Transportation Network Companies (TNC) are companies that use online-enabled platforms to connect passengers with drivers. In recent years, they have sparked controversy with the taxi industry, which accuses TNCs of operating unfairly. In my study, I look at taxi regulation, consumer transportation preferences, and costs and benefits of TNCs. I analyze data comparing three of these companies, Uber, Lyft, and Sidecar, with a traditional taxicab, and evaluate trends in taxi employment from the Bureau of Labor Statistics. I find that Transportation Network Companies generally have shorter wait times, cheaper prices, and increased convenience, aspects that appeal to consumer preferences. I also find that taxi driver employment tends to fluctuate with economic conditions, however cities that are more likely to use TNCs exhibit smaller growth. I predict that at current conditions, TNCs such as Uber and Lyft will overtake taxi services. Thus, the taxi industry must focus on increasing TNC regulation, creating innovative technology, and modifying its service to appeal to consumers.

For many years, research on user acceptance of different technologies has been one of the most important topics within the field of information systems. In markets with the sheer size and uniqueness of the Chinese mobile economy fostered rapid development of sharing economy firms. Transportation Network Companies (TNC) can be regarded as a context of the sharing economy that focuses on personal transportation. Intrigued by the immense success of TNC and notorious competition between TNC companies Uber and DiDi in China, we study why users are susceptible to TNC. In this study, user acceptance is defined as intention to use TNC and the actual use of TNC. This study aims to examine what factors affect user acceptance of TNC in China and to what extent. By this, the thesis aims to provide TNC with adequate recommendations for success. The state of the art user acceptance model UTAUT2 has been used in this research with an explanatory purpose and a deductive approach. The UTAUT2 model consists of factors related to user acceptance, such as Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value and Habit. These factors were individually tested with Simple Linear Regression to determine their influence on user acceptance. These calculations were executed upon quantitative data from an electronically distributed survey. Upon analysis of the findings, research and practical implications are provided such as managerial recommendations for how TNC can raise user acceptance and increase market share.

The aim of this master thesis is to analyze the present condition of sharing economy and design a critical success factors model on a IT startups which operate as a transportation network companies. The result is finally validated on few representative businesses. The problem is solved by the modified qualitative critical success factors method whose author is John F. Rockart. The gained factors were finally used in the causal model which is based on the principles of system dynamics. The outcomes of this thesis are enabling to understand the relations which are hidden behind success of transportation network companies in the area of sharing economy. The information can be useful not only for startups but also for academic sphere and possible investors.

The major transit systems operating in San Francisco are San Francisco Municipal (MUNI), Bay Area Rapid Transit (BART), and Caltrain. The system of interest for this paper is MUNI, in particular the bus and light rail systems. During the past decade transit ridership in the area has experienced diverging growth, with bus ridership declining while rail ridership is growing significantly (Erhardt et al. 2017). Our data show that between 2009 and 2016, MUNI rail ridership increases from 146,000 to 171,400, while MUNI bus ridership decreases from 520,000 to 450,000. Direct ridership models (DRMs) are used to determine what factors are influencing MUNI light rail and bus ridership. The DRMs predict ridership fairly well, within 10% of the observed change. However, the assumption of no multi-collinearity is voided. Variables, such as employment and housing density, are found to be collinear. Fixed-effects panel models are used to combat the multi-collinearity issue. Fixed-effects panel models assign an intercept to every stop, so that any spatial correlation is removed. A transportation network company, Uber and Lyft, variable is introduced (TNC) to the panel models, to quantify the effect they have on MUNI bus and light rail ridership. The addition of a TNC variable and elimination of multi-collinearity helps the panel models predict ridership better than the daily and time-of-day DRMs, both within 5% of the observed change. TNCs are found to complement MUNI light rail and compete with MUNI buses. TNCs contributed to a 7% growth in light rail ridership and a 10% decline in bus ridership. These findings suggest that the relationship TNCs have with transit is complex and that the modes cannot be lumped together.