The Use of Repeatable Components in Hybrid Models to Enhance Software Project Management Success

Abstract

The management of software project development requires a dynamic and reactive environment to meet shorter time-to-market demands to address competition efficently in the software industry. This scenario requires the use of effective and robust methodologieswhere opportunities are not lost due to delays and failures in timely software project deliveries.The Agile Manifesto in 2001 which introduced 4 values and 12 principles was designed to develop and manage software projects in a more suitable and effective way to improvethe success rates of software projects. But, increase in overall success rates are still not significant with failure rates remaining plauteaued at about 30% over the last 10 years.  Hybrids methodologies seem to have worked better as agile hybrid management methodshave shown more promise when compared to pure agile methods with an overall success rate increase of 16%. There is evidence too that by combining agile methodologies with traditional methodologies, there would be a further increase in success rates. Whilst many hybrid methodologies have been suggested and researched, the gaps in the literature review reveal there is a lack of hybrid models that have been empirically developed and studied as second order components. To build a robust hybrid model, it is important to gather the relevant information and careful consuideration must be given to the design of the questionnaireto fit second order components and models must incorporate and provide for the use repeatable ways to test models once the data is collected.This paper presents a review of the current gaps in hybrid methodologies and proposes a questionnaire design that supports the research methodology and empirical study to be undertaken with second order components (Constructs).Further it looks at the design approach in questionnaires which incorporates the use of repeatable constructs and the measures used and emphasizes this as an important ingredient for developing and testing hybrid models in research studies.