Survival trees - a new method in innovation theory: A successful introduction of a method commonly u
This book deals with survival trees and their application to the analysis and prediction of innovation diffusion processes. Three major contributions of the book are noteworthy: Firstly, the author presents a very comprehensive, accurate and accessible overview of the current research activities on survival trees. This is particularly important because, due to the novelty of the method, no universally accepted best approach exists yet; many technical details of the method are still subject to ongoing research and debate. By providing an overview of the current state of research, the author identifies the different approaches that have been proposed for splitting nodes, pruning, and final tree selection, providing guidance for the choice of an appropriate approach to the applied part of the text. Secondly, the overview of statistical packages that are available for survival tree analyses and the discussion of their respective merits and limitations has a high practical value and is unique within ist category. Thirdly, the applied part of the text successfully demonstrates the usefulness of the survival tree method to identify clusters with significant differences in expected adoption times, thus providing a rigorous and easyly interpretable analysis of early and late adopter groups. In the discussion section, the authorfurther points out how the survival tree method deals with censored observations.
Chapter 2.1, Analysing and Forecasting Innovation Diffusion by Dynamic Micro Models:
,An innovation is an idea practice or object that is perceived as new by an individual or another unit of adoption’. Commonly speaking, innovation diffusion theory addresses how new ideas, products and social practices spread within society or from one society to another. Moreover, adoption theory analyzes the process of innovation adoption by an individual. Both theories aim to identify explanatory variables that drive and determine the respective process. The adoption process of each individual can differ in starting point and duration. In this way, adoption decisions of members of social systems are spread across time. Consequently, the adoption theory forms the fundament of innovation diffusion theory and is thus part of it.
While, by definition, adoption theory is mainly concerned with the exploration of the determinants of adoption, the diffusion theory focuses on the aggregate analysis of all adoption decisions of the members of a social system.
However, by recognizing that the diffusion process is built upon individual adoption decisions, the adoption theory should be recognized and modelled much more as the key basement of diffusion theory rather than a theory that is conceptionally and in content different to the diffusion theory. For this reason, I will make no explicit distinction between these two theories which I claim to belong together.
The diffusion of an innovation has traditionally been defined as the process by which ,an innovation is communicated through certain channels over time among the members of a social system’. This definition, with its reference to innovation, communication (and the respective communication channels), time and the members of a social system names the four key components widely recognized as driving innovation diffusion. Although the diffusion process is undoubtedly a dynamic process, the majority of the models that have emerged in diffusion theory could only insufficiently capture this essential feature. Empirical research for analysis and forecast of the diffusion process is still dominated by aggregate diffusion models that mostly envisage capturing the influence of marketing variables on the success of an innovation.
These approaches are convenient in practical terms but they raise the following question: Can a genuine diffusion model be constructed by aggregating demand from consumers who behave in the neoclassical way? That is, assume that consumers are smart and are not just carriers of information? They therefore maximize some objective function such as expected utility or benefit from the product, taking into account the uncertainty associated with their understanding of its attributes, its price, pressure from other adopters to adopt it and their budget. Because the decision to adopt is individual-specific, all potential adopters do not have the same probability of adopting the innovation in a given time-period. Is it possible to develop the adoption curve at the aggregate market level, given the heterogeneity among potential adopters in terms of adopting the innovation at any time t?
In fact, aggregate models cannot explain why an individual adopts or rejects an innovation at a specific point in time. As a result, these models achieve no adequate aggregation of individual adoption decisions. Analysis and forecast of adoption procedures by means of these models is hardly convincing. While attempts have been taken to unbundle adopters of the aggregate level by categorizing adopters ex-post into a scheme, they could not eliminate the shortcomings of the underlying assumption of adopter homogeneity.
The general scheme used for adopter classification is that of Rogers. Rogers divided individual responses to technology into five ideal categories: innovators, early adopters, early majority, late majority, and laggards. According to him, the main concern of the innovation diffusion research is how innovations are adopted and why innovations are adopted at different rates. Furthermore, he identified five characteristics of innovations that help to explain differences in adoption rates: relative advantage, compatibility, complexity, trialability, and observability. His work has become fundamental to innovation diffusion research and has been documented and quoted in many papers and books.
Although a wide variety of innovations and diffusion processes have been investigated, one research finding keeps recurring. If the cumulative adoption time path or temporal pattern of the diffusion process is plotted, the resulting distribution can generally be described as taking the form of an s-shaped (sigmoid) curve. The observed regularity in the diffusion process results from the fact that initially only few members of the social system adopt the innovation in each time period. In subsequent time periods, however, an increasing number of adoptions per period occurs as the diffusion process begins to unfold more fully. Finally, the trajectory of the diffusion curve slows down and begins to level off, ultimately reaching an upper asymptote. At this point diffusion is complete.
In entrepreneurial reality, information about the process of diffusion is crucial to the success of new product marketing. If this information is provided on the aggregate level, however, marketing implications are limited. A company will not know whom to target to drive the diffusion process forward. These shortcomings may have let to an unquantifiable waste of resources as companies are likely to have targeted late adopters in the early stages of the innovations market placing and vice-versa. A tool that can identify crucial target groups at every stage of the diffusion process is seen to be of utmost importance in marketing. So far, there is no method that is capable of providing this insight.
Besides, the witnessed unilateral reliance on aggregate models may have let to a great number of incorrect diffusion prognoses. The most prominent example of an (ex-post) off beam forecast that was based on an aggregate model is described in a diffusion study by Berndt and Altobelli (1991). Other wrong forecasts may prove the insufficient predictive power of these diffusion models.
In practice, companies need information about target clients and the factors that drive their decisions; something aggregate models cannot provide. This growing recognition has materialized in a mounting demand for rapid integration of micro models to identify and analyse the adoption and diffusion process. Next to the widely used macro models, these micro models can contribute decisively to the analysis and forecast of adoption behaviour and the resulting diffusion process.
Even though the adoption behaviour is nothing but the disaggregated form of the diffusion process, the areas of adoption theory and diffusion theory have been largely separated so far. In fact, not all micro models can be used to analyse and forecast innovation diffusion.
Generally, all micro models consider the heterogeneity of individuals and allow for the integration of co-variables. There is only one type of model, however, that can adequately model censored event data in order to capture the dynamics of the diffusion process. Thus, I claim that only dynamic micro models can be used to forecast innovation diffusion adequately.
To illustrate this, a comparison between a static model and dynamic micro model will be used.
If the focus of analysis is on finding out whether a specific individual adopts or rejects an innovation at a specific point in time and what explanatory variables can be identified, then logistic regression is often employed. This method explains one dependent dichotomous variable through a number of independent variables. Within the framework of ADT, the dichotomous variable can be labelled ,adoption of innovation’ and ,rejection of innovation’ always with respect to one specific point in time. Independent variables could be all sorts of individual characteristics. In ADT one often differentiates between product-, adopter- and environment specific independent variables.
For logistic regression the usual restrictive assumptions that are known from linear regressions have to be taken. A violation of these premises can lead to distorted and inefficient estimations for the regression coefficients and eventually to invalid statistical inferences. Here, empirical research is still severely limited by the existence of multicollinearity and autocorrelation between the independent variables. Generally speaking, logistic regression establishes a functional relation between the probability that an event takes place (i.e. an individual adopts the innovation) and a number of predetermined explanatory variables (i.e. independent variables).
In contrast to the linear regression the observable dependent variable, in this case, is not metric, but dichotomous. Logistic regression quantifies and thus identifies the factors driving or preventing individual innovation adoption. Heterogeneity of individuals is respected and uncovered. Nevertheless, the characteristics of the process itself are not considered at all. With the help of logistic regressions only the result of the adoption process can be revealed. All individuals who have adopted the innovation in between the market placing and the end of the observation period are classified as adopters. Individuals who have not yet or will never adopt the innovation are accordingly classified as non-adopters. There is no differentiation with respect to the adoption’s specific point in time and the future possibility of adoption. Logistic regression ignores time and thus merely gives a snapshot of adoption behaviour and the diffusion process.
No valid conclusions can be drawn concerning future market potential, for instance. Despite of this, it is out of the question that with the method elementary relations between adoption decisions and its determinants can be established. Nevertheless, in logistic regressions the duration between market placing and adoption is not taken into account. There is no difference between those individuals who adopt the innovation shortly after market placing and those who adopt shortly before the observation period ends.
Yet, it appears only natural that, by average ,early adopters’ exhibit a higher likelihood of adoption than ,late adopters’. The negligence of this information reduces accuracy and inferential power of static estimations. Besides, it is the time-related observation of the adoption process, in particular, that enables predictions about future adoption behaviour and thus innovation diffusion. A solution could be the integration of a time-to-adoption independent variable but then one could only consider the individuals who have already adopted the innovation within the observation period. As for the individuals who have not adopted in the period, no time-to-adoption duration can be asserted, as we do not know when and whether they will adopt the innovation after the observation period ends. These observations are ,censored’. Censored data can simply be ignored and filtered off the analysis, but this leads to distorted estimates, which is why this approach should be abandoned in the presence of censored data. It is here that the so-called event history models come in.
Burkhard Frhr. v. Wangenheim was born in 1978 in Cologne. After periods of study in the USA and Chile, he completed his degree in Business Administration with honours. Presently, the author resides in London, where he works for an international capital fund, investing and assisting investments in medium-sized German businesses.
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- Artikel-Nr.: SW9783954895045
- Artikelnummer SW9783954895045
Burkhard Freiherr von Wangenheim
- Wasserzeichen ja
- Verlag Anchor Academic Publishing
- Seitenzahl 101
- Veröffentlichung 01.06.2013
- ISBN 9783954895045
- Wasserzeichen ja