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Understanding, Analyzing and Predicting Online User Behavior

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Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the…

Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.
ContributorsLi, Chunxiao (Author) / Gu, Bin (Thesis advisor) / Chen, Pei-Yu (Committee member) / Xiong, Hui (Committee member) / Arizona State University (Publisher)
Created2019
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A Mechanistic Account of the Relation between Working Memory Capacity and Fluid Intelligence

Description
Working memory capacity and fluid intelligence are important predictors of performance in educational settings. Thus, understanding the processes underlying the relation between working memory capacity and fluid intelligence is important. Three large scale individual differences experiments were conducted to determine the mechanisms underlying the relation between working memory capacity and…

Working memory capacity and fluid intelligence are important predictors of performance in educational settings. Thus, understanding the processes underlying the relation between working memory capacity and fluid intelligence is important. Three large scale individual differences experiments were conducted to determine the mechanisms underlying the relation between working memory capacity and fluid intelligence. Experiments 1 and 2 were designed to assess whether individual differences in strategic behavior contribute to the variance shared between working memory capacity and fluid intelligence. In Experiment 3, competing theories for describing the underlying processes (cognitive vs. strategy) were evaluated in a comprehensive examination of potential underlying mechanisms. These data help inform existing theories about the mechanisms underlying the relation between WMC and gF. However, these data also indicate that the current theoretical model of the shared variance between WMC and gF would need to be revised to account for the data in Experiment 3. Possible sources of misfit are considered in the discussion along with a consideration of the theoretical implications of observing those relations in the Experiment 3 data.
ContributorsWingert, Kimberly Marie (Author) / Brewer, Gene A. (Thesis advisor) / McNamara, Danielle (Thesis advisor) / McClure, Samuel (Committee member) / Redick, Thomas (Committee member) / Arizona State University (Publisher)
Created2018

影响购物中心租金收入因素的研究

Description目前我国社会主要矛盾是“人民日益增长的美好生活需要和不平衡不充分的发展之间的矛盾”。消费对经济增长的贡献率达76.2%，而消费场所-购物中心为代表的的商业地产发展却落后于住宅地产的发展，不被房地产商所青睐。本文希望通过对影响购物中心租金收入因素的研究，运用统计回归模型分析，发现购物中心运营效益的重要决定因素。样本数据主要来自于8家上市公司的146个项目，对各项目2015年-2019年连续的租金收入进行分析。 现有研究购物中心文献，对于中国购物中心多区域多品牌动态运营绩效的研究文献很少，同时研究数据的取得难度大，还没有发现通过大样本数理统计得出的结论。 本文通过实证分析研究框架，运用二手数据，采用归纳研究，以“城市商圈的影响力”对购物中心每平米每日租金的影响进行回归分析。通过回归模型的量化分析，对日租金收入的影响因素城市商圈、城市人均GDP、开业期限、建筑面积、资金成本、出租率等进行分析，对购物中心品牌进行调节变量分析，得出城市商圈对日租金收入的影响是显著的，大城市的核心商圈或新城区核心商圈的购物中心日租金收入将强于同行，同时购物中心品牌的影响也是明显的。 中国住宅房地产投资已经到高点，房地产商未来的发展之路将面临新的选择。不同于住宅，购物中心的持有型特性将会给房地产商带来稳定的收入。通过对城市商圈等影响因子的分析，将对房地产商未来投资运营购物中心有重要的指导意义。
ContributorsLu, Xiaohui (Author) / Chen, Pei-Yu (Thesis advisor) / Chen, Xin (Thesis advisor) / Zhang, Anming (Committee member) / Arizona State University (Publisher)
Created2021