The emotional expressions in live streaming e-commerce possess a strong contagious effect, enabling viewers to easily resonate with the specific emotions conveyed by the streamers and consciously build an empathy transmission chain. This study constructs a regression model based on the emotional contagion theory and explores the impact of empathy between streamers and viewers on sales performance. Using data from 30 live streams, totaling 22,707 min, from one of China’s most popular live streaming rooms, “East Buy”, between February and April 2024, we demonstrate the significant positive impact of empathy between streamers and viewers on sales. Additionally, product type positively moderates this relationship. The unexpected thing is that live streaming time does not significantly affect the relationship between empathy and sales. This study employs text sentiment analysis methods to extract emotional features from the streamers’ speech and real-time comments from viewers. Our research extends the application of emotional contagion theory to the context of live-streaming e-commerce, enriches the literature on emotional interaction in service marketing, and provides practical insights for live-streaming platforms and streamers. Streamers can optimize marketing strategies and achieve sales goals by creating a more engaging and empathetic live-streaming experience.

This article introduces a new metric for evaluating digital innovation in enterprise transformation using textual analysis of annual reports from U.S.-listed companies. Through network analysis and topic modeling, we identified 12 topics categorized into three main areas: digital technology innovation, customer-oriented digital strategy, and digital transformation in traditional business operations. Our research indicates that digital innovation strategies are critical for maintaining competitiveness and have shifted to a more innovation management-oriented approach. We also found differences in digital innovation strategies between companies and industries. Our study contributes to the theoretical significance of enterprise management and sustainable development.

Purpose: This study investigates how the information and sentiment of content conveyed by AI agents on digital platforms influence user engagement with multimodal content. Analyzing data on 2,238 agents from the character AI system, we show how text and images activate distinct information-processing channels and jointly shape user interactions. Design/methodology/approach: We analyze multimodal data from AI agents using structural topic modeling (STM), VADER sentiment analysis, image information entropy and a ResNet-50 deep-learning model. Grounded in dual coding theory (DCT) and the elaboration likelihood model (ELM), we focus on two attributes – informational richness and sentiment polarity – for both text and images, and test their impacts on users’ online engagement behavior. Findings: Text information shows an inverted-U relationship with user engagement. Image visual complexity moderates the effect of textual informational richness. Although aggregate sentiment does not significantly predict engagement, image sentiment amplifies the effect of text sentiment. Images with low emotional intensity create an “emotional vacuum” that increases engagement when paired with positive textual sentiment. Originality/value: By integrating DCT and ELM, this study offers a new framework for explaining the behavioral effects of multimodal content. It also introduces a method for quantifying the informational and affective attributes of text–image pairs. The findings provide actionable guidance for optimizing digital marketing content and the design of AI-driven conversational agents.

In the context of climate change, the increasing urgency to mitigate environmental impacts has driven firms to adopt green supply chain strategies. Existing research primarily focuses on either carbon tax or emission trading schemes, leaving a gap in understanding the combined impact of hybrid carbon policies. This study addresses this gap by developing a dual-tier supply chain model with a manufacturer and retailer, exploring the effects of a carbon tax, emission trading, and a hybrid policy on emission reduction strategies and pricing decisions. Using a reverse inductive method within a Stackelberg game framework, we identify optimal strategies for emission reduction and profit maximization under each policy scenario. Results indicate that the hybrid policy achieves the lowest unit carbon emissions when the manufacturer’s initial pollution level is below a critical threshold. This research contributes to the literature by providing actionable insights into the strategic advantages of hybrid carbon policies for firms seeking both profitability and sustainability in green supply chains.

Modern smart agriculture increasingly relies on edge computing for real-time, high-concurrency tasks such as wide-area drone-based crop monitoring. However, highly volatile workloads and severe environmental noise in agricultural Internet of Things (IoT) networks often lead to resource congestion and high latency when relying on traditional reactive scheduling. To address these challenges, this paper proposes a hybrid prediction-driven proactive resource scheduling method for edge computing. We construct a Variational Mode Decomposition-Convolutional Neural Network-Attention-Bidirectional Long Short-Term Memory (VMD-CNN-Attention-BiLSTM) model to filter environmental noise and accurately capture the spatio-temporal features of bursty traffic. Furthermore, a deep reinforcement learning scheduling algorithm based on Proximal Policy Optimization (PPO) incorporates future workload trends into its state space, dynamically optimizing task offloading. To evaluate the proposed Predictive Computational Scheduling Framework (PCSF), we developed a custom edge computing simulation environment and synthesized a hybrid dataset combining real-world server logs from the Alibaba Cluster Trace with deep learning inference workloads derived from a Wheat Plant Diseases image repository. Simulations demonstrate that the prediction model achieves a Root Mean Square Error of 0.030 and a Mean Absolute Error of 0.0215. Compared to static and reactive baselines, the PCSF reduces average task timeout violations to 2.2 and total system energy consumption by nearly 40%. This proactive mechanism effectively overcomes decision-making lags, enabling efficient, low-latency computing resource allocation for modern agricultural facilities.