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Identifying and Explaining Instabili...

Mallinson, Daniel J.

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Identifying and Explaining Instability in the General Model of Policy Innovation Diffusion.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifying and Explaining Instability in the General Model of Policy Innovation Diffusion./
Author:	Mallinson, Daniel J.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	253 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.
Contained By:	Dissertation Abstracts International78-11A(E).
Subject:	Political science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10629086
ISBN:	9781369991895

Identifying and Explaining Instability in the General Model of Policy Innovation Diffusion.
Mallinson, Daniel J.

Identifying and Explaining Instability in the General Model of Policy Innovation Diffusion. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 253 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.

Thesis (Ph.D.)--The Pennsylvania State University, 2017.

Policy diffusion research has deep roots in political science and continues to grow as a research program. Over the last 25 years, scholars assembled a body of knowledge one piece at a time using event history models that focus on the adoption of a single innovation. This approach generated new knowledge about specific predictors of adoption; however it also created a fractured testing of the general model of policy diffusion. In response, scholars recently began returning to an older approach of aggregating adoption data in order to determine the generalizability of the policy diffusion model. This dissertation bridges the two approaches and further builds on the macro-level approach by testing the general patterns of innovation adoption while also pushing forward our understanding of the causal mechanisms underlying the process.

ISBN: 9781369991895Subjects--Topical Terms:

528916
Political science.

Identifying and Explaining Instability in the General Model of Policy Innovation Diffusion.
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245 1 0 $a Identifying and Explaining Instability in the General Model of Policy Innovation Diffusion.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 253 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.
500 $a Adviser: David Lowery.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2017.
520 $a Policy diffusion research has deep roots in political science and continues to grow as a research program. Over the last 25 years, scholars assembled a body of knowledge one piece at a time using event history models that focus on the adoption of a single innovation. This approach generated new knowledge about specific predictors of adoption; however it also created a fractured testing of the general model of policy diffusion. In response, scholars recently began returning to an older approach of aggregating adoption data in order to determine the generalizability of the policy diffusion model. This dissertation bridges the two approaches and further builds on the macro-level approach by testing the general patterns of innovation adoption while also pushing forward our understanding of the causal mechanisms underlying the process.
520 $a The first chapter of this dissertation pulls together the extant evidence, i.e., the existing trees, through a systematic review and synthesis of the last 25 years of diffusion findings. The goal is to understand the weight of evidence for theoretically important diffusion predictors, as well as the varied modeling approaches for spatial effects and duration dependence. Doing so reveals great diversity not only in scholars' approaches to modeling diffusion, but also their findings regarding some of its most important predictors (e.g., the influence of neighbor adoptions). The subsequent chapters test potential explanations for the diversity in these findings.
520 $a First, chapters 3 and 4 test the generalizability of the policy diffusion model and examine how its elements change across time and throughout the diffusion lifecycle. Specifically, using a large dataset of innovations adoptions from 1960 through 2010, chapter 3 tests how the association between external influences, internal characteristics, and innovation attributes changes from 1960 through 2010 and chapter 4 examines how they change during different stages of the diffusion process (i.e., innovation, early adoption, early majority adoption, late majority adoption, and laggard adoption). While many of the important predictors exhibit general effects when all data are pooled, those effects are not stable across time. For instance, there is evidence to suggest that the effect of regional pressure declined over the last 50 years, while ideological similarity is increasingly important. This comports with an emerging understanding of increasingly polarized politics in the states.
520 $a Chapter 5 views adoption from a different angle by offering a new continuous measure of policy adoption speed. In this case, the policy becomes the unit of analysis so that researchers can examine how innovation attributes shape how quickly policies are taken up by the states. This is an important development for sorting out when learning is the primary causal mechanism driving the diffusion of a policy versus other mechanisms that are typically faster or slower.
520 $a Understanding macro-level diffusion dynamics is important; however researchers also must advance our understanding of the micro-level causal mechanisms that drive innovation adoption. To that end, Chapter 6 offers a theoretical construct for sorting and testing these mechanisms, as well as the foundation for and experimental paradigm for the purpose of understanding the elite socialization mechanism. Specifically, this mechanism focuses on how social pressure from elites' interpersonal relationships influences the propensity to adopt new ideas. While the experiment does not yet directly test this theory among an elite population, it demonstrates that public compliance pressure and private acceptance underlie conformity behavior in the political realm. This successful experimental protocol provides a foundation for additional incremental advancements that will help us understand how the personal interactions of state legislators shape their search for and use of policy information.
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