Browsing by Author "Li, Han"
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- Moving consumers from ‘free’ to ‘fee’: Addressing the vexing differentiation and fairness issues in the platform-based market of multiplayer online battle arena (MOBA) GamesWang, Le; Lowry, Paul Benjamin; Luo, Xin Robert; Li, Han (INFORMS, 2022-03)Companies in platform-based business markets have widely embraced freemium business models where profit primarily depends on a minority of paying customers. However, the key challenge of these models is transitioning participants from free users to paying consumers. To encourage paid consumption, companies often rely on product differentiation such as providing consumers who pay for products or services with enhanced features. However, limited research has addressed how such product differentiation may convert consumers from “free” to “fee.” Our research examines multiplayer online battle arena (MOBA) games as a compelling example of freemium platform-based business models. We contribute to the freemium literature by introducing three new MOBA-specific differentiations—character competency, character variety, and character-appearance differentiation. We also extend consumption values theory (CVT) into a dual-path model to unveil the underlying mechanisms through which product differentiation influences in-game purchase. We empirically validate our dual-path model using data from a two-wave longitudinal experiment and three cross-sectional experiments. Our findings support opposing mediating paths for product differentiation in character competency and variety and indicate that these two types of differentiation can indeed undermine perceived game fairness. Conversely, character-appearance differentiation exerts only a positive influence on players’ purchasing of in-game items. Consequently, the findings of this study have important potential implications for platform-based companies leveraging freemium business models that seek to increase their share of paying customers.
- Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature StudyLi, Han (Virginia Tech, 2014-11-04)Water temperature is a critical factor for the quality and biological condition of streams. Among various factors affecting stream water temperature, air temperature is one of the most important factors related to water temperature. To appropriately quantify the relationship between water and air temperatures over a large geographic region, it is important to accommodate the spatial and temporal information of the steam temperature. In this dissertation, I devote effort to several statistical modeling techniques for analyzing bivariate spatial-temporal data in a stream temperature study. In the first part, I focus our analysis on the individual stream. A time varying coefficient model (VCM) is used to study the relationship between air temperature and water temperature for each stream. The time varying coefficient model enables dynamic modeling of the relationship, and therefore can be used to enhance the understanding of water and air temperature relationships. The proposed model is applied to 10 streams in Maryland, West Virginia, Virginia, North Carolina and Georgia using daily maximum temperatures. The VCM approach increases the prediction accuracy by more than 50% compared to the simple linear regression model and the nonlinear logistic model. The VCM that describes the relationship between water and air temperatures for each stream is represented by slope and intercept curves from the fitted model. In the second part, I consider water and air temperatures for different streams that are spatial correlated. I focus on clustering multiple streams by using intercept and slope curves estimated from the VCM. Spatial information is incorporated to make clustering results geographically meaningful. I further propose a weighted distance as a dissimilarity measure for streams, which provides a flexible framework to interpret the clustering results under different weights. Real data analysis shows that streams in same cluster share similar geographic features such as solar radiation, percent forest and elevation. In the third part, I develop a spatial-temporal VCM (STVCM) to deal with missing data. The STVCM takes both spatial and temporal variation of water temperature into account. I develop a novel estimation method that emphasizes the time effect and treats the space effect as a varying coefficient for the time effect. A simulation study shows that the performance of the STVCM on missing data imputation is better than several existing methods such as the neural network and the Gaussian process. The STVCM is also applied to all 156 streams in this study to obtain a complete data record.