Linear Change Model Research Articles

The Co-Evolution of Technology and Discourse: A Study of Substitution Processes for the Insecticide DDT

Product substitution is an important discontinuity in technology evolution. Conventional accounts draw on rational, linear models of change and emphasize that the process is driven by the appearance and adoption of new artifacts. This article adopts a constructivist approach to address the question of whether the social reconstruction of incumbent artifacts can trigger their substitution, even in the absence of new alternatives. Drawing on a case study of the insecticide DDT and employing a discourse analytical perspective, four artifact-constituting discourses which have been employed to construct and reconstruct DDT are identified, and their implications for product substitution discussed.

A tale of two councils: strategic change in Australian local government

This article: contrasts the strategic change programmes undertaken at the two leading reformist councils in local government in New South Wales, Australia, namely Liverpool and Wollongong. uses the distinction between linear and processual change as the framework of analysis. argues that the configuration of contextual factors influences the adoption of linear or processual pathways to change, and analyses these factors in the cases of the two councils. rejects the argument that linear change models are no longer appropriate for explaining transformational change.

A tale of two councils: strategic change in Australian local government

This article: contrasts the strategic change programmes undertaken at the two leading reformist councils in local government in New South Wales, Australia, namely Liverpool and Wollongong. uses the distinction between linear and processual change as the framework of analysis. argues that the configuration of contextual factors influences the adoption of linear or processual pathways to change, and analyses these factors in the cases of the two councils. rejects the argument that linear change models are no longer appropriate for explaining transformational change.

Root Metaphor Versus Square Root: Research Evidence for a Contextualist Theme

Steenbarger has elegantly described contextualist themes in counseling. His discussion clearly reveals the limitations of linear models of change; nevertheless, one should carefully examine the language used to discuss higher order change processes. The point of this article is that the language used should be put into practice so that research can be conducted to examine the concepts conveyed by the language. In this way, we may better understand the mechanisms of change, and practitioners may benefit.

Learning on a response continuum: Comparison of a linear change and a functional learning model

Two reinforcement schedules were used to compare the predictive validity of a linear change model with a functional learning model. In one schedule, termed “convergent,” the linear change model predicts convergence to the optimum response, while in the other, termed “divergent,” this model predicts that a subject's response will not converge. The functional learning model predicts convergence in both cases. Another factor that was varied was presence or absence of random error or “noise” in the relationship between response and outcome. In the “noiseless” condition, in which no noise is added, a subject could discover the optimum response by chance, so that some subjects could appear to have converged fortuitously. In the “noisy” conditions such chance apparent convergence could not occur. The results did not unequivocally favor either model. While the linear change model's prediction of nonconvergence in the divergent conditions (particularly the “noisy” divergent condition) was not sustained, there was a clear difference in speed of convergence, counter to the prediction inferred from the functional learning model. Evidence that at least some subjects were utilizing a functional learning strategy was adduced from the fact that subjects were able to “map out” the relation between response and outcome quite accurately in a follow-up task. Almost all subjects in the “noisy” conditions had evidently “learned” a strong linear relation, with slope closely matching the veridical one. The data were consistent with a hybrid model assuming a “hierarchy of cognitive strategies” in which more complex strategies (e.g., functional learning) are utilized only when the simpler ones (e.g., a linear change strategy) fail to solve the problem.