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Yuandong Xu,Mengmeng Zhang,Huanzhang Wang 글로벌지식마케팅경영학회 2023 Global Marketing Conference Vol.2023 No.07
AI recommendation service is adopted in consumption consulting such as high-tech and fashion consumption (Thapliyal & Ahuj, 2021). Now, for high-tech and fashion products, the advance selling strategy is widely adopted. Thus, this study targets to detective the consumers’ preference toward AI agents comparing human agents under advance selling and spot selling. The independent variable of this study is consumption type: Pre-sale Products vs. Spot Products. Pre-sale Products are quite popular currently, especially technological products. Construal-level theory (CLT) offers a valuable framework to explain the mechanisms that trigger evaluations, predictions, and behaviors by linking the degree of mental abstraction (the construal level) to psychological distance (Trope & Liberman, 2000; 2003; 2010). Four dimensions including temporal, special, social, and probability distance are argued to present the psychological distance (Trope et al., 2007). Liberman et al. (2022) discuss the time distance and argue the distant-future events are represented in a more abstract, structured, high-level manner than near-future events. Kim & Duhachek (2020) draw on a dimension of persuasion by AI agents to posit that AI agents are perceived as low-construal agents because of the fact that people hold a lay theory that AI agents do not have superordinate goals and cannot learn from their experiences or possess consciousness like humans do. Consequently, they find that individuals perceive greater appropriateness and are more persuaded when an AI agent’s persuasive messages highlight low-construal as opposed to high-construal features. Moreover, consumers prefer abstract information related to a certain product rather than concrete information when a purchase is to take place in the distant future or when construal levels are high (Hernandez et al., 2015). Thus, this research hypothesizes: When consumers buy pre-sale products (vs. spot products), human agents will be the more favorable service provider than AI agents since the consumer is under a high level of construal. This research proposes to adopt a 2 (Advance Selling vs. Spot Selling) x 2 (Short Psychological Distance vs. Far Psychological Distance) x 2 (AI Agents vs. Human Agents) between groups experimental study to test the main effects and mechanism (H1). Furthermore, this study would identify the key moderating effects to discuss the boundary effects of the mechanism for establishing marketing strategies with AI services for managers.
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Yuandong Lei,Bohao Xu,Ling Yu 국제구조공학회 2024 Structural Engineering and Mechanics, An Int'l Jou Vol.91 No.2
Sparse regularization methods have proven effective in addressing the ill-posed equations encountered in moving force identification (MFI). However, the complexity of vehicle loads is often ignored in existing studies aiming at enhancing MFI accuracy. To tackle this issue, a double l1 regularization method is proposed for MFI based on a response spectrum-based weighted dictionary in this study. Firstly, the relationship between vehicle-induced responses and moving vehicle loads (MVL) is established. The structural responses are then expanded in the frequency domain to obtain the prior knowledge related to MVL and to further construct a response spectrum-based weighted dictionary for MFI with a higher accuracy. Secondly, with the utilization of this weighted dictionary, a double l1 regularization framework is presented for identifying the static and dynamic components of MVL by the alternating direction method of multipliers (ADMM) method successively. To assess the performance of the proposed method, two different types of MVL, such as composed of trigonometric functions and driven from a 1/4 bridge-vehicle model, are adopted to conduct numerical simulations. Furthermore, a series of MFI experimental verifications are carried out in laboratory. The results shows that the proposed method’s higher accuracy and strong robustness to noises compared with other traditional regularization methods.
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