Full dissertation is available via NSUWorks and ProQuest Gradworks digital repositories.

Introduction

Despite recent advances in information technologies and successes in software engineering, the problem of failures of software development projects is extremely significant (Figure 1), as it costs various commercial firms and government organizations astronomical amounts in direct and indirect damages as well as significant lost time and opportunities [4, 10, 11, 22].

Solving this problem requires efforts for identification and understanding of factors of success and their interrelationships. The problem of failures in commercial and governmental software products and information systems (IS) equally exists in an increasingly popular and important domain of FLOSS [8, 18]. Considering the exponential rate of FLOSS adoption go to this site by the business world as well as the fact that FLOSS powers practically the whole Internet and many information systems of critical infrastructure, it is clear that research on FLOSS success factors is not only extremely useful and vitally important, but also that it offers a significant potential in terms of attracting funding support from commercial, non-profit and government organizations.


This thesis of mine is supported by the explosive growth of research community’s interest [5, 19], as evidenced by subsequent growth and abundance of research publications in various peer-reviewed outlets [6, 17]. My analysis of FLOSS research literature identified various essential components and areas of interest. However, my attention was especially drawn to two important aspects within FLOSS ecosystem: governance [3, 7, 12–15, 21] and organizational sponsorship [3, 9, 15, 16, 20, 23].

Dissertation Research Study

Diss_Figure2
The goal of my dissertation research [2] was to study and empirically investigate the role and significance of governance and organizational sponsorship in success of FLOSS development, which included determining the validity of the proposed model of FLOSS success (Figure 2). While initially I planned to use several data sources, due to various data quality urgent essay writing service issues, I decided to collect required data from a single, but world’s largest, FLOSS repository—Source-Forge.net. Since data quality issues forced me to use very large samples, I was exposed to performing data collection, preparation (validation, cleaning) and analysis on a relatively large scale (big data). I have written software in R statistical and programming language to automate all those phases as much as possible. Moreover, by extensively using open source software, such as R, RMarkdown, knitr, pandoc, LaTeX, make, Git, I made maximum efforts to design my dissertation research and the created data analysis software with reproducible research principles in mind [1].

Prior to using SEM (path analysis) as the dissertation study’s main quantitative method, I performed exploratory data analysis (EDA) as well as exploratory and confirmatory factor analyses (EFA/CFA). Based on the intermediate results, then SEM (path analysis) was performed for three alternative models (direct effects, mediation and moderation). The results were then compared across alternative models, after which I assessed construct validity and reliability as well as validated the proposed model by analyzing hypotheses testing results. Overall, all alternative models showed average numbers in goodness-of-fit (GoF) index, with a slight advantage for direct effects and moderation models, and average and quite similar numbers for predictive power (R-squared). In terms of validity and reliability, the results are also mixed, with validity and reliability criteria being satisfied for 50 to 80 percent, depending on constructs. Finally, hypotheses testing (structural model) resulted in their partial support, with most tests being statistically significant.

While these results might not sound impressive, they are, in fact, quite good, taking into account limited scope of data coverage (of potential indicators) and extremely low quality of the data set. What, in my opinion, is more important is that my dissertation proved that the proposed approach, used methods and created software can be used to perform reproducible quantitative research of good quality on FLOSS and, potentially, in many other knowledge domains, related to complex socio-technical systems. Additionally, mixed results were expected due to lack of relevant theory, research streams disconnect and exploratory nature of the study. However, this dissertation can be seen as an important step toward establishing theoretical foundation for the research on FLOSS and its success. Moreover, the study also contributes to establishing practical foundation by providing a blueprint of applying reproducible research paradigm to FLOSS and IS research.

Postgraduate Dissertation-related Plans

Since my recent graduation, I was busy preparing my documents and performing a job search for a post-doctoral and junior faculty positions. While I do not have an opportunity to perform any meaningful research at the present time, I am performing preliminary planning for converting my dissertation into several research papers, publishable in peer reviewed journals, or, alternatively, a book chapter. In addition, I am planning to convert my dissertation research software into two R packages: 1) FLOSS repositories data collection and 2) FLOSS data analysis, using SEM.

References

[1] Aleksandr L. Blekh. Research software for quantitative analysis of open source software ecosystem using structural equation modeling, version 1.0.2. [software]. 2014. doi:10.5281/zenodo.13143. URL https://github.com/abnova/diss-floss-official.

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