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ABSTRACT
Research on network governance of local economic development has mostly focused on consequences of bonding and bridging networks to the neglect of antecedents of these networks. Framing collaboration ties between economic development organizations as social networks, this research presents theoretical arguments for emergence of various forms of bridging and bonding networks and illustrates the theoretical arguments by using collaboration network between economic development organizations engaged in economic development of the city of Priest River in Idaho. The results obtained by fitting exponential random graph models on the economic development collaboration network suggest that bridging networks develop when these organizations, faced with coordination problems in collaboration, engage in search of information or alignment of diverse interests. On the other hand, bonding networks emerge when these organizations, faced with cooperation problems in collaboration, engage in enforcing norms or in maintaining credibility of the commitment.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. The city of Priest River is in Bonner County, Idaho, at the crossroads of US Highway 2 and State Highway 57. It is located 7 miles east of Newport, Washington, and 23 miles west of Sandpoint, Idaho. The nearest urban center is Spokane, WA, 55 miles to the southwest.
2. Mail (paper) survey, postcard reminders, and phone calls were used as follow-ups to increase the response rate. The list of potential EDOs turned out to be fairly exhaustive as only 8 new EDOs outside the list were nominated by the respondent EDOs.
3. The network graphs used the spring embedding layout which brings nodes with the smallest path lengths closest to one another (Borgatti et al., Citation2002).
4. Those entities were Idaho Department of Environmental Quality, Idaho Department of Labor, Bonner County Board of Commissioners, and Bonner County Planning Commission.
5. ERGMs are the probability distribution from which the random graphs are simulated using the Markov Chain Monte Carlo (MCMC) procedure (Snijders et al., Citation2010). The simulations are then used to refine the initial estimation of the parameters (e. g., activity spread) by comparing the value of the statistics computed on the simulated networks with those observed. The parameters are determined using the maximum likelihood estimation that maximize the probability of the observed network under the specified ERGM.
6. These models also included simple two-path structure but was dropped from the final model because it not only did not produce statistically significant parameter and but also impaired the model convergence. The effect of organizational type as an attribute on formation of collaboration ties was also tried, but the model did not converge.
7. The t-ratios for the GOF test were < .30, sufficient for achieving GOF for the estimated models (Robins et al., Citation2007). The GOF t-ratio is the ratio of difference between observed and simulated average values of graph statistics, and the standard deviation of the simulated values.