Adoption of Open-Source Software in Corporate Sector
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The Role of Indian IT Service Providers in Adoption of Open-Source Software in Corporate Sector

Subject Area: Education / Adult Learning / Quantitative Statistics

Modified: 2nd September 2025

The software industry encompasses a wide range of entities with diverse needs for processes, methods, and tools to achieve their goals. Open-Source Software (OSS) has gained significant attention due to its rapid development and adoption in both academic and professional sectors. OSS stands out for its royalty-free licensing, public access to code, and the ability to modify and redistribute software, offering benefits like cost reduction, flexibility, and quality assurance. The increasing adoption of OSS has raised questions about its success, impact on system security, and overall software quality. Despite its advantages, OSS adoption presents challenges, particularly in developing countries, where it can help reduce IT costs and foster business opportunities. However, its long-term viability and sustainability, along with its impact on the software industry, need further exploration, especially in terms of government strategies, business models, and its integration into global markets.

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Table Of Content

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S. No

Contents

Page

1

Respondents profile

2

2

Reliability analysis for OSS factors

3

3

illustrates the relationship between reliability and the extent of OSS adoption

6

4

Convergent validity statistics

7

5

Discriminant validity of major constructs

7

6

Evaluation of Structural Models

7

Respondents profile

Position

Figure 1 shows that most respondents were from the Managerial level (53.5%), including roles like Project Manager and Business Manager. The next largest group was non-managerial level (42.1%), including roles such as Developer and Tester. Only 0.8% held Top Management roles, such as CXO or Director. Additionally, 3.5% identified their roles as “Others,” including Technology Architect (11), Technology Lead (4), Associate General Manager (1), and Consultant (1).

Descriptive statistics

Table 1 shows the descriptive statistics for OSS adoption and factors. The IT outsourcing factor has the highest ranking, with a mean of 3.959 and a standard deviation of 0.627, ranging from 1 to 5. In contrast, software cost ranked lowest, with a mean of 2.605 and a standard deviation of 0.622, also ranging from 1 to 5.

Construct 

N 

Mean 

Std. Deviation 

Maximum 

Minimum 

Reliability (RELIB) 

482 

3.080 

.353 

2.333 

5 

License concern (LICNSE) 

482 

2.755 

.593 

1 

3 

Legal concern (LEGAL) 

482 

3.224 

.945 

1 

5 

Software cost (SWCST) 

482 

2.605 

.622 

1 

3.333 

Management support (MGTSUP) 

482 

2.571 

.731 

1 

5 

IT Outsourcing (ITOUTG) 

482 

3.959 

.627 

1 

5 

OSS Support availability (OSSSUP) 

482 

3.216 

.652 

1.667 

5 

IT Service provider (SWVEN) 

482 

3.202 

.658 

1 

5 

Client Organization Size (SIZE) 

482 

3.614 

.952 

1 

5 

OSS Adoption at Client (OSSADP) 

482 

3.041 

1.159 

1 

5 

Reliability analysis

The study conducted reliability analysis using Cronbach’s alpha for each multi-item scale. Table 2 shows the results, with six out of eight factors having alpha values greater than 0.6, meeting the reliability standard. Cronbach’s alpha was not applicable for two factors (Legal concern and IT Outsourcing) as they each had a single item. Overall, the reliability coefficients ranged from 0.61 to 0.8079, indicating acceptable reliability. 

Factors 

Initial Number of Items 

Number of Items carried Forward to the Analysis 

Mean 

Variance 

Cronbach’s alpha 

Reliability (RELIB) 

5 

3 

3.080 

.006 

.677 

License concern (LICNSE)^ 

3 

2 

2.755 

.006 

.784 

Legal concern (LEGAL) ^ 

1* 

NA 

NA 

NA 

Software cost (SWCST) 

6 

3 

2.605 

.076 

.714 

Management support (MGTSUP) 

6 

4 

2.571 

.631 

.721 

IT Outsourcing (ITOUTG) 

1 

1* 

NA 

NA 

NA 

OSS Support availability (OSSSUP) 

5 

3 

3.216 

.020 

.606 

IT Service provider (SWVEN) 

5 

3 

3.202 

.001 

.798 

^ License and Legal concern were determined as single factor in Literature review. 

* Not applicable as single item 

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Table 2: Reliability analysis for OSS factors

Hypotheses Testing 

Impact of Reliability on OSS adoption 

Hypothesis 1: IT Outsourcing Organizations serviced by Indian IT service providers that perceives OSS to be reliable will significantly adopt OSS. 

The impact of Reliability on OSS adoption was assessed using multiple regression tests and results are presented in table 3 below. 

Model 

Unstandardized Coefficients 

Adjusted R Square 

F Change 

t 

Sig. 

Beta 

Std. Error 

1 

(Constant) 

1.441 

.460 

.023 

12.276 

3.135 

.002** 

Reliability 

.520 

.148 

3.504 

.001** 

2 

(Constant) 

1.833 

.486 

.032 

5.610 

3.767 

.000 

Reliability 

.545 

.148 

3.681 

.000*** 

Client Organization Size 

-.130 

.055 

-2.368 

.018* 

Dependent variable: What is the extent of OSS adoption at client? 
Significance: *p < .05, **p < .01, ***p < .001  

Table 3: illustrates the relationship between reliability and the extent of OSS adoption

Table 3 of the multiple regression analysis shows that the beta coefficient for OSS adoption on Reliability is highly significant (beta=0.520, t=3.504, p=0.001). When Client Organization Size is added as a control variable, its effect is also significant (beta=-0.130, t=-2.368, p=0.018). With an R-square of 0.032, the results reject the null hypothesis and support the alternative hypothesis, indicating a significant association between Reliability and OSS adoption.

Convergent validity

Convergent validity ensures that items representing a construct share a high proportion of variance. Individual item reliability was assessed by examining the weight and loading of each item on its construct. All indicator weights were found to be significant, supporting the inclusion of all indicators.

As per Table 4, Composite Reliability (CR) and Reliability statistics were greater than the recommended cut-off 0.70. The Average Variance Extracted (AVE) was above the 0.50 threshold with the exception of OSS Support Availability (OSSSUP), which was 0.460. However, since OSSSUP showed minimal correlation with other factors, and its Cronbach’s alpha (0.606) and Composite Reliability (0.706) were adequate, it was retained. These results support convergent validity.

Construct 

Average Variance Extracted (AVE) 

Composite Reliability (CR) 

Reliability (RELIB) 

.617 

.820 

License concern (LICNSE) 

.828 

.906 

Legal concern (LEGAL) 

1 

1 

Software cost (SWCST) 

.639 

.826 

Management support (MGTSUP) 

.609 

.854 

IT Outsourcing (ITOUTG) 

1 

1 

OSS Support availability (OSSSUP) 

.458 

.709 

IT Service provider (SWVEN) 

.714 

.882 

Client Organization Size (SIZE) 

1 

1 

OSS Adoption at Client (OSSADP) 

1 

1 

Table 4: Convergent validity statistics

Discriminant validity

Discriminant validity was assessed by ensuring the square root of the Average Variance Extracted (AVE) was higher than all inter-construct correlations, as shown in Table 5. Additionally, cross-loadings were examined, with results in Appendix C-4 confirming that each item loads higher on its intended construct than on others. Both tests indicate the model meets the required level of discriminant validity.

 

RELIB 

LICNSE 

LEGAL 

SWCST 

MGTSPT 

ITOUTG 

OSSSPT 

SOFTVEN 

OSSADP 

SIZE 

RELIB 

.785 

 

 

 

 

 

 

 

 

 

LICNSE 

-.106 

.910 

 

 

 

 

 

 

 

 

LEGAL 

-.028 

-.008 

1 

 

 

 

 

 

 

 

SWCST 

-.223 

.585 

.007 

.799 

 

 

 

 

 

 

MGTSPT 

.140 

-.154 

-.492 

-.272 

.780 

 

 

 

 

 

ITOUTG 

.016 

.002 

.075 

-.038 

-.039 

1 

 

 

 

 

OSSSPT 

.227 

-.379 

-.185 

-.596 

.372 

.068 

.677 

 

 

 

SOFTVEN 

.106 

-.107 

-.114 

-.089 

.181 

-.005 

.187 

.845 

 

 

OSSADP 

.159 

-.136 

-.259 

-.22 

.376 

.042 

.247 

.227 

1 

 

SIZE 

.069 

-.038 

.025 

-.06 

-.038 

.032 

.037 

-.038 

-.095 

1 

The diagonal values representing the square root of the Average Variance Extracted (AVE)

Table 5: Discriminant validity of major constructs

Assessment of the Structural model

A structural model was used to examine the linear regression effects of endogenous constructs, assessing path coefficients (β), path significance (p-value), and explained variance (R²) through PLS. A bootstrapping procedure of 5000 resamples with a one-tailed t-test for significance was implemented in the analysis. The theoretical, control, and full models were estimated. Results showed that the R² for OSS adoption was significant across all models. The full model explained significantly more variance than the theoretical model at 95.6% variance explained with highly significant F-statistics (p < 0.001), signifying that the model explains OSS adoption better.

 

Hypotheses 

Full Model 

Theoretical Model 

Control Model 

H1 

Reliability è OSS Adoption 

.083* 

.076* 

 

H2a 

License concern è OSS Adoption 

-.009 

-.008 

 

H2b 

Legal concern è OSS Adoption 

-.120* 

-.119* 

 

H3 

Software cost è OSS Adoption 

-.108* 

-.104* 

 

H4 

Management support è OSS Adoption 

.240*** 

.246*** 

 

H5 

IT Outsourcing è OSS Adoption 

.058 

.055 

 

H6 

OSS Support availability è OSS Adoption 

.086* 

.082* 

 

H7 

Software Vendor è OSS Adoption 

.143** 

.147** 

 

H8 

OSS Support availability è Software cost 

-.596*** 

-.596*** 

 

C1 

Size of the organization è OSS Adoption 

-.092* 

 

-.095** 

 

R2  OSS Adoption 

.208 

.200 

.009 

 

R2 Software Cost 

.355 

.355 

 

 

F 

13.862 

14.871 

4.338 

 

Significance 

.000*** 

.000*** 

.038* 

 

Increase in R2 OSS Adoption 

 

3.85% 

95.67% 

è Has impact on 

Significance: *p < .05, **p < .01, ***p < .001  

Table 6: Evaluation of Structural Models

The results for the structural model (displayed in Figure 4.9) indicated the degree of OSS adoption (our dependent variable) had an R² value of 20.8%. In PLS-SEM, models are assessed on their contribution to explaining variance in dependent variables; an R² value of 0.20 or above is high in the context of customer behaviour. While this may be considered weak in marketing studies, the current model, focusing on OSS adoption in IT outsourcing, successfully explains a significant portion of the variance, aligning with the definition of consumer behaviour as a study of decision-making processes.

Conclusion:

This study highlights the significant role of Reliability in the adoption of Open-Source Software (OSS) in Indian IT outsourcing organizations. It also reveals that Client Organization Size influences OSS adoption. The reliability analysis confirms that the constructs used in the study are consistent, and both convergent and discriminant validity were established. The full model provided the best explanation of OSS adoption, with a significant increase in explained variance. Although the R² value for OSS adoption is 20.8%, it is considered a meaningful result in consumer behaviour research. Overall, the findings emphasize the importance of considering both organizational and software-related factors in OSS adoption strategies and suggest further research into the long-term sustainability of OSS in the corporate sector.

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