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学者姓名：李嫣然
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Abstract ：
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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Abstract ：
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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