Agriculture Reference
In-Depth Information
14.5 TIME DYNAMICS, ACTOR IDENTITIES, AND INNOVATION
Up to this point, we have learned a great deal about the framing of small-farmer
choices for ISFM. From this exploration of the first phase, four broad points can be
retained: (i) farmers and scientists see the world differently; (ii) economic factors
shaping ISFM choices extend beyond the field and farm to include complex farm
household livelihood systems; (iii) dissemination processes occur over time and
involve some sort of social learning; and (iv) ideologies and religions can be mobi-
lized to help frame ISFM choices. In contrast to the limited study of the framing of
decision making, the second phase, or that of the actual decision making, has been
studied extensively. Rogers'
Diffusion of Innovations
(1962) is paradigmatic of that
approach. Here we examine how this theory of technological change in agriculture
characterizes the process of innovation by examining actor identification, the time
dynamics, and the concept of innovation itself.
Rogers'
Diffusion of Innovations
(1962) was first to consider the adoption of inno-
vations as process and identified a range of adopter categories that would succes-
sively take up an innovation. These adopters could be modeled as successive groups
extending from one end of the bell curve to the other: innovators, early adopters,
early majority, later majority, and laggards (Figure 14.1). The time of adoption was
attributed to the individuals' degree of innovativeness. The accumulation of these
adopters over time could be graphed as an S-curve (Figure 14.2). Here, the shape
of the S-curve was explained by differences among adopter categories. The time
dimension was measured by the rate of adoption, or the length of time before a larger
percentage of the population adopted the innovation (Rogers 1983). This approach
has been highly influential.
Originally the work focused on a rather static perspective coming out of the Green
Revolution based on cross-sectional studies that did not allow for much emphasis
of the time dimensions that are integral to diffusion as a process. More dynamic
approaches to modeling adoption processes emerged (see Besley and Case 1993;
Innovators
Late
majority
34%
Early
adopters
13.5%
Early
majority
34%
Laggards
16%
2.5%
−
−
2sd
−
−
sd
−
−
+
sd
FIGURE 14.1
Adopter categorization on the basis of innovativeness (measured by time).
(Adapted from Rogers, E.M. 1983.
The Diffusion of Innovations
. 3rd ed. New York: The Free
Press. With permission.)
