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Assessing and addressing differences in climate action predictors using k-means for audience segmentation
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This study aims to contribute with a model to assess climate -means clustering to address their differences better. To answer this, 209 respondents answered 13 questions assessing nine known predictors of climate action, out of which four predictors, across 184 validated responses, were selected as features for the final model: self-efficacy, response-efficacy, hope, and self-identity. The final model found four natural groupings a cross evenly densed groups: The Average, Pessimist With Potential, Advocates, and finally Not Convinced, that all varied a cross the four mentioned predictors, hence requiring different means of addressing explored in the common part as an initial onboarding.
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Climate change Clustering Sustainable entrepreneurship K-means Climate action Audience segmentation Business analysis