Matching Items (5)
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- All Subjects: Information Technology
- All Subjects: strategic behavior
- Genre: Doctoral Dissertation
- Creators: Gu, Bin
- Resource Type: Text
Description
Mobile applications (Apps) markets with App stores have introduced a new approach to define and sell software applications with access to a large body of heterogeneous consumer population. Several distinctive features of mobile App store markets including – (a) highly heterogeneous consumer preferences and values, (b) high consumer cognitive burden of searching a large selection of similar Apps, and (c) continuously updateable product features and price – present a unique opportunity for IS researchers to investigate theoretically motivated research questions in this area. The aim of this dissertation research is to investigate the key determinants of mobile Apps success in App store markets. The dissertation is organized into three distinct and related studies. First, using the key tenets of product portfolio management theory and theory of economies of scope, this study empirically investigates how sellers’ App portfolio strategies are associated with sales performance over time. Second, the sale performance impacts of App product cues, generated from App product descriptions and offered from market formats, are examined using the theories of market signaling and cue utilization. Third, the role of App updates in stimulating consumer demands in the presence of strong ranking effects is appraised. The findings of this dissertation work highlight the impacts of sellers’ App assortment, strategic product description formulation, and long-term App management with price/feature updates on success in App market. The dissertation studies make key contributions to the IS literature by highlighting three key managerially and theoretically important findings related to mobile Apps: (1) diversification across selling categories is a key driver of high survival probability in the top charts, (2) product cues strategically presented in the descriptions have complementary relationships with market cues in influencing App sales, and (3) continuous quality improvements have long-term effects on App success in the presence of strong ranking effects.
ContributorsLee, Gun Woong (Author) / Santanam, Raghu (Thesis advisor) / Gu, Bin (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Created2015
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 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
Description
By collecting and analyzing more than two million tweets, U.S. House Representatives’ voting records in 111th and 113th Congress, and data from other resources I study several aspects of adoption and use of Twitter by Representatives. In the first chapter, I study the overall impact of Twitter use by Representatives on their political orientation and their political alignment with their constituents. The findings show that Representatives who adopted Twitter moved closer to their constituents in terms of political orientation.
By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives on Twitter to evaluate their effects on Representatives’ political polarization in the second chapter. I found support for the effects of repeated expressions and peer influence in Representatives’ political polarization.
Last but not least, by employing a recently developed dynamic network model (separable temporal exponential-family random graph model), I study the effects of homophily on formation and dissolution of Representatives’ Twitter communications in the third chapter. The results signal the presence of demographic homophily and value homophily in Representatives’ Twitter communications networks.
These three studies altogether provide a comprehensive picture about the overall consequences and dynamics of use of online social networking platforms by Representatives.
By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives on Twitter to evaluate their effects on Representatives’ political polarization in the second chapter. I found support for the effects of repeated expressions and peer influence in Representatives’ political polarization.
Last but not least, by employing a recently developed dynamic network model (separable temporal exponential-family random graph model), I study the effects of homophily on formation and dissolution of Representatives’ Twitter communications in the third chapter. The results signal the presence of demographic homophily and value homophily in Representatives’ Twitter communications networks.
These three studies altogether provide a comprehensive picture about the overall consequences and dynamics of use of online social networking platforms by Representatives.
ContributorsMosuavi, Seyedreza (Author) / Gu, Bin (Thesis advisor) / Vinzé, Ajay S. (Committee member) / Shi, Zhan (Michael) (Committee member) / Arizona State University (Publisher)
Created2016
Description
The recent changes in the software markets gave users an unprecedented number
of alternatives for any given task. In such a competitive environment, it is imperative
to understand what drives user behavior. To that end, the research presented in
this dissertation, tries to uncover the impact of business strategies often used in the
software markets.
The dissertation is organized into three distinct studies into user choice and post
choice use of software. First using social judgment theory as foundation, zero price
strategies effects on user choice is investigated, with respect to product features,
consumer characteristics, and context effects. Second, role of social features in
moderating network effects on user choice is studied. And finally, the role of social
features on the effectiveness of add-on content strategy on continued user engagement
is investigated.
The findings of this dissertation highlight the alignments between popular business
strategies and broad software context. The dissertation contributes to the litera-
ture by uncovering hitherto overlooked complementarities between business strategy
and product features: (1) zero price strategy enhances utilitarian features but not
non-utilitarian features in software choice, (2) social features only enhance network
externalities but not social influence in user choice, (3) social features enhance the
effect of add-on content strategy in extending software engagement.
of alternatives for any given task. In such a competitive environment, it is imperative
to understand what drives user behavior. To that end, the research presented in
this dissertation, tries to uncover the impact of business strategies often used in the
software markets.
The dissertation is organized into three distinct studies into user choice and post
choice use of software. First using social judgment theory as foundation, zero price
strategies effects on user choice is investigated, with respect to product features,
consumer characteristics, and context effects. Second, role of social features in
moderating network effects on user choice is studied. And finally, the role of social
features on the effectiveness of add-on content strategy on continued user engagement
is investigated.
The findings of this dissertation highlight the alignments between popular business
strategies and broad software context. The dissertation contributes to the litera-
ture by uncovering hitherto overlooked complementarities between business strategy
and product features: (1) zero price strategy enhances utilitarian features but not
non-utilitarian features in software choice, (2) social features only enhance network
externalities but not social influence in user choice, (3) social features enhance the
effect of add-on content strategy in extending software engagement.
ContributorsKanat, Irfan (Author) / Santanam, Raghu (Thesis advisor) / Vinze, Ajay (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2016
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 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