Geography Reference
In-Depth Information
new knowledge resembles a stone thrown into a calm pond, its ripples moving steadily
across the entire surface.
Though this pattern accurately describes the dif usion of a wide variety of innova-
tions and knowledge, critics have faulted this focus on the S-curve for several reasons
(see Mahajan et al., 1990; Hargadon, 1998). Two of these critiques have particular rel-
evance here. First, the classic dif usion literature typically depicts knowledge as moving
unaltered as it passes from one actor to the next. Contrary to this depiction, in reality
transmission rarely occurs with perfect i delity. Both gaps in the information sent and
errors in its interpretation typically require the receiver to reconstruct portions of the
original knowledge. This process occurs so commonly that it even forms the basis of
amusement in the children's game of telephone.
2
Most knowledge, therefore, requires
ef ort to acquire and transmutes to some extent as actors strive to receive and build on it;
recipients assimilating new knowledge must actively process it by experimenting with its
application to new problem domains and environmental contexts. Witness, for instance,
the ef orts of American automakers as they struggled to digest the knowledge embodied
in Japanese lean production techniques (Womack et al., 1990) or the labors of computer
makers as they sought to imitate Dell's direct distribution model (Porter and Rivkin,
1999). In both cases, the receipt of knowledge required years of trial, error, rel ection,
and adjustment and, arguably, remains incomplete.
Even within the supportive infrastructure of an organization, receiving and building
on new knowledge can prove dii cult. Teece (1977), for example, reports that the trans-
mission and assimilation of technical know-how accounted for 19 percent of project
costs, on average - running as high as 59 percent in one case - in 26 international tech-
nology transfer projects. Chew et al. (1990) i nd the internal transfer of best practices
so incomplete in multi-plant commercial food operations that, within a i rm, the best
plants produce twice as ei ciently as the worst, even after controlling for dif erences
in processing technology, location, and plant size (Szulanski, 1996, of ers additional
evidence). Hence, we regard the act of receiving and building on knowledge not as the
acceptance of a complete, well-packaged gift, but rather as the beginning of a trial-and-
error process.
Our second concern regarding the simple S-curve characterization of dif usion arises
from its inattention to the crucial role that social networks play in dif usion. Several
studies, largely out of sociology, demonstrate that knowledge spreads from its source
not in concentric circles, but along conduits dei ned by social connections (Burt, 1987;
Coleman et al., 1966; Lazarsfeld et al., 1944; see Marsden and Friedkin, 1993, for
a review). Consider some of the relevant i ndings: Hedström (1994) discovered that
network density and geographic proximity can explain most of the spread of the idea of
unionization in Sweden. In an analysis of adoption patterns for 'poison pills' and 'golden
parachutes', Davis and Greve (1997) of ered strong evidence that information about
these policies traveled through corporate board interlocks. And Hansen (1999) found
that strong ties best conveyed complex knowledge across product development teams
within a i rm. A growing literature thus points to the importance of social networks as
pathways that channel the l ow of knowledge among actors.
We synthesize these two perspectives - knowledge receipt as an active process of
experimentation and search, and an appreciation for the role of social networks - into
a model of knowledge l ow. The model of ers unique predictions regarding how knowl-
