Abstract
Web-based innovation indicators may provide new insights into firm-level innovation activities. However, little is known yet about the accuracy and relevance of web-based information for measuring innovation. 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. 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. 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. 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. Moreover, predictions with website characteristics significantly differ from baseline predictions according to a McNemar test. Results also indicate a better performance for the prediction of product innovators and firms with innovation expenditures than for the prediction of process innovators.
Highlights
Innovation, defined as the implementation of either new or significantly improved products or processes as well as combinations thereof [1], brings vast benefits to consumers and businesses
Our results show that predictions based on website characteristics can perform significantly better than a random prediction based on the sample mean
We contribute to the discussion on whether web-based innovation indicators are a feasible alternative to survey-based innovation indicators
Summary
Innovation, defined as the implementation of either new or significantly improved products or processes as well as combinations thereof [1], brings vast benefits to consumers and businesses. Technological progress is considered as a main driver of economic growth [2]. It is, a matter of public interest to analyze and understand innovation dynamics as it is conducted in several studies (e.g., [3,4,5,6,7,8,9]). A prerequisite for the analysis of innovation-related questions is to correctly measure firmlevel innovation activities. It should be noted that no universally accepted measurement approach exists.
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