Shengli Li

This study investigates the factors influencing landlords’ price adjustments during peak seasons on accommodation-sharing platforms, focusing on the role of platform-certified and user-generated signals. Utilizing a comprehensive dataset of 11,795 observations from a leading Chinese accommodation-sharing platform, we employ binary logit models to investigate how these signals affect landlords’ pricing strategies during “festival” and “weekend” peak times. Our analysis reveals that both platform-certified signals (such as “Preferred House” badges) and user-generated signals (such as customer satisfaction ratings) significantly increase the probability and magnitude of price adjustments during both festival and weekend peak seasons. Specifically, houses with a “Preferred” status are up to 28 times more likely to have price hikes during weekends compared to non-preferred ones. Further analysis reveals that higher levels of landlord professionalism, measured by the number of properties managed, amplifies the impact of user-generated signals on both the probability and magnitude of price adjustments. However, as the level of professionalism increases, this effect diminishes, indicating that highly professional landlords may have less flexibility to adjust prices due to already-high baseline rates. Interestingly, landlord professionalism did not significantly influence the impact of platform-certified signals on price adjustments, suggesting that the influence of such signals remains consistent across different levels of landlord professionalism. These results underscore the significant roles that both types of signals and landlord professionalism play in shaping pricing strategies, offering valuable insights for platform management and policy formulation aimed at enhancing consumer trust and competitive dynamics in the sharing economy.

Innovation and competition are two major pillars in the information technology (IT) industry. In this paper, we examine the causal relationship between product market competition and innovations in the IT industry. We measure the innovations from both the quantity (number of patents) and the quality (number of non-self citations) perspectives. Based on the Herfindahl-Hirschman Index and natural experiments, we find robust evidence that the competition has a positive effect on the innovations of the IT industry. Specifically, after the exogenous shock to the market (import tariff cuts and thus increased industry competition), the number of patents per IT firm increases by 27.3 percent, and the number of non-self citations per IT firm increases by 38.2 percent on average. The effects are more pronounced in the more competitive sectors. Our study highlights the importance of the competition in the IT industry and suggests that the positive effect of competition on innovation in the IT context is likely to be driven by the parallel search effect. Our findings contribute to the recent policy debate and suggest that reducing import barriers may bring important welfare gains through innovations in the IT industry.

Previous research shows that online reviews may have different effects for search goods and experience goods. However, as a typical type of experience goods, software can be further divided into different categories based on product characteristics. Little research has been conducted regarding the different effects of online reviews for different types of software. Furthermore, to offer free samples is another common practice of software firms to alleviate consumer uncertainty prior to purchase. To fill the corresponding research gap, this research focuses on the interaction effects between online reviews and free samples for different types of software. Through our empirical analysis, we find that user ratings significantly increase consumers’ sample downloads. Furthermore, consumers download more samples for some categories than for others. Finally, user and editor ratings might have differential effects for different types of software.

In the context of the new curriculum standard, senior high school English reading for writing instructions have been given new requirements.The CLIL concept aims to enable students to acquire language knowledge and subject knowledge simultaneously and to enhance their bilingual thinking skills and cross-cultural communication skills, which can provide new ideas for improving the efficiency of senior high school English reading for writing instruction and realizing the goal of all-round education. Therefore, this article takes the reading for writing instruction of Unit 2 Travelling Around of PEP Edition of Senior High School English Subject as an example to explore the practice approach of CLIL theory. The 4Cs model of CLIL is used to optimize the teaching effectiveness of English reading for writing instructions in senior high school and to realize the synchronous development of both subject content and language.

This research aims to identify price determinants for sharing economy-based accommodation services and to further use the identified price determinants to predict accommodation prices. A dataset drawn from Airbnb.com, was collected for analysis. We identify price determinants from five categories. The top five price determinants are identified as room type, city, distance to tourist attractions, number of pictures posted, and number of amenities provided. More importantly, we find that interaction effects between variables can also significantly influence price. Finally, a series of price prediction models are built based on the identified price determinants.