## Introduction

Even though an extensive body of literature exists on medical technology assessment, much less is known about the productivity consequences of new technologies and about the way technologies are diffused among hospitals. The tying together of productivity and technology diffusion is relevant because technology is often cited as a cause for increasing costs in developed countries. An assessment of the productivity—innovation diffusion relationship among hospitals—contributes to important policy recommendations on how to influence the diffusion of innovations in order to reduce costs and enhance quality of care. Because costs are involved in developing and introducing new medical technologies, insight as to which factors contribute to timely diffusion of these technologies is of great interest. The central question therefore is whether or not factors can be identified that are sensitive to policy measures.

In this paper, we intend to model and to measure the relationship between hospital characteristics and the probability of technological diffusion. Responding to the Greenhalgh *et al*. (2004) criticism that previous work in this area focused on a single unit within an organization or a single technology, we introduce the concept of clustering innovations to describe the diffusion of technologies. Because we have access to a rather unique panel data set (1995–2002) of more than 60 Dutch hospitals and 60 technological innovations, we can develop a detailed description of the diffusion process. In earlier studies, the productivity issue is discussed for a large number of innovations in the Dutch hospital industry (Blank, (2008); Blank and van Hulst, 2009).

In this research, we use a log odds random effects regression model to describe the diffusion of technologies. Log odds models are generally introduced when estimating probabilities or when a dependent variable is bounded. We also introduce an innovation index, as our dependent variable measuring the individual hospital's innovation “performance” relative to the industry's average. Because we are dealing with panel data, we apply a random effects model estimation technique (see, e.g., Greene, 2008: 831–835). The remainder of the paper unfolds as follows. In the Theory of diffusion section, we discuss a general theoretical background of the diffusion of innovations. In the Dutch hospital industry section, the institutional context of Dutch hospital industry is briefly discussed. In the Econometric model and estimation section, we present an econometric model for estimating the effects of various determinants on the innovation index, measuring the number of technologies relative to the industry's average. In the Data section, we describe the data followed by the Empirical results section where the outcomes of the econometric analyses are presented. In the Conclusions and policy recommendations section, we conclude the paper and present some possible policy recommendations.