An examination of factors that influence entrepreneurial intention of high school students in Kenya

Abstract Considerable research has been carried out on entrepreneurship in efforts to understand its incidence in order to influence and maximize its benefits. Essentially, researchers and policy makers have sought to understand the link between individuals and business creation: Why some people start businesses while others do not. The research indicates that personality traits, individual background factors and association of entrepreneurship with career choice and small business enterprises, cannot sufficiently explain entrepreneurship. It is recognized that entrepreneurship is an intentional process and based on Ajzen’s Theory of Planned Behavior, the most defining characteristic of entrepreneurship is the intention to start a business. The purpose of this study was, therefore, to examine factors that influence entrepreneurial intention in high school students in Kenya. Specifically, the study aimed at determining if there were relationships between the perceptions of desirability, and feasibility of entrepreneurship with entrepreneurial intention of the students, identifying any difference in these perceptions with students of different backgrounds, and developing a model to predict entrepreneurship in the students. The study, therefore, tested how well Ajzen’s Theory of Planned Behavior applied in the Kenyan situation. A questionnaire was developed and administered to 969 final year high school students at a critical important point in their career decision making. Participants were selected using a combined convenience and random sampling technique, considering gender, rural/urban location, cost, and accessibility. Survey was the major method of data collection. Data analysis methods included descriptive statistics, correlation, ANOVA, factor analysis, effect size, and regression analysis. iii The findings of this study corroborate results from past studies. Attitudes are found to influence intention, and the attitudes to be moderated by individual background factors. Perceived personal desirability of entrepreneurship was found to have the greatest influence on entrepreneurial intention and perceived feasibility the lowest. The study findings also showed that perceived social desirability and feasibility of entrepreneurship contributed to perception of personal desirability, and that the background factors, including gender and prior experience, influenced entrepreneurial intention both directly and indirectly. In addition, based on the literature reviewed, the study finds that entrepreneurship promotion requires reduction of the high small business mortality rate and creation of both entrepreneurs and entrepreneurial opportunities (Kruger, 2000; Shane & Venkataraman, 2000). These findings have theoretical and practical implications for researchers, policy makers, teachers, and other entrepreneurship practitioners in Kenya.

Preventing encrypted traffic analysis

Many existing encrypted Internet protocols leak information through packet sizes and timing. Though seemingly innocuous, prior work has shown that such leakage can be used to recover part or all of the plaintext being encrypted. The prevalence of encrypted protocols as the underpinning of such critical services as e-commerce, remote login, and anonymity networks and the increasing feasibility of attacks on these services represent a considerable risk to communications security. Existing mechanisms for preventing traffic analysis focus on re-routing and padding. These prevention techniques have considerable resource and overhead requirements. Furthermore, padding is easily detectable and, in some cases, can introduce its own vulnerabilities. To address these shortcomings, we propose embedding real traffic in synthetically generated encrypted cover traffic. Novel to our approach is our use of realistic network protocol behavior models to generate cover traffic. The observable traffic we generate also has the benefit of being indistinguishable from other real encrypted traffic further thwarting an adversary's ability to target attacks. In this dissertation, we introduce the design of a proxy system called TrafficMimic that implements realistic cover traffic tunneling and can be used alone or integrated with the Tor anonymity system. We describe the cover traffic generation process including the subtleties of implementing a secure traffic generator. We show that TrafficMimic cover traffic can fool a complex protocol classification attack with 91% of the accuracy of real traffic. TrafficMimic cover traffic is also not detected by a binary classification attack specifically designed to detect TrafficMimic. We evaluate the performance of tunneling with independent cover traffic models and find that they are comparable, and, in some cases, more efficient than generic constant-rate defenses. We then use simulation and analytic modeling to understand the performance of cover traffic tunneling more deeply. We find that we can take measurements from real or simulated traffic with no tunneling and use them to estimate parameters for an accurate analytic model of the performance impact of cover traffic tunneling. Once validated, we use this model to better understand how delay, bandwidth, tunnel slowdown, and stability affect cover traffic tunneling. Finally, we take the insights from our simulation study and develop several biasing techniques that we can use to match the cover traffic to the real traffic while simultaneously bounding external information leakage. We study these bias methods using simulation and evaluate their security using a Bayesian inference attack. We find that we can safely improve performance with biasing while preventing both traffic analysis and defense detection attacks. We then apply these biasing methods to the real TrafficMimic implementation and evaluate it on the Internet. We find that biasing can provide 3-5x improvement in bandwidth for bulk transfers and 2.5-9.5x speedup for Web browsing over tunneling without biasing.