Idea mining for web-based weak signal detection

Abstract

We investigate the impact of idea mining filtering on web-based weak signal detection to improve strategic decision making. Existing approaches for identifying weak signals in strategic decision making use environmental scanning procedures based on standard filtering algorithms. These algorithms discard patterns with low information content; however, they are not able to discard patterns with low relevance to a given strategic problem. Idea mining is proposed as an algorithm that identifies relevant textual patterns from documents or websites to solve a given (strategic) problem. Thus, it enables to estimate patterns’ relevance to the given strategic problem. The provided new methodology that combines weak signal analysis and idea mining is in contrast to existing methodologies. In a case study, a web-based scanning procedure is implemented to identify textual internet data in the field of self-sufficient energy supply. Idea mining is applied for filtering and weak signals are identified based on the proposed approach. The proposed approach is compared to a further – already evaluated – approach processed without using idea mining. The results show that idea mining filtering improves quality of weak signal analysis. This supports decision makers by providing early and suggestive signals of potentially emerging trends, even with only little expressive strength.