Abstract
The article identifies the relationship among different agile software development approaches such as response extensiveness, response efficiency, team autonomy, team diversity, and software functionality that software teams face difficult challenges in associating and achieving the right balance between the two agility dimensions. This research strategy, in terms of quantity, is descriptive and correlational. Statistical analysis of the data was carried out, using SmartPLS 3.0. Statistical population, consist of employees of software industries in Bangladesh, who were engaged in 2017 and their total number is about 100 people. The data show that the response extensiveness, response efficiency, team autonomy, team diversity, and software functionality have impact on software development agility and software development performance.
Specifications table
| Subject area | Software Engineering |
| More specific subject area | Software development agility and software development performance. |
| Type of data | Table, figure |
| How data was acquired | Questionnaire analysis was adopted. SmartPLS 3.0 was used to develop the model. |
| Data format | Raw, analyze, descriptive, statistical |
| Experimental factors | Agile software development approaches, which affirm sense-and-respond, self-organization and cross-functional teams were considered to determine the software development agility. |
| Experimental features | The relationship among response extensiveness, response efficiency, team autonomy, team diversity, and software functionality were determined |
| Data source location | Dhaka, Bangladesh. |
| Data accessibility | Data is with this article |
| Related research article | Lee, G., & Xia, W. (2010). Toward agile: an integrated analysis of quantitative and qualitative field data on software development agility. Mis Quarterly, 34(1), 87–114. |
Value of the data
|
1. Data
The dataset of this article provides the information on the recent agile software development approaches. Table 1 shows the demographic details of employers of software companies.
Table 1.
Demographic characteristic of employers of software companies.
| Parameter | Characteristics | Number (Percentage) |
|---|---|---|
| Organizations | Software Development &Health | 57.4 |
| Software Development | 25 | |
| Banking/Finance/Insurance | 2.9 | |
| Consulting | 5.9 | |
| Telecommunications | 7.4 | |
| Government | 1.5 | |
| Respondents | Quality Assurance | 4.4 |
| Technical Project Manager | 2.9 | |
| Lead Test Engineer | 1.5 | |
| Software Engineer | 1.5 | |
| Sr. Software Engineer (android) | 10.3 | |
| Sr. Software Engineer | 8.8 | |
| Lead Software Engineer | 7.4 | |
| Senior Software Engineer (media) | 8.8 | |
| Head of Design | 1.5 | |
| Technology Lead | 5.9 | |
| Project Coordinator | 1.5 | |
| System Analyst | 1.5 | |
| Technical Writer | 5.9 | |
| Software Writer | 5.9 | |
| Software Developer | 1.5 | |
| Graphics Designer | 2.9 | |
| Senior Developer | 2.9 | |
| Developer | 13.2 | |
| Junior Developer | 2.9 | |
| Tester | 1.5 | |
| Work Experience | >6 | 13.2 |
| >1 | 17.6 | |
| 4–6 | 32.4 | |
| 1–3 | 36.8 | |
| Company Size | <5 | 7.4 |
| 21–50 | 17.6 | |
| 51–120 | 75 | |
| Budget | <10000 | 13.6 |
| 10000–50000 | 0 | |
| 50000–100000 | 22.7 | |
| 100000–500000 | 59.1 | |
| >500000 | 4.5 | |
| Project Duration | <3 months | 26.5 |
| 3–5 months | 45.6 | |
| >5 months | 26.5 |
2. Experimental design, materials, and methods
20 software firms were chosen from Dhaka, Bangladesh. 160 questionnaires were distributed and 100 usable questionnaires were returned for analysis. In this study, data were gathered from all kind of software firms (small, medium, large) as well as a questionnaire [1] including the demographic data (e.g. qualification, experience). Then, the collected data were collected, coded and entered into SmartPLs 3.0. Data analysis was performed, using SPSS-21. Data were analyzed; applying descriptive and statistical tests including partial least squares approach.
2.1. Measurement model
Table 2 shows that composite reliability and the AVE of all variables are higher than 0.7 and 0.5 [2], [3], [4] respectively, we can state that both criterion accept our five variables.
Table 2.
Composite Reliability and Average Variance Extracted (AVE) of variables.
| Composite Reliability | AVE | |
|---|---|---|
| Software team autonomy (AUT) | 0.901 | 0.695 |
| Software team diversity (DIV) | 0.908 | 0.712 |
| Software Team Response Efficiency (EFI) | 0.907 | 0.625 |
| Software Team Response Extensiveness (EXT) | 0.742 | 0.608 |
| Software functionality(FUN) | 0.869 | 0.625 |
Table 3 shows that the square root of the average variance where all the values on the diagonals are greater than the corresponding row and columns. It indicates that the measures are discriminant.
Table 3.
Square root of the average variance.
| AUT | DIV | EFI | EXT | FUN | |
|---|---|---|---|---|---|
| AUT | 0.834 | ||||
| DIV | 0.476 | 0.844 | |||
| EFI | −0.254 | −0.084 | 0.790 | ||
| EXT | 0.150 | 0.277 | 0.096 | 0.780 | |
| FUN | 0.170 | 0.303 | −0.211 | 0.554 | 0.791 |
Bold indicates to highlight that diagonal values are higher than other values.
2.2. Structural model
Table 4 presents that in the structural model the significance of the relations among variables is measured by the path coefficient. We found that Software team autonomy (AUT) (β = −0.254 and p < 0.05) significantly influences Software Team Response Efficiency (EFI), Software team diversity (DIV) (β = 0.277 and p < 0.1) significantly influences Software Team Response Extensiveness (EXT). The relationship between EFI (β = −0.267 and p < 0.05) and EXT (β = 0.580 and p < 0.05) also have significantly influence on Software functionality (FUN) (see Fig. 1).
Table 4.
Path coefficient of the variables.
| Original Sample (O) | T Statistics (|O/STDEV|) | P Values | Result | |
|---|---|---|---|---|
| AUT → EFI | −0.254 | 1.737 | 0.083 | Supported* |
| DIV → EXT | 0.277 | 2.224 | 0.027 | Supported** |
| EFI → FUN | −0.267 | 1.827 | 0.068 | Supported* |
| EXT → FUN | 0.580 | 6.748 | 0.000 | Supported** |
Note: PLS estimation results (n = 100, **p < 0.05, *p < 0.1).
Bold indicates to highlight the strongly supported results.
Fig. 1.
Pictorial representation of Table 4.
The effect was calculated by following Cohen's effect size estimation [5]. Effect size is considered as small, medium and large if the values are 0.02, 0.15 and 0.35 respectively. Next this study also assessed effect sizes (f2). Besides the path coefficient also the effect size can be evaluated to control for the respective impact of different variables in one model. In our case, Table 5 shows that AUT and DIV have small effect on EFI and EXT. For the dependent variable FUN, EFI has small effect comparatively to EXT.
Table 5.
Effect size.
| Effect Size | Remark | |
|---|---|---|
| AUT → EFI | 0.069 | Small |
| DIV → EXT | 0.083 | Small |
| EFI → FUN | 0.114 | Small |
| EXT → FUN | 0.535 | Large |
Funding sources
There was no funding source for this work.
Footnotes
Transparency document associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2019.103741.
Contributor Information
Farzana Sadia, Email: sadia.swe@diu.edu.bd.
Imran Mahmud, Email: imranmahmud@usm.my.
Eva Dhar, Email: eva-482@diu.edu.bd.
Nusrat Jahan, Email: nusrat.swe@diu.edu.bd.
Syeda Sumbul Hossain, Email: syeda.swe@diu.edu.bd.
A.K.M. Zaidi Satter, Email: sattar10@daffodilvarsity.edu.bd.
Transparency document
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References
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