Date of Award

August 2015

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Management Science

First Advisor

Mark Srite

Committee Members

Atish Sinha, Huimin Zhao, Sanjeev Kumar, Xiaojing Yang

Keywords

Cultrue, Data Analysis, Envy, Sentiment Analysis, Social Media

Abstract

Social Media (SM) has grown to be one of the most popular Internet technologies for individual users and has fostered a global community. For instance, recent statistics reveal that monthly active users of Facebook are almost 1.5 billion by Mar 2015. At the same time, 20% of internet users in the US are expected to have Twitter accounts. This figure has grown from 15.2% in 2012, and is expected to rise to 24.2% by 2018 (Twitter 2015).

People like spending their time on SM to track the latest news, seek knowledge, update personal status, and connect with friends. It is possible that being exposed to others’ positive information on SM could generate darker emotions, such as envy. Extant literature suggests that envy significantly influences human behaviors and life satisfaction (Krasnova et al. 2013).

This dissertation, consisting of three essays, studies the effects of SM on human behaviors. Chapter 2 investigates how others’ positive information arouses envy and influences user behaviors from different angles. Chapter 3 focuses on how espoused national cultures reshape online benign envy and impact SM usage. Chapter 4 discusses the relationship between social media and envy with textual analysis techniques. Chapter 5 provides a summary and overall conclusion to this work.

Chapter 2- Envy and How it can Influence SM Use

Users tend to disclose the positive side of their lives on SM. Such information can be perceived in an extremely positive light in the eyes of their connections, which could leads to envy. In the current study, we develop a theoretical framework that elaborates the mechanism through which online envy is generated and consequently influences SM usage. We specify that online users experience two types of envy: malicious and benign envy, which have distinct impacts on IS use. Specifically, malicious envy plays a mitigating role and benign envy serves as an enhancer of SM use. Our findings provide valuable implications for both academic researchers and IS practitioners.

Chapter 3 – Benign Envy, Social Media, and Culture

Although envy universally exits in human society, its influence on human behaviors varies by cultural contexts. As shown in chapter 2, benign envy is a more salient factor in the social media context. In the current essay, we focus on investigating how different espoused national cultural values affect this relationship between online benign envy and consequent behaviors. We also developed a benign envy and IT usage model, which integrates four espoused national cultural values. We conceptualized several main constructs and then theoretically justified the relationships between them. As expected, if people experience benign envy when using SM, they are more likely to continue their use. Moreover, different espoused national culture values work as independent and moderating variables along with the envy procedures. People who hold different levels of culture behave distinctly. The study found that people who espouse a greater level of collectivism were be more likely to compare with other peers in order to evaluate their self-social status; people who espouse higher levels of uncertainty avoidance were more likely to experience benign envy; and the relationship between perceived enhancement and use intention was stronger for individuals with higher levels of espoused masculinity. However, espoused power distance values were not significantly moderating the relationship between perceived enjoyment and intended behavior in the current context (general SM). This study provided some theoretical and practical implications.

Chapter 4 – Tweet, Favorite, Status, and Envy

Many social media studies have demonstrated that aggregating social information could provide valuable insight into sociological, economical, healthcare, and other critical fields. Among these studies, Twitter has been one of the most popular social platforms that researchers value. It has a greater potential for academics to observe and explore critical social behaviors, such as envy, which could lead to avoidance of using certain IT platforms, emotional depression, and even worse, suicide. With text mining techniques, massive numbers of tweets can be collected, classified, and analyzed. The envy literature has largely theorized on the motivations of envy. However, in the IS context, envy related research is very limited, and the empirical tests are confounded by limited data. In order to address these gaps, we collected envy related tweets from Twitter and classified them into the two types (benign and malicious) of envy relying on text mining techniques with sentiment analysis (positive to negative). Based on the data set, we further analyzed the patterns of online envy. Additionally, by using logistic regression, the impacts of certain social media usage behaviors were tested on differentiating online envy. Our work included both qualitative observation and quantitative analysis, along with the evaluation of regression output.

Available for download on Monday, October 23, 2017

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