Research on Customer Loyalty of Online Short-term Rental Service: A Meta-analysis

Online short-term rental service has developed rapidly recently. Various scholars focused on how to improve customer loyalty of online short-term rental service, but their conclusions are usually different. Therefore, we built a comprehensive analysis to derive a unified conclusion. A meta-analysis was conducted according to the effect sizes extracted from 35 empirical articles about customer loyalty of online short-term rental service. The effect of customer loyalty classification was further explored from the two sub-dimensions, behavioral loyalty and composite loyalty. The results of the main effect analysis show that only sustainability has no significant effect on attitude. The loyalty classification analysis proves the validity and particularity of the results from the perspective of sub-dimensions of loyalty. The conclusions of this study will bring significant enlightenment to the academic and industry

User Needs Mining Based on Topic Analysis of Online Reviews

The purpose of this paper is to aggregate the topic information of online review text and clarify the user needs. We conducted the study on online reviews of women’s clothing store of Taobao.com with semantic analysis and text mining. Online reviews were collected by means of web crawler. Using Chinese word segmentation tool and data analysis tool, the word frequency statistics was realized. The statistical software was used for the clustering analysis and multidimensional scaling analysis of high frequency keywords. The results show that the content of online reviews mainly includes four topics: basic features of products, additional features of products, user experience and product display. It reveals the potential user needs of women’s clothing store of Taobao.com, which cannot only help consumers to make rational decisions, but also provide guidance to merchants and manufacturers

SoK: Decentralized Finance (DeFi) Attacks

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi's popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain's transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security? In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners' community. For example, few academic papers address "price oracle attacks" and "permissonless interactions", while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) SoTA bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges

Leveraging Machine Learning for Bidding Strategies in Miner Extractable Value (MEV) Auctions

The emergence of blockchain technologies as central components of financial frameworks has amplified the extraction of market inefficiencies, such as arbitrage, through Miner Extractable Value (MEV) from Decentralized Finance smart contracts. Exploiting these opportunities often requires fee payment to miners and validators, colloquially termed as bribes. The recent development of centralized MEV relayers has led to these payments shifting from the public transaction pool to private channels, with the objective of mitigating information leakage and curtailing execution risk. This transition instigates highly competitive first-price auctions for MEV. However, effective bidding strategies for these auctions remain unclear. This paper examines the bidding behavior of MEV bots using Flashbots\u27 private channels, shedding light on the opaque dynamics of these auctions. We gather and analyze transaction data for the entire operational period of Flashbots, providing an extensive view of the current Ethereum MEV extraction landscape. Additionally, we engineer machine learning models that forecast winning bids whilst increasing profitability, capitalizing on our comprehensive transaction data analysis. Given our unique status as an adaptive entity, the findings reveal that our machine learning models can secure victory in more than 50% of Flashbots auctions, consequently yielding superior returns in comparison to current bidding strategies in arbitrage MEV auctions. Furthermore, the study highlights the relative advantages of adaptive constant bidding strategies in sandwich MEV auctions

SoK: Decentralized Finance (DeFi) Attacks

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi’s popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain’s transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security? In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners’ community. For example, few academic papers address “price oracle attacks” and “permissonless interactions”, while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) SoTA bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges

Influencing Factors of University Students’ Use of Social Network Sites: An Empirical Analysis in China

This paper explores the influencing factors of Chinese university students in accepting and using social networking sites (SNS) to propose measures and recommendations that can guide and help these students correctly use SNS. In addition, this paper aims to provide theoretical support in increasing user loyalty for the SNS service providers and attract new users. The correlation and multiple regression analyses showed that perceived value, enjoyment, and influence positively influence the intention of individuals to use SNS

Influencing Factors of University Students’ Use of Social Network Sites: An Empirical Analysis in China

This paper explores the influencing factors of Chinese university students in accepting and using social networking sites (SNS) to propose measures and recommendations that can guide and help these students correctly use SNS. In addition, this paper aims to provide theoretical support in increasing user loyalty for the SNS service providers and attract new users. The correlation and multiple regression analyses showed that perceived value, enjoyment, and influence positively influence the intention of individuals to use SNS

Influence Factors of Satisfaction with Mobile Learning APP: An Empirical Analysis of China

The main purpose of this study is to construct the user satisfaction structure model of mobile learning APP (software designed that run on mobile devices) from the perspective of the mobile learning participants, and to analyze the influence factors of satisfaction with mobile learning APP. The results of this study show that perceived responsiveness and perceived content are both the important factors that affect user satisfaction with mobile learning APP. Users have high satisfaction with the content conforming to the mobile learning context. Users expect to get immediate feedback in the process of learning, which can improve the learning methods and efficiency

Influence Factors of Satisfaction with Mobile Learning APP: An Empirical Analysis of China

The main purpose of this study is to construct the user satisfaction structure model of mobile learning APP (software designed that run on mobile devices) from the perspective of the mobile learning participants, and to analyze the influence factors of satisfaction with mobile learning APP. The results of this study show that perceived responsiveness and perceived content are both the important factors that affect user satisfaction with mobile learning APP. Users have high satisfaction with the content conforming to the mobile learning context. Users expect to get immediate feedback in the process of learning, which can improve the learning methods and efficiency