![]() ![]() We use traditional firm-level indicators from a questionnaire-based innovation survey (German Community Innovation Survey) to train an artificial neural network classification model on labelled (product innovator/no product innovator) web texts of surveyed firms. Specifically, we develop a method to identify product innovator firms at a large scale and very low costs. In this paper, we propose a first approach on generating web-based innovation indicators which may have the potential to overcome some of the shortcomings of traditional indicators. Consequently, they struggle to provide policy makers and scientists with the full picture of the current state of the innovation system. However, traditional indicators from patents and questionnaire-based surveys often lack coverage, granularity as well as timeliness and may involve high data collection costs, especially when conducted at a large scale. Results also indicate a better performance for the prediction of product innovators and firms with innovation expenditures than for the prediction of process innovators.Įvidence-based STI (science, technology, and innovation) policy making requires accurate indicators of innovation in order to promote economic growth. Moreover, predictions with website characteristics significantly differ from baseline predictions according to a McNemar test. In our main analysis, models using all website characteristics jointly yield AUC values of up to 0.75 and increase accuracy scores by up to 18 percentage points compared to a baseline prediction based on the sample mean. ![]() Our results show that the most relevant website characteristics are textual content, the use of English language, the number of subpages and the amount of characters on a website. Website characteristics are measured by several data mining methods and are used as features in different Random Forest classification models that are compared against each other. In this study, we use data on 4,487 firms from the Mannheim Innovation Panel (MIP) 2019, the German contribution to the European Community Innovation Survey (CIS), to analyze which website characteristics perform as predictors of innovation activity at the firm level. However, little is known yet about the accuracy and relevance of web-based information for measuring innovation. Web-based innovation indicators may provide new insights into firm-level innovation activities. ![]()
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