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Enterprises with consistent and sufficient revenues are more likely to access credit services from local and international financing institutions. In this study, participants reported that RVR’s revenue stayed below performance target for far too long, which weakened the concessionaire’s ability to meet overhead costs, pay concession fees regularly, attract financing and keep business afloat in the midst of competition. Tariff adjustment scored a relative importance index of 0.5; again, suggesting that the indicator was an average predictor of the project’s financing. Pricing of transport services is often a matter of in-depth economic analysis. In other words, comprehensive feasibility studies should precede pricing processes. In this study, participants reported that RVR reserved the right to adjust tariffs when necessary; however, tariff adjustments were, in most cases, boardroom decisions. Consequently, adjustment of tariffs often resulted GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 340 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 to a drop in business volume, with far-reaching consequences on revenues, liability portfolio and financing of infrastructure development. Concession period scored a relative importance index of 0.3, suggesting that it was a weak predictor of the project’s financing. Notably, the concession period was 25 years for freight services and 5 years for passenger services (IEA-Kenya, 2014). The challenge with fixed concession periods is that there is no time to adjust to internal and external factors that may heighten non-completion and/or demand-related risks. In this regard, the length of concession period should hook around the concessionaire’s recovery of investments. Concession periods vis-à-vis market risk factors, is a key element that usually inform financiers’ investment decisions.

Concordance of Perceptions regarding Influence of Concessional Factors on the Project’s Financing The analysis obtained an average level of concordance in the ranking of concessional factors influencing the project’s financing. The results suggest up to 95% chance that the level of concordance was statistically significant (W = 0.618, χ2 = 17.248, df = 3 & ρ-value = 0.015). The results show that participants’ perceptions regarding the influence of concessional factors on the project’s financing were concordant and the level of concordance was statistically significant. The results suggest that concessional factors are important to the success of concession projects.


The purpose of PPP initiatives is to improve the delivery of essential services to citizens, in line with national development goals. The success of PPP initiatives is important to all stakeholders, including citizens, public authorities and private sector operators. This study reveals that concessional factors are important predictors of the success of concessional projects. Regarding the railway project in Kenya, the findings suggest that concessional factors might have contributed to under-financing and underperformance that characterized the project’s first decade. As the railway project gets into its second decade of concession, failure to address emerging concessional challenges is likely to continue preventing the project from reaching its full productivity potential. Concessions run for 25 to 30 years, which makes it necessary for partners to create regular interludes for joint review of concessional contracts, in view of issues arising from internal and external environments. Notably, 30 years is a long time and many changes may occur during implementation, which may prevent concessional projects from achieving set objectives.

Consequently, it is important for concessional contracts to have provisions for periodical revisions to facilitate implementation processes as well as improve financing and attainment of performance targets.


We thank University of Nairobi for giving Stephen Okelo Lucas (co-author) the opportunity and a scholarship to pursue his PhD degree in Project Planning and Management. We acknowledge the support of Prof. Joyce Kanini Mbwesa in supervising and guiding Stephen through the process of conducting this study and developing the Thesis. We are also grateful to all the participants who volunteered their time to provide the requisite information. Finally, we thank our colleague, Tom Odhiambo for reviewing the draft manuscript and providing insightful comments.


African Development Bank (2011). Rift Valley Railway Project: Environmental and Social Assessment.

Addis Ababa: ADB Group Asian Development Bank (2010). Public Private Partnership Handbook. Manila: AsDB.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 341 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Frimpong, Y., Olowoye, J. and Crawford, L. (2003). “Causes of Delay and Cost Overruns in Construction of Ground Water Projects in Developing Countries: Ghana as a Case Study,” International Journal of Management, Vol. 21 (1), p. 321-326.

Institute of Economic Affairs Kenya (2014). Railway Transportation Policy in Kenya: State of Play and Policy Challenges. Nairobi: IEA-Kenya.

Kometa, S.T., Oloimolaiye, P. and Harris, F. (1994). “Attributes of UK Construction Clients Influencing Project Consultants’ Performance,” Construction Management Economics, Vol. 12, p. 433-443.

Ministry of Transport (2014). “Standard Gauge Railway: Forging New Frontier in Railway Development In Kenya And The Region,” Uchukuzi, Issue 1, June 2014.

United Nations (2011). A Guidebook on Public-Private Partnership in Infrastructure. Bangkok:


Walker, J. (1993). Preparing for Private Sector Participation in the Provision of Water Supply and Sanitation Services: WASH Technical Report No. 84. Office of Health, Bureau for Research and Development, USAID.

World Bank (1997). Selecting an Option for Private Sector Participation. Washington DC: The International Bank for Reconstruction and Development/ the World Bank.


Charles M. Rambo is an Associate Professor and Chairman at the Department of Extra Mural Studies, University of Nairobi, Kenya. His academic interests include financial management, Small and Medium Enterprises, small-scale farming and education financing. His previous work appears in journals such as Journal of Continuing, Open and Distance Education, International Journal of Disaster Management and Risk Reduction and the Fountain: Journal of Education Research, African Journal of Business and Management, African Journal of Business and Economics. He is reachable at the University of Nairobi through Telephone Number, +254 020 318 262; Mobile numbers +254 0721 276 663 or + 254 0733 711

255.Channel all correspondence regarding this article to Prof. Rambo.

Stephen Okelo Lucas is a Lecture in the School of Continuing and Distance Education. He is reachable through Telephone Number, +254 725747247.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 342 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1

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Nowadays, we have several sophisticated tools and various successful methods for software project management engineering. Despite this fact, the famous Cobb’s Paradox “We know why projects fail; we know how to prevent their failure—so why do they still fail?” is still relevant. In fact, many reports into the software projects management ask the same question! This may be the cause of unknown factors that can complicate the smooth running of a project management. So what if we are able to benefit from the company’s experience in project management, taking into account vagaries of the future? In this article we apply fuzzy logic to evaluate of a company's software management engineering process (SMEP). We define a fuzzy computational environment to study the efficiency of the SMEP. This consists of (1) a knowledge base which holds production rules used in the fuzzy evaluation of SMEP efficiency, (2) a SMEP database that stores the company's data concerning past software projects, (3) a fuzzy database that contains membership functions structures, and (4) an inference mechanism that fuzzifies managerial input vectors, and generate fuzzy inferences which are defuzzified before they are submitted to the project manager. A complete illustrative example, provided at the end of this article, will further show how fuzzy logic can be an effective decision aid when the complex and semi-structured nature of the problem inhibits the use of a rigorous analytical model.

JEL: C02, C63, C88, L86 KEYWORDS: Fuzzy Sets, Database, Project Management, Software Engineering, Fuzzification, Defuzzification


Even though software is hardly cited to customers, and rarely mentioned in sales brochures, and price books, software products are always there. Software is either implanted in the product R&D process, employed to assist sales people and better serve their customers, or utilized by information services to provide timely and accurate information to line managers. Obviously the organization does not have to be a software company to consider software management a high-level support activity, in order to assure the subsistence of other business functions. This paper shows how fuzzy logic can be effectively applied to the evaluation of a software engineering process. Based mostly on knowledge obtained from various experts in the field of software engineering, especially Boehm (1981), Christensen & Thayer (2002), and Pfleeger et al. (2005).

This article is organized into five sections, in addition to the introduction and the conclusion. The first section describes main activities of the Software Management Engineering Process (SMEP) and how they are evaluated. The second section defines fuzzy sets and linguistic variables used in the fuzzy analysis of SMEP efficiency. The third section is reserved for a detailed presentation of the fuzzy evaluation system.

The Fourth section presents the fuzzy database. The last section describes the inference mechanism.

The Smep Evaluation Process In evaluating a company's SMEP, the Software Engineering (SE) literature suggested that productivity, software quality, cost, and documentation should be measured (Boehm,1981; Dennis and al., 2014; Dennis, GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 343 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 2012; Fenton & Bieman, 2015; Grady & Caswell, 1987; Pfleeger et al., 2005; Christensen & Thayer, 2002).

The evaluation will aid top management in redefining their goals and in devising new strategies for the purpose of enhancing the software engineering process. The refinement of the software engineering process will have a direct impact on organizational performance, products, customers, and sales Christensen and Thayer, (2002). There is therefore no doubt that software is a strategic business issue.

Even though size-oriented metrics like quality, cost, productivity, and documentation, are utilized in the evaluation of a software engineering process. SE management also use other ways to evaluate and improve the company's software engineering process. However, most approaches adopted in SMEP evaluations are based on data collection, metrics computations, and data evaluation. Those approaches are thoroughly studied in Grady & Caswell (1987), Fenton (1991), and Jones (1991). Independently of the evaluation methodology, the complex and semi-structured nature of the evaluation task has often obstructed the effective development and application of rigorous analytical models. Senior software managers have often relied on their expertise and subjective judgment to perform the evaluation task.

According to several researches (Boehm, 1981; Fenton & Bieman, 2015; Grady & Caswell, 1987; Pfleeger et al., 2005; Christensen & Thayer, 2002), a company's software applications are organized into four types, namely, information system applications (IS), system applications (SA), real-time embedded systems (RT), and human-rated systems (HR). Independently of the type of the software application, the SMEP consists of three main activities; definition, development, and maintenance. These activities are conducted in all aspects of software engineering, regardless of application area, project size, or complexity. The definition phase is characterized by the joint effort exerted by both the software engineers and the users to identify key requirements of the system and the software. The definition activity is evaluated by examining customer contracts, project planning, and requirement analysis (Christensen & Thayer (2002)).

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