Volume 2 No. 10, October 2012 ISSN 2223-4985 International Journal of Information and Communication Technology Research ©2012 ICT Journal. All rights reserved http://www.esjournals.org 769 Evaluating Factors Affecting Broadband Intensity in Kenya Gilbert Barasa Mugeni 1 , Gregory Wabuke Wanyembi 1 , Joseph Muliaro Wafula 2 1 Department of Computer Science, Masinde Muliro University of Science and Technology, Kakamega, Kenya 2 Department of Computing, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya. ABSTRACT The term “broadband intensity” emerged nearly a decade ago from the Organisation for Economic Co-operation and Development (OECD) as one the domains for the assessment of broadband markets. The other two domains were broadband “adoption” and “impact”, respectively. OECD then defined broadband intensity to include the “nature, value and volume of broadband transactions”. Over the years, there have been a number of both macro and micro level studies on broadband adoption and the economic benefits associated with broadband. However, not much study has been undertaken in the area of broadband intensity. In the recent past, the emergence of mobile broadband and the associated value added services (VAS) and applications (Apps) has re-kindled interest in the subject area of broadband intensity. This study 3 investigates factors affecting broadband intensity in a developing country context, in this instance Kenya. In view of the increasing access to broadband via mobile and nomadic devices in developing countries, access to and use of mobile broadband is included in the investigation to determine the demographic and control factors affecting broadband intensity in Kenya. The data on these variables was collected using a self-administered questionnaire approach. Data was tested for non-response bias and regression analysis conducted to test the role of the variables in influencing broadband intensity. The findings of this research suggest that age, level of education, income, awareness of mobile broadband, and mobile broadband use are key influencers of broadband intensity in Kenya. The paper proceeds to outline the research methodology, findings and recommendations. Keywords: Intensity, Broadband, Mobile Broadband, VAS &Apps, Kenya, Developing countries 3 This study is a product of on-going Ph. D research work entitled “A framework for broadband metrics for developing countries”, funded by the National Council for Science and Technology (NCST), Ministry of Higher Education, Science and Technology, Nairobi, Kenya. 1. INTRODUCTION This study is based on an on-going research on the topic “A Framework for Broadband Metrics for Developing Countries”, based on a research model consisting of broadband readiness, intensity and adoption [1]. Studies on broadband readiness and adoption were already concluded. In order to comprehensively assess the state of broadband in a developing country context, [2] recommends that broadband be viewed as an ecosystem that includes networks, the services that the networks carry, the applications they deliver, and users. This three-domain research model of broadband readiness, intensity and adoption compliments the broadband eco-system suggested by [2]. Only a few studies have investigated broadband intensity [3][4] and therefore this study makes a significant contribution to the body of knowledge on broadband intensity within a developing country context. Based on the findings of this research, policy makers and ICT regulators will be able to more clearly and accurately identify un-served and underserved areas, and appropriately target investment and resources. Broadband service providers could employ the findings in modeling and forecasting projected demand and revenue strategies, as well as supporting their ability to meet evolving consumer and business demands. As a guide to the evaluation of the factors affecting broadband intensity in Kenya, the study sought to answer the following questions. 1. What relationship exists between demographic factors and broadband intensity among Kenyans? 2. What factors have the greatest impact in explaining variations in broadband intensity in Kenya? The rest of this paper is structured as follows: Section 2 gives a theoretical underpinning of the study, Section 3 provides a brief discussion of the research methodology. The findings are then presented and discussed in sections 4 and 5. Finally, a conclusion and recommendations of the research are provided in the last section. 2. THEORETICAL BASIS Certain characteristics or variables associated with an individual’s innovativeness i.e demographics could be key in investigating the variations in the intensities of new technologies such as broadband. This study adopts three main approaches to investigating broadband intensity in Kenya, the theory of planned behavior (TPB) [5][6], the technology acceptance model (TAM) [7][8], and the diffusion of innovations theory (DOI) [9][10].The central factor in the theory of planned behaviour is the individual’s intention to perform a given behavior based on some motivational factors that influence the behavior [5]. The technology acceptance model is based on an individual’s formation of an intention to act [8] regardless of the constraints, whereas the diffusions of innovations theory is based on