation through data integration. OFPs must balance network growth with low-carbon technology adoption to align commercial and environmental objectives.