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学者姓名：李嫣然
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Abstract ：
Rapid burgeoning of mobile social media has become the primary channel by which individuals construct interpersonal networks and contact with each other recently. The primary objectives of this study are to explore how and whether network externalities would enhance WeChat users' distinct types of perceived gratifications, and how such perception of gratifications subsequently impacts their attitudes and continuous usage behavior. This article utilized a structured web-based survey questionnaire to conduct empirical research, and gathered and analyzed data of 788 WeChat users by structural equation modeling (SEM) approach. Perceived gratifications are categorized into three dimensions: hedonic gratifications, social gratifications, and utilitarian gratifications. Obtained outcomes reveal that both referent network size and perceived complementarity are positively and significantly associated with all three dimensions of perceived gratifications. Additionally, hedonic gratifications, social gratifications, and utilitarian gratifications are positively related to users' attitudes toward WeChat, which, in turn, are significantly correlated with their continuous usage behavior. These findings contribute to the growing body of literature on WeChat user behavior, offering novel theoretical insights for academics and practical implications for mobile social media operators.
Keyword ：
Attitudes Attitudes Continuous usage behavior Continuous usage behavior Mobile social media Mobile social media Network externalities Network externalities Perceived gratifications Perceived gratifications
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| GB/T 7714 | Pang, Hua , Qiao, Yuxin , Li, Yanran et al. Do network externalities and perceived gratifications boost WeChat continued use? Combining perspectives of network externalities and uses and gratifications [J]. | ACTA PSYCHOLOGICA , 2024 , 251 . |
| MLA | Pang, Hua et al. "Do network externalities and perceived gratifications boost WeChat continued use? Combining perspectives of network externalities and uses and gratifications" . | ACTA PSYCHOLOGICA 251 (2024) . |
| APA | Pang, Hua , Qiao, Yuxin , Li, Yanran , Wang, Lei . Do network externalities and perceived gratifications boost WeChat continued use? Combining perspectives of network externalities and uses and gratifications . | ACTA PSYCHOLOGICA , 2024 , 251 . |
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Abstract ：
This paper focuses on the problem of emergency restricted zone avoidance for high-speed drones. Existing path planning methods struggle with inefficiency due to large search spaces, slow convergence rates, and difficulties in path planning for high-speed drones. To overcome these challenges, we propose a novel approach that integrates rules with deep learning. This hybrid approach simplifies decision-making by converting temporal decisions into a limited set of middle way-points, significantly reducing the complexity of both the state and solution search space. Additionally, rules are employed to prevent aimless exploration within the solution space. To enhance the algorithm performance, we introduce a situation prediction model, which is trained to capture the relationship between way-points and flight outcomes, such as restricted zone encounters and energy consumption. Experimental results demonstrate notable improvements over purely rule-based methods, with high success rates in avoiding restricted zones and maintaining sufficient kinetic energy to reach the goal. This approach effectively addresses the challenges posed by high-speed drones operating under complex physical models and dynamic emergency scenarios. Copyright © 2024 Yan Zheng, et al.
Keyword ：
Deep learning Deep learning High-speed drone High-speed drone Path planning Path planning
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| GB/T 7714 | Zheng, Y. , Li, Y. , Zhang, Y. et al. RUDE: Fusing Rules and Deep Learning for High-Speed Drone Path Planning [J]. | Advances in Artificial Intelligence and Machine Learning , 2024 , 4 (4) : 2969-2980 . |
| MLA | Zheng, Y. et al. "RUDE: Fusing Rules and Deep Learning for High-Speed Drone Path Planning" . | Advances in Artificial Intelligence and Machine Learning 4 . 4 (2024) : 2969-2980 . |
| APA | Zheng, Y. , Li, Y. , Zhang, Y. , Lian, S. , Wen, Y. , Yu, Y. . RUDE: Fusing Rules and Deep Learning for High-Speed Drone Path Planning . | Advances in Artificial Intelligence and Machine Learning , 2024 , 4 (4) , 2969-2980 . |
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In practice, online livestreaming demonstration and in-store demonstration services for product sales have experienced rapid development. In response to consumers' cross-channel access to product demonstration and showrooming behavior of purchasing products at the lowest price via each channel, manufacturers increasingly offer consumers more comprehensive product demonstration services by establishing an omni-channel market. Considering this emerging trend, this paper analyzes the optimal sales strategies for the livestreaming demonstration modes and the pricing strategies for an omni-channel manufacturer. Additionally, this paper further digs into the manufacturer's dilemma under the influence of omni-channel retailing and consumer showrooming behavior when adopting livestreaming demonstrations. Initially, we consider a scenario where a manufacturer sells a product through the omni-channel, demonstrating its products in the offline store and hiring an online shopping influencer as the sales agent for the livestreaming demonstration. Subsequently, three modes are examined: non-livestreaming mode, basic livestreaming mode, and time-limited livestreaming mode. Through game theory modeling, we compare each mode's optimal pricing strategies and analyze the omni-channel manufacturer's preference for livestreaming demonstration modes and the influencing factors. The results indicate that while livestreaming demonstrations enhance consumers' product valuation, the manufacturer cannot always achieve higher profits through the livestreaming demonstration. Besides, the time-limited promotion measures of the livestreaming mode are not always beneficial to the manufacturer. Furthermore, the impact of the livestreaming demonstration effort level of the sales agent, the browsing cost of different channels, and the impact of the livestreaming demonstration on product suitability are analyzed.
Keyword ：
Livestreaming demonstration Livestreaming demonstration Omni -channel Omni -channel Pricing strategy Pricing strategy Sales mode Sales mode Showrooming Showrooming
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| GB/T 7714 | Li, Yanran , Li, Bo , Wang, Minxue et al. Optimal sales strategies for an omni-channel manufacturer in livestreaming demonstration trends [J]. | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW , 2023 , 180 . |
| MLA | Li, Yanran et al. "Optimal sales strategies for an omni-channel manufacturer in livestreaming demonstration trends" . | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 180 (2023) . |
| APA | Li, Yanran , Li, Bo , Wang, Minxue , Liu, Yang . Optimal sales strategies for an omni-channel manufacturer in livestreaming demonstration trends . | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW , 2023 , 180 . |
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The rapid technological revolution of Industry 4.0 has set off rapid advancement in the smart product development. Testing the smart product's system before release to market is crucial for ensuring proper functioning. Smart product manufacturers must carefully consider different testing modes to choose the optimal one based on product characteristics and consumer's shopping intention. Additionally, different testing modes have varying impacts on the environment and social welfare, as well as profitability of the product. This study utilizes a two-period game theory model to analyze a smart product manufacturer's optimal testing mode and pricing decision. Three testing mode are evaluated: manual testing mode (MT mode), artificial intelligence testing mode (AT mode), and public testing mode (PT mode). Consumers' utility is influenced by factors such as consumer preferences, product quality, usage duration, and network externality effect. Our findings indicate that the optimal testing mode depends on quality improvement and network effect coefficients. Specifically, when the degree of quality improvement is high and the network effect is moderate, MT mode is preferred; when the quality improvement is high but the network effect is small, AT mode dominates; under other conditions, PT mode is optimal. Quality improvement and network effect coefficients have similar effects on pricing decisions but impact the profits in different manners. Social welfare is the highest under MT mode when the network effect is high and quality improvement is low, under AT mode when network effect is low and quality improvement is high, and under PT mode when both factors are high or low. Finally we provide a numerical example of how profit-driven business decisions may conflict with social welfare, potentially leading to lower social welfare than the theoretical optimum under the chosen testing mode strategy.
Keyword ：
Artificial Intelligence Artificial Intelligence Game Theory Game Theory Pricing decision Pricing decision Smart product Smart product Social welfare Social welfare Testing strategy Testing strategy
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| GB/T 7714 | Li, Yanran , Zheng, Yan , Teo, Yon Shin et al. Is AI testing beneficial for the manufacturer and social welfare? Optimal test strategy of a smart product [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 241 . |
| MLA | Li, Yanran et al. "Is AI testing beneficial for the manufacturer and social welfare? Optimal test strategy of a smart product" . | EXPERT SYSTEMS WITH APPLICATIONS 241 (2023) . |
| APA | Li, Yanran , Zheng, Yan , Teo, Yon Shin , Lin, Shang-Wei . Is AI testing beneficial for the manufacturer and social welfare? Optimal test strategy of a smart product . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 241 . |
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