Academic journal article Cityscape

Adoption of High-Performance Housing Technologies among U.S. Homebuilding Firms, 2000 through 2010

Academic journal article Cityscape

Adoption of High-Performance Housing Technologies among U.S. Homebuilding Firms, 2000 through 2010

Article excerpt

Introduction

In the National Climate Assessment, researchers report that the warming of the U.S. climate during the past 50 years is significantly related to human (Melillo, Terese, and Yohe, 2014). They argue that a strong need exists for businesses and individuals to adopt innovative products, processes, and thinking that changes how products are produced and energy is consumed. Failure to move toward these innovations, scientists believe, will result in continued growth in the severity and types of risks to the United States.

The U.S. Department of Energy (DOE) reports that the housing stock has been increasing energy efficiency since 1980. Houses built most recently are 14 percent more energy efficient (EE) than homes built 30 years ago and 40 percent more EE than homes built 60 years ago (DOE, 2014). With respect to energy consumption, in 2014, all residential buildings consumed 21.15 quadrillion BTUs (British Thermal Units) of energy, down 1.1 percent from 2010.

From 2005 to 2010, the academic literature focused on climate change doubled in size along with heavy expansion in the range of topics, geographies, and disciplines analyzed (Burkett and Suarez, 2014). One study area has had an expansion of analysis is in regard to innovation applied to issues of environmental change and performance. Innovation can be a powerful lens to process empirical information about changes within markets and can be used as a framework for gaining increased understanding of potential solutions to environmental problems. After more than 100 years of innovation research, scholars can show that adoption and diffusion of innovation are critical forces that build competitive advantage, disrupt existing markets, and create new markets (Christensen, Anthony, and Roth, 2004). Despite innovation being applied to a wide swath of disciplines, until recently, scholars of innovation have not focused a great deal on construction. Few diffusion-ofinnovation modeling techniques have been applied in the commercial construction literature (Kale and Arditi, 2009, 2006, 2005; Rose and Manley, 2014, 2012) and scholars have not regularly experimented with advancing variations of innovation diffusion models within residential building construction or new and existing housing. At the same time, U.S. home builders have been characterized as being resistant or slow to adopt innovation.

In light of these industrial concerns, a substantial opportunity for new analysis exists. This work (and article) sits at the convergence of these topics and serves as a foundational step of a larger project examining U.S. home builders' choices to adopt innovative housing technologies that improve the environmental performance of new single-family homes. The article begins by summarizing literature on adoption and diffusion of innovation and defining its relationship to homebuilding. The work then describes a conceptual statistical model and application for analyzing innovation adoption among home builders. Another goal of the work is to distill current and previous research, variables, and methods for future work. Future projects could augment the statistical model to examine extant factors that explain U.S. home builders' choice of EE and highperformance technology over traditional and less EE substitutes.

In the following sections of this article, the authors address these research questions: (1) What external parameters are likely to be associated with builders' decisions to adopt high-performance housing technology alternatives across time and into recent years and (2) do external parameters surrounding this change support a general shift toward environmental performance as a central component of diffusion in the homebuilding industry? In answering, we describe an array of data that will inform diffusion modeling and enable others to refine industry models and draw empirical conclusions about builders' innovation adoption choices. Our description of the data and the generic conceptual model further proposes (1) methods for measuring adoption patterns of high-performance technologies, (2) a comparison of the sample with independent measures of the builder population, (3) regression analysis tools, and (4) the potential significance of the preliminar)7 model for diffusion of technology in general. …

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