Characteristics of volunteering networks and activities

To characterize some features of voluntary networks among EMOs, these surveys explored the duration of voluntary activity, party selection behaviors at the time of monitoring activity, the adequacy of the volunteer training by the organization, their literacy in the T3 application for validating monitoring results, their first involvement with the organization, the main rationale (initial reason) for engaging in volunteer activity, the spread of voluntary membership within the social environment of the informant, the number of elections in which they have participated in monitoring activity (election-based), the belief in the effectiveness of voluntary activity, and their social bonds with other members and founders of the organization.

A significant result, which is compatible with the results on media use, is about the first encounter of the volunteer with the EMO (Where did you hear about this organization first?) Among 42 participants, 57.1 percent said that they initially heard of it through their social media networks, 21.4 percent in offline (everyday, not-computer-mediated) life, 11.9 percent said they heard of it on the Internet by coincidence, and only 2.3 percent from the conventional media. The results are explanatory in terms of how influential social media networks are in the spreading of civil society activity and participatory behavior. The percentages are also compatible with previous research on the relatively higher social media usage of the electorate of the western, coastal regions of Turkey in comparison to that of the voters of the central Anatolian regions.

In terms of how the voluntary membership of the EMOs spread, the data provides significant details. In total, 54.7 percent of volunteers stated that they led 1-6 persons from their social networks to become volunteers for the organization, 16.6 percent said more than 20, and 11.9 percent said 7-20 persons. A characteristic difference between VB and AV here is that the larger organization (VB) volunteers mostly recruited between 1-6, while the smaller organization (AV) volunteers functioned more effectively, with more of its respondents recruiting more than 20 people. (VB/AV: 18/5 for 1-6 persons, and 1/6 for more than 20persons). Thus, smaller groups seem to recruit greater numbers of members.

In terms of technological “literacy”, 41.3 percent of VB volunteers stated that they had been able to learn the use of the T3 application by their own effort, and 51.7 percent said they had learnt through VB trainings. The survey data show that 31 percent of VB volunteers stated that they came across a situation on the election day that required information outside the scope of what the VB trainings had provided them. Despite these partial structural limitations, 78.5 percent believed in the effectiveness of their voluntary activity for vote counting processes, while 21.4 percent were more pessimistic about their “added value” for elections processes and said they did not believe they had made a contribution.

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