## 3 Results and Discussion
### Statistical Analysis  
With the purpose of further analyzing the practices along the demographic data, a statistical analysis was performed. Two independent variables were defined: (i) _profile_ of the respondent (technical versus non-technical); and (ii) _size_ of the company (medium/large versus micro/small). Table 4 shows the significance of the Pearson's chi-squared statistical test, and the odds ratio (OR) statistic and 95% confidence interval for each combination of independent and dependent variables. Numbers in bold are used to highlight statistically significant outcomes. Column \"Set\" refers to the specific set of items: sets number 1 and 2 are shown in Table 1 (18 questions); and set number 3 is presented in Table 2 (16 questions). The difference between sets 1 and 2 is that the former refers to whether the respondents apply the practices at work, whereas the latter is focused on whether the respondents believe that these practices can help companies be more sustainable. In addition, the answers in sets 2 and 3 are not dichotomic, so they had to be binarized before performing this statistical analysis. \"Strongly disagree\", \"Disagree\", and \"Neither agree nor disagree\" were thus mapped to \"No\", and \"Agree\" and \"Strongly agree\" were matched to \"Yes\". Participants with a technical role were less inclined (OR = 0.398, 95% CI 0.171-0.924) to apply principles or requirements for usability and user experience in the development of products than those with a non-technical role. The application of principles or requirements for accessibility in the development of products also depended on whether those who did so were technical staff (OR = 0.324, 95% CI 0.138-0.763) in comparison with non-technical staff. The professional category \"technical\" reduced the probability (OR = 0.357, 95% CI 0.152-0.839) of applying agile and lean methodologies in comparison with managers, project managers, and systems managers. These results are coherent with the idea that the latter are usually more concerned about software requirements and development methodologies than the former. Likewise, participants with a technical role were less inclined to apply cloud computing and virtualization services (OR = 0.419, 95% CI 0.179-0.984) and big data and data mining (OR = 0.089, 95% CI 0.019-0.421) in their companies than those with a non-technical role. Again,this could be explained by nature of the tasks assigned to each type of employee in the company, since non-technical roles are those who make the decisions about the application of those practices. Regarding the size of the company, medium and large companies (MLCs) were less inclined to apply requirements reuse (OR = 0.328, 95% CI 0.127-0.843) and specific sustainability requirements (OR = 0.159, 95% CI 0.033-0.754) than micro and small companies (MSCs). This might seem surprising, since one could expect larger and more resourceful companies to excel in this regard. However, MSCs in some cases prove to be less congested than MLCs, and to be more dynamic and innovative. On the contrary, MLCs increased the probability of applying testing and continuous integration (OR = 2.866, 95% CI 1.142-7.193) compared to MSCs. The implementation of testing and continuous integration, and other DevOps practices, is often based on full-scale adoption; this seems to diverge from the philosophy of smaller companies, even though small teams can reap great benefits in adopting these practices. For example, large organizations usually have applications that are difficult to move to continuous delivery, yet they cannot be ignored in the process of continuous delivery adoption because many of them are very important for the success of the company. Employees of MLCs were also less inclined to apply the Internet of Things (OR = 0.219, 95% CI 0.058-0.830) than those of MSCs. This could be due to the same reasons that were argued in relation to the requirements reuse and specific sustainability requirements.