Wenqi Zhou

Online user reviews, product variety, and the long tail: An empirical investigation on online software downloads

Our study examines the impact of both a demand side factor (online user reviews) and a supply side factor (product variety) on the long tail and superstar phenomena in the context of online software downloading. The descriptive analysis suggests a significant superstar download pattern and also the emergence of the long tail. Using the quantile regression technique, we find the significant interaction effect between online user reviews and product variety on software download. We find that the impacts of both positive and negative user reviews are weakened as product variety goes up. In addition, the increase in product variety reduces the impact of user reviews on popular products more than it does on niche products. After taking the interaction effect into account, we find that the overall impact of the increased product variety helps niche products to get more downloads. These results highlight the importance of considering the intricate interplay between demand side and supply side factors in the long tail and online word-of-mouth research.

Do Professional Reviews Affect Online User Choices through User Reviews?: An Empirical Study

With the broad reach of the Internet, online users frequently resort to various word-of-mouth (WOM) sources, such as online user reviews and professional reviews, during online decision making. Although prior studies generally agree on the importance of online WOM, we have little knowledge of the interplay between online user reviews and professional reviews. This paper empirically investigates a mediation model in which online user reviews mediate the impact of professional reviews on online user decisions. Using software download data, we show that a higher professional rating not only directly promotes software download but also results in more active user-generated WOM interactions, which indirectly lead to more downloads. The indirect impact of professional reviews can be as large as 20% of the corresponding total impact. These findings deepen our understanding of online WOM effect, and provide managerial suggestions about WOM marketing and the prediction of online user choices.

Online Product Rating Manipulation and Market Performance

Manipulating online ratings of a product, in terms of both volume and value, can substantially influence its market performance, but the profits of a particular strategy can vary across products and might not be maximized by the highest rating values.

Risk Generation Mechanism of Complex Product System

CoPS (Complex product system) play a vital role in the modern economy and society, but the risks in CoPS innovation are very high. As the most urgent problem in Chinese CoPS development is how to identify and analyze the project risk systematically, It is necessary to explain the risk generation mechanism based on key risk factors identification, which can establish an organic linkage among risk identification, risk assessment and risk control. The path relationships from risk factors to risk consequences in CoPS innovation, which describe the risk generation mechanism in CoPS projects, are explored with structural equation modeling (SEM). The results of data analysis reveal that not all risk factors have direct effects on risk consequences, and the changes of several risk factors influence the consequences via some other factors indirectly

A novel iterative online rating attack based on market self-exciting property

The prosperity of online rating system makes it an important place for malicious vendors to mislead publics online decisions, whereas the security related studies are lagging behind. In this work, we adopt a quantile regression model to investigate influential factors on online user choices and reveal the “self-exciting” property of online market. Inspired by these findings, we propose a novel iterative rating attack and validate its advantage through experiments.

Efficiently Promoting Product Online Outcome: An Iterative Rating Attack Utilizing Product and Market Property

The prosperity of online rating system makes it a popular place for malicious vendors to mislead public’s online decisions, whereas the security related studies are lagging behind. In this paper, we develop a quantile regression model to investigate influential factors on online user choices and reveal that the promotion effect on products’ market outcomes is determined by not only the attacker’s manipulation power but also the specific property of the target product and the market self-exciting power. Inspired by these findings, we propose a novel iterative rating attack and validate its effectiveness through experiments.

Influencing Factors of E-innovation Performance

In this study, a model is developed and tested to evaluate E-innovation projects. The influencing factors are project strategy, feature of team, feature of team members, communication mechanism, process control, resources support, market feature and culture while the E-innovation performance are in terms of window of opportunity, financial performance and market share. We collect data via focused survey and questionnaires to nearly 50 E-innovation projects in China, and analyze these data through factor analysis and multiple regression. The results indicate that companies should pay more attention on three most significant factors-project strategy, market feature and culture, as they are the common influencing factors to the three dimensions of performance.