With the deep popularity of mobile Internet, the “eyeball economy” is more active than ever. Driven by powerful modern media, livestreaming, as a new form of attracting public attention to obtain economic benefits, is worth studying its influence path on consumers. Based on the technology acceptance model and the mediating effect of emotion, this study constructs the consumer influencing factor model of livestreaming e-commerce. The research model and related hypotheses are verified by SPSS and linear multiple regression models. The research found that emotional trust and perceived emotional value could be regarded as mediating variables to stimulate consumers' purchase intention in livestreaming e-commerce. They have a full mediating effect on product and atmosphere and a partial mediating effect on homogeneity and promotion, which identifies that online celebrity's homogeneity, and sales promotion could influence consumers' purchase intention through the partial mediating role of emotional trust and perceived emotional value, while product and atmosphere induced by emotional contagion could exert influence on consumers' purchase intention through the full mediating effect of emotional trust and perceived emotional value.

The ongoing climate changes have put forth various challenges for each industry to achieve their sustainable environmental goals, in an orderly manner. Like the other extensively growing industries, China's Film and Drama industries are responsible for contributing effectively toward a sustainable environment. In order to validate the same, this study has applied the Nonlinear Autoregressive Distributed Lag (NARDL) technique, so as to identify the asymmetric association among the Film and Drama Industry, green innovation, and sustainable environment. The quarterly time-series data of the variables is assessed from January 2000 to December 2019. The outcomes of the study have revealed that the appreciation in the Film and Drama stock prices tends to increase the CO₂ emissions. Whereas, the depreciation of the Film and Drama stock prices improves the efforts towards a sustainable environment. On the other hand, the increase in green innovation tends to have an adverse impact on environmental degradation, while the decline in green innovation tends to increase the carbon emissions. In order to handle the grievous problem of environmental degradation, caused by the Film and Drama industry of China, this study suggests that adopting green practices in Film and Drama making, such as green filming, the use of hybrid vehicles, LED lights, and solar generator supplies, and perhaps Computer-Generated Imagery sets will ultimately contribute towards the improvement of the quality of the environment.

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality.

With the deep integration of industries brought about by big data technology, users’ design needs are diversifying and individuating. Thanks to big data technology, users’ diverse design needs can be precisely met. Meanwhile, big data can be used to realize emotional interaction for personalized design needs of users, resulting in a better user experience. Using grounded theory to mine user demand text data, this paper investigates the influencing factors of emotional interaction and dynamic resource allocation in the digital design supply chain. The results show that government-driven factors have a direct impact on the demand for industrial design in user emotional interactions. Market factors are the most fundamental in the development of an industrial design service platform, and universities play an important role in this. Furthermore, a lack of market sensitivity stems from a lack of emotional interaction with users, resulting in a schism between industry, university, and research, which has become a major impediment to the development of China’s industrial design industry. This study not only lays the theoretical groundwork for understanding the mechanisms of user emotional interaction on IDSPs, but it also points the way forward for future industrial design service platform development.

This paper examines how AI adoption affects firms' labor investment efficiency, focusing on moderating the impact of globalization in GCC nations. Using firm-level panel data from 2015 to 2024, it applies a System Generalized Method of Moments (GMM) to account for endogeneity and dynamic adjustments. Dynamic Capabilities Theory (DCT) suggests that AI adoption is a strategic resource for a firm to improve efficiency. Using four country-level globalization proxies: the Globalization Index (KOF), the Digital Adoption Index (DAI), the Global Competitiveness Index (GDI), and Total Factor Productivity (TFP), it found that globalization moderates AI adoption's impact on labor investment efficiency, supporting Globalization and Labor Market Theory, which states that globalization affects enterprises' resource optimization through global markets and technologies. It also shows that AI adoption improves labor investment efficiency significantly, and globalization's moderating effect strengthens this relationship, especially in more globally integrated countries.