In mobile computing, there is variety of approaches to develop applications. One challenge is the diversity of mobile devices in the market and targeting more than one mobile device to develop applications. One approach to handle this is traditional, in which for every device there is a project carried out to develop application. Another method is the cross-platform application development which has been introduced in recent years. The purpose of this thesis is to explore if existing cross-platform frameworks can aid startup companies to develop cross-platform mobile apps. The research questions are 1) How can cross-platform frameworks be a viable approach for start-up companies in this respect? 2) What challenges and benefits do cross-platform approaches bring to developers? In order to answer the research questions, respondents from five small-sized and two large companies developing mobile apps are being interviewed. The results shows that cross-platform frameworks are good tools when developing mobile apps for multiple platforms, but are applicable to different degrees depending on the actual case. Challenges and benefits identified by the respondents mainly touched upon decreasing development time, and less coding effort, but also the uniqueness of Apple and Android products and difficulties in making developers switch to new languages.

I developed a resource to aid people going through reentry and reverse culture shock after returning from long stays abroad. Based on my experience with the JET Programme, I used North American JET alumni as my case study, but aimed to develop a solution that would be scalable and useful for people in other programs and situations. While JET and other similar programs assist and support participants in the initial journey abroad and during their stay, they tend to not provide much support when the participants return. This is a real issue because culture shock upon returning from an extended trip is stronger and generally unexpected than what’s experienced at the initial departure. Because of the increasing number of people going abroad for extended stays, this highlights a very real need for support, either from the organizing groups or from alumni of those groups and programs. I began my exploratory research with an extensive survey, where I gathered information on people’s situations prior to joining JET, various qualitative and quantative aspects of their time in Japan, and their experiences upon their return. I continued to conduct exploratory research to gather people’s personal experiences, and I worked closely with JET alumni during my generative and evaluative research phases as well. From the beginning, I leaned towards an online solution that would connect JET alumni regardless of distance or location. Though I considered other options, this approach was vindicated by the strong needs for “community” and “shared experiences” voiced by JET alumni throughout all my research. Ultimately I developed a plan for an online platform that enables people to share their experiences through writing and other media, as well as to communicate and connect easily with others. The name of this platform is okaeri, which means both “return” (verb) and “welcome” (greeting) in Japanese. Beyond the site’s structure and function, the key element is the visual-verbal rhetorical strategy throughout the content and layout, which will set an empathetic tone and perpetuate the sense of community that already exists among JET alumni.

Partially Observable Markov Decision Process (POMDP) is widely used to model sequential decision making process under uncertainty and incomplete knowledge of the environment. It requires strong computation capability and is thus usually deployed on powerful machine. However, as mobile platforms become more advanced and more popular, the potential has been studied to combine POMDP and mobile in order to provide a broader range of services. And yet a question comes with this trend: how should we implement POMDP on mobile platform so that we can take advantages of mobile features while at the same time avoid being restricted by mobile limitations, such as short battery life, weak CPU, unstable networking connection, and other limited resources. In response to the above question, we first point out that the cases vary by problem nature, accuracy requirements and mobile device models. Rather than pure mathematical analysis, our approach is to run experiments on a mobile device and concentrate on a more specific question: which POMDP implementation is the ``best'' for a particular problem on a particular kind of device. Second, we propose and justify a POMDP implementation criterion mainly based on battery consumption that quantifies ``goodness'' of POMDP implementations in terms of mobile battery depletion rate. Then, we present a mobile battery consumption model that translates CPU and WIFI usage into part of the battery depletion rate in order to greatly accelerate the experiment process. With our mobile battery consumption model, we combine a set of simple benchmark experiments with CPU and WIFI usage data from each POMDP implementation candidate to generate estimated battery depletion rates, as opposed to conducting hours of real battery experiments for each implementation individually. The final result is a ranking of POMDP implementations based on their estimated battery depletion rates. It serves as a guidance for on POMDP implementation selection for mobile developers. We develop a mobile software toolkit to automate the above process. Given basic POMDP problem specifications, a set of POMDP implementation candidates and a simple press on the ``start'' button, the toolkit automatically performs benchmark experiments on the target device on which it is installed, and records CPU and WIFI statistics for each POMDP implementation candidate. It then feeds the data to its embedded mobile battery consumption model and produces an estimated battery depletion rate for each candidate. Finally, the toolkit visualizes the ranking of POMDP implementations for mobile developers' reference. Evaluation is assessed through comparsion between the ranking from estimated battery depletion rate and that from real experimental battery depletion rate. We observe the same ranking out of both, which is also our expectation. What's more, the similarity between estimated battery depletion rate and experimental battery depletion rate measured by cosine-similarity is almost 0.999 where 1 indicates they are exactly the same.

The aim of this thesis was to increase understanding of King company success in the social mobile game industry by asking the question: How does a company manage to organize the innovation work in successful casual games within social mobile gaming industry? In order to answer it, we conducted a case study research with secondary data in which we examined the company to discover the elements that contribute to this success, despite a lack of research in how these kind of companies build their business model and strategies, highlighting the players' behaviour. Our findings conclude it is possible to success in social mobile game industry using incremental innovation in different aspects: games design, implementation of the games, and in the business model.  By applying this innovation, with a good viral strategy and giving the player the decision to play by free or purchasing virtual goods, King has been able to become the largest developer game company on Facebook.

The need for aggregating privacy policies is present in a variety of application areas today. In traditional client/server models, websites host services along with their policies in different private domains. However, in a cloud-computing platform where aggregators can merge multiple services, users often face complex decisions in terms of choosing the right services from service providers. In this computing paradigm, the ability to aggregate policies as well as services will be useful and more effective for users that are privacy conscious regarding their sensitive or personal information. This thesis studies the problems associated with the Platform for Privacy Preference (P3P) language, and the present issues with communicating and understanding the P3P language. Furthermore, it discusses some efficient strategies and algorithms for the matching and the merging processes, and then elaborates on some privacy policy conflicts that may occur after merging policies. Lastly, the thesis presents a tool for matching and merging P3P policies. If successful, the merge produces an aggregate policy that is consistent with the policies of all participating service providers.

In this thesis, we present a tool that is an extension of the NAVIRE Framework and used in remote environment exploration and navigation. It is available for end users on everyday devices such as desktops and mobile devices. The tool offers the characteristic of being cross-platform and easy to use and manipulate. Cubic panoramas that are generated by the NAVIRE Framework are loaded in the tool and rendered in 3D space on the user’s device. The 3D panoramas are interactive by allowing the user to view the scene in all direction, move from one scene to another or through interaction with augmented objects. The tool is geared to be interactive in the most natural and intuitive fashion using the inputs available in the different devices of the end-user.

The product platform strategy is well known for its several positive effects. However, these effects differ under different market situations. Several product platform projects related decisions can influence these effects. This research work will show how these effects are influenced by decisions under different market situations, findings will help academics in enriching product platform theories and, it will help managers to take proper decisions to enhance the possibility of product platform project success.

Im Sommersemester 2012 wurde die Online Community auditorium von Informatik-Studenten der TU Dresden entwickelt. Die Online Plattform bietet den Studierenden und Mitarbeitern einen Anlaufpunkt für alle Fragen und Antworten rund um das Studium. Der Fokus lag darin, eine zentrale Anlaufstelle für die studentischen Belange zu schaffen, um das vorhandene Wissen besser teilen zu können und für später zu archivieren.

This thesis studies the value of branding in the smartphone market. Measuring brand value with data available at product level potentially entails computational and econometric challenges due to data constraints. These issues motivate the three studies of the thesis. Chapter 2 studies the smartphone market to understand how operating system platform providers can grow one of the most important intangible assets, i.e., brand value, by leveraging the indirect network between two user groups in a two-sided platform. The main finding is that iPhone achieved the greatest brand value growth by opening its platform to the participation of third-party developers, thereby indirectly connecting the consumers and the developers via its app store effectively. Without the open app store, I find that iPhone would have lost its brand value by becoming a two-sided platform. Hence these findings provide an important lesson that open platform strategy is vital to the success of building platform brands. Chapter 3 solves a computational challenge in structural estimation of aggregate demand. I develop a computationally efficient MCMC algorithm for the GMM estimation framework developed by Berry, Levinsohn and Pakes (1995) and Gowrisankaran and Rysman (forthcoming). I combine the MCMC method with the classical approach by transforming the GMM into a Laplace type estimation framework, therefore avoiding the need to formulate a likelihood model. The proposed algorithm solves the two fixed point problems, i.e., the market share inversion and the dynamic programming, incrementally with MCMC iteration. Hence the proposed approach achieves computational efficiency without compromising the advantages of the conventional GMM approach. Chapter 4 reviews recently developed econometric methods to control for endogeneity bias when the random slope coefficient is correlated with treatment variables. I examine how standard instrumental variables and control function approaches can solve the slope endogeneity problem under two general frameworks commonly used in the literature.

This thesis studies the value of branding in the smartphone market. Measuring brand value with data available at product level potentially entails computational and econometric challenges due to data constraints. These issues motivate the three studies of the thesis. Chapter 2 studies the smartphone market to understand how operating system platform providers can grow one of the most important intangible assets, i.e., brand value, by leveraging the indirect network between two user groups in a two-sided platform. The main finding is that iPhone achieved the greatest brand value growth by opening its platform to the participation of third-party developers, thereby indirectly connecting the consumers and the developers via its app store effectively. Without the open app store, I find that iPhone would have lost its brand value by becoming a two-sided platform. Hence these findings provide an important lesson that open platform strategy is vital to the success of building platform brands. Chapter 3 solves a computational challenge in structural estimation of aggregate demand. I develop a computationally efficient MCMC algorithm for the GMM estimation framework developed by Berry, Levinsohn and Pakes (1995) and Gowrisankaran and Rysman (forthcoming). I combine the MCMC method with the classical approach by transforming the GMM into a Laplace type estimation framework, therefore avoiding the need to formulate a likelihood model. The proposed algorithm solves the two fixed point problems, i.e., the market share inversion and the dynamic programming, incrementally with MCMC iteration. Hence the proposed approach achieves computational efficiency without compromising the advantages of the conventional GMM approach. Chapter 4 reviews recently developed econometric methods to control for endogeneity bias when the random slope coefficient is correlated with treatment variables. I examine how standard instrumental variables and control function approaches can solve the slope endogeneity problem under two general frameworks commonly used in the literature.