Matching Items (3)
Filtering by

Clear all filters

157177-Thumbnail Image.png
Description
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
157017-Thumbnail Image.png
Description基于中国人口老龄化加速、目前人均医疗支出偏低和国内医疗器械企业以中低端产品

为主的局面,中国的医疗器械行业正面临着巨大的发展机遇,投融资活动将迎来爆发式增

长。 在此背景下, 医疗器械企业的估值研究对投融资双方都有较大的意义。

我们假设医疗器械公司的产品差异度对其公司的估值有正面影响。

产品差异度定义为:该产品区别于其他竞争性产品的独特性,由以下六个方面构成:

产品唯一性水平、先进性(器械类别、优秀国产医疗设备个数、产品的专利化程度)、利润

边际和其市场容量,并对此用 12 个指标做出了定量的估计。本研究主要的数据来源是上市

的医疗器械公司,因为这些公司的相关数据取得比较容易且数据客观性较强。我们使用一

般回归分析测量产品差异度与公司估值之间的关系。在得出正面的回归结果之后, 我们采

用双重差分分析(DID)方法,验证实际情形下新产品相关信息发布对公司股价波动的影响。

根据回归分析结果:

1、 “产生营收的产品唯一性水平”和“边际利润”与市值有显著相关性: 说明医疗器械

类企业确实是核心产品驱动发展的, 产品唯一性程度高(已剔除那些已逐渐被市场淘汰的

产品) 说明市场定价能力强, 边际利润率高,盈利能力强, 进而对公司估值形成正面影

响。

2、 “”净利润“和”“互联网概念”与市值也呈现显著相关性。净利润的相关性是显而易见

的。互联网概念的相关性体现了互联网+医疗成为近几年市场的风口。

iv

3、 其他一些指标未呈现明显的相关性,有可能是因为我们的数据量太少引起的, 也

有可能还有其他未在我们考虑范围内的因素导致的,也可能是因为中国目前的股票市场还

未到达半强式有效市场。这可能对其他行业的影响也是如此。

在后面进行的实证分析中, 除个别情况外,我们发现公司重磅新产品相关信息的发布

基本上对公司之后 1-30 个交易日的股价起到了较明显的推动作用。

关键词: 产品差异度 医疗器械行业 公司估值
ContributorsShen, Huifeng (Author) / Chen, Pei-Yu (Thesis advisor) / Wang, Tan (Thesis advisor) / Jiang, Zhan (Committee member) / Arizona State University (Publisher)
Created2018
156674-Thumbnail Image.png
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