Session 4: Network Theory

Session 4: Network Theory

Dongil Jang

 

 [1] How social structure assists or impedes economic performance? Uzzi (1996) demonstrates the processes and consequences of how social networks affect economic performance. Processes are described with ethnographies of 23 apparel firm. He founds that Trust, Fine-grained information transfer, and Joint problem-solving arrangements are the unique function of embedded ties compared to the arm’s length relationships. And consequences are analyzed with logit analysis on the survival likelihood of contractor firms in New York’s apparel industry[1]. The result reveals that firms with embedded ties are more stable than controls. However, second-order network coupling shows a complicated outcome which represents the threshold effect. # This paper integrates the qualitative and quantitative analysis and derives an overview of a network perspective. However, there are some analytic limitations. First, whether the exit is a proper indicator for the network’s effect on performance is questionable. A more consistent approach would be available with sales and innovativeness data. Second, if the author’s purpose is to observe the enhancement of stability, I would like to suggest that the selection of the period would be more strategic. For example, the period with unexpected exogenous shock would reveal the network’s effect while controlling the stableness of order. Third, the relationship between contractor and manufacturers is too abstract in quantitative research. Although the distinction between contractors and manufacturers is clear, I am not sure what does each of these groups and tie between them represent in network perspective due to their ‘within’ differences (Fig. 1 and 2). Nevertheless, the heterogeneity of network forms itself seems interesting and urges future researches (Uzzi 1996: 695).

 

[2] How interorganizational relationships effect organizational learning? At first glance, Powell, Koput, & Smith-Doerr (1996) seems like a paper on innovation. However, its analysis is focused on network approach to organizational learning. With a sample of dedicated biotechnology firms in the years 1990-1994, authors use a dynamic two-way fixed-effects model to demonstrate the independent force of network. The results show that network factors have a significant effect rather than individual attributes like age and size. # This paper specifies the cycles of learning in biotechnology. Nonetheless, there is a gap between analysis and argument. First, it does not approach the proposed idea directly. Authors’ main opinion is that the locus of innovation will be found in networks of learning, rather than an individual firm. However, what is shown is that networks’ effect on networks. Although Table 6 supplements the results, it is not clearly shown that network enhances firm performances. Second, the meaning of results also needs to be reconsidered. Results show that firms with more networks get more ties. Authors argue that this process is the result of reciprocal learning. But regarding industrial-level changes[2], the existence of the Matthew effect (Merton 1968; Podolny 1993) could also be an answer[3][4]. To examine this opinion, controlling the firm’s internal R&D capacity would be a way to separate the effect of capacity (Cohen & Levinthal 1990) and status (Podolny 1993).

 

[4] How does the network structure with a threshold affects the diffusion process? Centola & Macy (2007) examines the scope condition of the strength of the long tie (Granovetter 1973) and reveals the structural weakness of long ties. By integrating threshold model (Granovetter 1978) to “small world” model (Watts and Strogatz 1998), they figure out that adding too much long bridges would decrease the width of bridges which would lead to a slowdown or even prevention of cascades. # Authors draw an insightful finding through a slight adjustment to the famous “small world” model. In terms of the macro organizational phenomena, this model would be a solid network base to explain the institutional changes in the field (Meyer & Rowan 1977; DiMaggio & Powell 1983) and the diversification of organizational forms (Hannan & Freeman 1977). In the former, isomorphism could be interpreted as a successful contagion. In the latter, organizational inertia could be accounted for the threshold of diffusion in the network perspective. Also, this paper gives a structural frame to analyze and appreciate network organizations. As a simulation paper, this research presents the parsimonious model and tests several robustness checks assumptions. However, I would like to suggest that the network is not randomly distributed but governed intentionally as social capital. Even though the spatial diffusion of information is important, organizations have their capacities to build and cut their inter-organizational ties. Then what would be the result of simulation if the condition of optimization is added?

 

 

References

 

Centola, D. and M. Macy (2007). “Complex contagions and the weakness of long ties.” American Journal of Sociology, 113: 702-734.

Cohen, and Levinthal (1990) "Absorptive capacity: A new perspective on learning and innovation." Administration Science Quarterly, 35: 128-152.

DiMaggio, P. J. and W. W. Powell (1983). “The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields.” American Sociological Review, 48: 147-160

Granovetter, M. (1973) “The Strength of Weak Ties.” American Journal of Sociology 78:1360–80.

Granovetter, M. (1978) “Threshold Models of Collective Behavior.” American Journal of Sociology 83:1420–43.

Hannan, M. T. and J. Freeman (1977). “The population ecology of organization.” American Journal of Sociology, 82: 929-964.

Merton, Robert K. (1968) "The Matthew Effect in Science." Science 159:56-63.

Meyer, J. W. and B. Rowan (1977). “Institutional organizations: Formal structure as myth and ceremony.” American Journal of Sociology, 83: 340-363.

Podolny, J. M. (2001). “Networks as the pipes and prisms of the market.” American Journal of Sociology, 107: 33-60.

Powell, W. W., K. W. Koput, and L. Smith-Doerr (1996). “Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology.” Administrative Science Quarterly, 41: 116-145.

Uzzi, B. (1996). “The sources and consequences of embeddedness for the economic performance of organizations: The network effect.” American Sociological Review, 61: 674-698.

Watts, Duncan J., and Steven H. Strogatz. (1998) “Collective Dynamics of ‘Small-World’ Networks.” Nature 393:440–42.

 

 

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[1] The author estimates the occurrence of the exit in 1991 with variables in 1990.

[2] Its secondary micro-to-macro effect is the concentration at the industry level (Powell, Koput, & Smith-Doerr 1996: 142-143). Between 5 years field becomes more selective and tightly connected.

[3] Subsequently, I would like to ask a discussion topic. Is an organization in a focal field an adequate entity to discuss the effect of network on community level? In other words, what would be reasonable unit of analysis for the industry-level differences?

[4] Revealing the sources of the social capital could be another interesting research topic.