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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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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
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Description随着我国市场经济的高速发展和城乡居民消费水平的日益提高,我国老年市场有着极为广阔的发展空间,老年辅助用品行业是发展潜力巨大的朝阳产业。然而国内许多企业都面临着经营方向不明确、营销方案不清晰等问题,这些问题严重影响了老年产品及服务的发展,同时也延缓了我国老年产业的发展进程。因此,本文以老年辅助用品行业的客户满意度为研究对象,分析情感营销对老年客户满意度的影响,以及健康顾问对客户满意度的影响。

本研究主要包括以下内容。首先,在文献梳理和政策研究的基础上,系统阐述情感、情感营销、顾问式情感营销、老年辅助用品等相关概念、特点,以及系统回顾情感营销相关理论,同时详细分析了我国老年辅助用品市场的现状以及未来的发展方向,随后对国内外有关老年辅助用品的研究文献进行了总结归纳。其次,基于理论回顾的内容提出了本研究的四个具体假设,并构建了相应的分析模型。紧接着开发并检验了量表,对数据进行了描述性统计及相关性分析,采用了层次分析的方法对上述假设进行了分析和检验,并详细探讨了产品情感营销和服务情感营销具体维度对老年客户满意度的影响。最后,得出了相关结论,即产品情感营销和服务情感营销能够显著提高老年客户的满意度水平,健康顾问的专业素养和心理素质也能显著提高老年客户的满意度水平。此外,也进一步提出本研究的理论意义和实践意义,以及存在的不足和未来研究展望。

关键词: 情感营销;健康顾问;客户满意度;老年辅助用品
ContributorsZhan, Jianxing (Author) / Chen, Pei-Yu (Thesis advisor) / Chen, Xinlei (Thesis advisor) / Zhu, Qigui (Committee member) / Arizona State University (Publisher)
Created2020