Measuring the Efficiency of Free and Open Source Software Projects Using Data Envelopment Analysis

2009 ◽  
pp. 2963-2977
Author(s):  
Stefan Koch
Keyword(s):  
Data Envelopment Analysis ◽  
Open Source ◽  
Optimization Method ◽  
Future Research ◽  
Data Envelopment ◽  
Software Projects ◽  
Data Set ◽  
Output Factors ◽  
Future Research Directions ◽  
Using Data

In this chapter, we propose for the first time a method to compare the efficiency of free and open source projects, based on the data envelopment analysis (DEA) methodology. DEA offers several advantages in this context, as it is a non-parametric optimization method without any need for the user to define any relations between different factors or a production function, can account for economies or diseconomies of scale, and is able to deal with multi-input, multi-output systems in which the factors have different scales. Using a data set of 43 large F/OS projects retrieved from SourceForge.net, we demonstrate the application of DEA, and show that DEA indeed is usable for comparing the efficiency of projects. We will also show additional analyses based on the results, exploring whether the inequality in work distribution within the projects, the licensing schem,e or the intended audience have an effect on their efficiency. As this is a first attempt at using this method for F/OS projects, several future research directions are possible. These include additional work on determining input and output factors, comparisons within application areas, and comparison to commercial or mixed-mode development projects.

Measuring the Efficiency of Free and Open Source Software Projects Using Data Envelopment Analysis

2007 ◽  
pp. 25-46
Author(s):  
Stefan Koch
Keyword(s):  
Data Envelopment Analysis ◽  
Open Source ◽  
Optimization Method ◽  
Future Research ◽  
Data Envelopment ◽  
Software Projects ◽  
Data Set ◽  
Output Factors ◽  
Future Research Directions ◽  
Using Data

In this chapter, we propose for the first time a method to compare the efficiency of free and open source projects, based on the data envelopment analysis (DEA) methodology. DEA offers several advantages in this context, as it is a non-parametric optimization method without any need for the user to define any relations between different factors or a production function, can account for economies or diseconomies of scale, and is able to deal with multi-input, multi-output systems in which the factors have different scales. Using a data set of 43 large F/OS projects retrieved from SourceForge.net, we demonstrate the application of DEA, and show that DEA indeed is usable for comparing the efficiency of projects. We will also show additional analyses based on the results, exploring whether the inequality in work distribution within the projects, the licensing scheme or the intended audience have an effect on their efficiency. As this is a first attempt at using this method for F/OS projects, several future research directions are possible. These include additional work on determining input and output factors, comparisons within application areas, and comparison to commercial or mixed-mode development projects.

Open Source Health Information Technology Projects

2012 ◽  
pp. 168-185
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Development Status ◽  
Future Research Directions ◽  
Using Data

This chapter discusses the growth of open source software projects in healthcare. It proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects, and it develops a systematic method for classifying projects based on their success potential. Using data from Sourceforge, an open source software development portal, we find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system, and programming language are significant predictors of an OSS project’s success categorization. We discuss research and application implications and suggest future research directions.

Open Source Health Information Technology Projects

2011 ◽  
pp. 308-325
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Development Status ◽  
Future Research Directions ◽  
Using Data

This chapter discusses the growth of open source software projects in healthcare. It proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects, and it develops a systematic method for classifying projects based on their success potential. Using data from Sourceforge, an open source software development portal, we find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system, and programming language are significant predictors of an OSS project’s success categorization. We discuss research and application implications and suggest future research directions.

A Classification Analysis of the Success of Open Source Health Information Technology Projects

2011 ◽  
pp. 256-273
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Information Technology Projects ◽  
Development Status ◽  
Future Research Directions

As the number of open source software (OSS) projects in healthcare grows rapidly, researchers are faced with the challenge of understanding and explaining the success of the open source phenomenon. This article proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects and it develops a systematic method for classifying projects based on their success potential. Drawing from economic theory, a novel proposition in the authors’ framework suggests that higher project-license restrictiveness will increase OSS adoption, because organizations will be more confident that the OSS project will remain open source in the future. Applying the framework to a sample of open source software projects in healthcare, the authors find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system and programming language are significant predictors of an OSS project’s success categorization. Application implications and future research directions are discussed.

An Application of the Data Envelopment Analysis Method to Evaluate the Performance of Academic Departments within A Higher Education Institution

2016 ◽  
pp. 71-87 ◽  
Author(s):  
Oanh Nguyen Hoang ◽  
Ngoc Nguyen Hong
Keyword(s):  
Data Envelopment Analysis ◽  
Research Output ◽  
Academic Staff ◽  
Education Institution ◽  
Academic Departments ◽  
Productivity Change ◽  
Data Envelopment ◽  
Analysis Method ◽  
Data Set ◽  
Using Data

This paper aims to evaluate the efficiency or the productivity of academic departments within a university using Data Envelopment Analysis. As an illustrative example, we investigate the performance of 57 departments of National Economics University (NEU) for three years, from 2013 to 2015. The data set consists of one input variable, which is the number of academic staff, and three output variables in which the number of research hours is considered as research output and the number of graduates and teaching load are defined as teaching outputs. Particularly, the output-oriented CCR, BCC, and SBM model under both the CRS and VRS assumptions are applied in order to determine accurate degrees of efficiency of individual departments and directions for performance improvement for less efficient ones. The output-oriented radial Malmquist DEA model is also employed to make a comparative analysis of the productivity change of the departments over the period. The results reveal some clear policy-making implications for departments to adjust their development plan in an appropriate way.

A Taxonomy of Glyph Placement Strategies for Multidimensional Data Visualization

2002 ◽  
Vol 1 (3-4) ◽  
pp. 194-210 ◽  
Author(s):  
Matthew O Ward
Keyword(s):  
Multidimensional Data ◽  
Future Research ◽  
Data Sets ◽  
Significant Information ◽  
Data Set ◽  
Multidimensional Data Visualization ◽  
Data Points ◽  
Future Research Directions ◽  
Visualization Of Data ◽  
Using Data

Glyphs are graphical entities that convey one or more data values via attributes such as shape, size, color, and position. They have been widely used in the visualization of data and information, and are especially well suited for displaying complex, multivariate data sets. The placement or layout of glyphs on a display can communicate significant information regarding the data values themselves as well as relationships between data points, and a wide assortment of placement strategies have been developed to date. Methods range from simply using data dimensions as positional attributes to basing placement on implicit or explicit structure within the data set. This paper presents an overview of multivariate glyphs, a list of issues regarding the layout of glyphs, and a comprehensive taxonomy of placement strategies to assist the visualization designer in selecting the technique most suitable to his or her data and task. Examples, strengths, weaknesses, and design considerations are given for each category of technique. We conclude with some general guidelines for selecting a placement strategy, along with a brief description of some of our future research directions.

scholarly journals A Review of Data Envelopment Analysis in Airline Efficiency: State of the Art and Prospects

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qiang Cui ◽  
Li-Ting Yu
Keyword(s):  
Data Envelopment Analysis ◽  
Rapid Development ◽  
Aviation Industry ◽  
Future Research ◽  
Data Envelopment ◽  
Social Concern ◽  
Network Dea ◽  
Advantages And Disadvantages ◽  
Dea Models ◽  
Future Research Directions

The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.

A Classification Analysis of the Success of Open Source Health Information Technology Projects

2009 ◽  
Vol 4 (4) ◽  
pp. 19-36 ◽  
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Information Technology Projects ◽  
Development Status ◽  
Future Research Directions

As the number of open source software (OSS) projects in healthcare grows rapidly, researchers are faced with the challenge of understanding and explaining the success of the open source phenomenon. This article proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects and it develops a systematic method for classifying projects based on their success potential. Drawing from economic theory, a novel proposition in the authors’ framework suggests that higher project-license restrictiveness will increase OSS adoption, because organizations will be more confident that the OSS project will remain open source in the future. Applying the framework to a sample of open source software projects in healthcare, the authors find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system and programming language are significant predictors of an OSS project’s success categorization. Application implications and future research directions are discussed.

Prediction Models and Techniques for Open Source Software Projects

2014 ◽  
Vol 5 (2) ◽  
pp. 1-39 ◽  
Author(s):  
M.M. Mahbubul Syeed ◽  
Imed Hammouda ◽  
Tarja Systä
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
Literature Survey ◽  
Future Research ◽  
Software Projects ◽  
Reliable Prediction ◽  
Research Directions ◽  
Fundamental Knowledge ◽  
Future Research Directions

Open Source Software (OSS) is currently a widely adopted approach to developing and distributing software. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This paper reports on a systematic literature survey aimed at the identification and structuring of research that offer prediction models and techniques in analyzing OSS projects. In this review, we systematically selected and reviewed 52 peer reviewed articles that were published between January, 2000 and March, 2013. The study outcome provides insight in what constitutes the main contributions of the field, identifies gaps and opportunities, and distills several important future research directions.

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