| Literature DB >> 23861761 |
Paolo Parigi1, Bogdan State, Diana Dakhlallah, Rense Corten, Karen Cook.
Abstract
In this paper we explore two contrasting perspectives on individuals' participation in associations. On the one hand, some have considered participation the byproduct of pre-existing friendship ties--the more friends one already has in the association, the more likely he or she is to participate. On the other hand, some have considered participation to be driven by the association's capacity to form new identities--the more new friends one meets in the association, the more likely he or she is to participate. We use detailed temporal data from an online association to adjudicate between these two mechanisms and explore their interplay. Our results show a significant impact of new friendship ties on participation, compared to a negligible impact of pre-existing friends, defined here as ties to other members formed outside of the organization's context. We relate this finding to the sociological literature on participation and we explore its implications in the discussion.Entities:
Mesh:
Year: 2013 PMID: 23861761 PMCID: PMC3701679 DOI: 10.1371/journal.pone.0067388
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1The information members enter after a friendship tie is reported on the website.
Figure 2Ratio of ties by size of local chapters (top panel) and by (log) number of members (bottom panel).
Each dot represents the total proportion of the two types of ties in a metropolitan area of the United States (CSA) as of December 2010 (N = 83.). We excluded CSAs where more than 90% of the members had no ties. In the top panel diamonds show the group means with 95% confidence intervals (One Way ANOVA, F ratio = 21.22, ). The bottom panel shows a quadratic fit to the data with 95% confidence interval. .
Figure 3Total logins by number of associational ties.
PVAR Estimates.
| Dependent Variable | (Unlagged) | Ln (logins): L | Degree: D | % Assoc. Ties: P | ||||||
| Lag (months) | Coef. | (S.E.) | t-statistic | Coef. | (S.E.) | t-statistic | Coef. | (S.E.) | t-statistic | |
| Ln (logins) | One | 0.475*** | (0.007) | 69.659 | 0.095*** | (0.011) | 8.448 | 0.531*** | (0.043) | 12.282 |
| Two | 0.075*** | (0.006) | 13.296 | 0.002 | (0.007) | 0.359 | −0.066* | (0.033) | −2.028 | |
| Three | 0.055*** | (0.005) | 11.998 | 0.004 | (0.006) | 0.620 | 0.002 | (0.025) | 0.085 | |
| Degree | One | 0.007 | (0.015) | 0.454 | 1.010*** | (0.062) | 16.231 | −0.034 | (0.036) | −0.946 |
| Two | −0.034** | (0.013) | −2.605 | −0.094 | (0.051) | −1.845 | 0.014 | (0.036) | 0.376 | |
| Three | 0.016 | (0.008) | 1.923 | 0.020 | (0.034) | 0.592 | 0.023 | (0.024) | 0.967 | |
| % Assoc. Ties | One | 0.009*** | (0.002) | 6.295 | −0.002 | (0.002) | −1.320 | 0.878*** | (0.010) | 86.283 |
| Two | −0.003** | (0.001) | −2.670 | −0.001 | (0.002) | −0.398 | −0.008 | (0.007) | −1.051 | |
| Three | 0.000 | (0.001) | 0.533 | −0.001 | (0.001) | −0.724 | −0.010* | (0.004) | −2.442 |
Source: CouchSurfing US dataset. Legend: *, ** , ***. N = 67,183.
Each variable is time-demeaned to take into account any secular trends. We controlled for heteroskedasticity by dividing each variable by its time dependent standard deviation. We addressed autocorrelation of individual observations by subtracting the forward mean, which corresponds to the mean of all future observations for each individual (Helmert transformation). The reported coefficients are -scores.
Figure 4Graphical representation of the PVAR model with a three-lag period.
L = number of logins (logs); D = number of ties; P = proportion of associational ties. Arrows represent statistically significant effects (at the 95% confidence level or more). Magnitude of the effects reported in Table 1.