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Forecasting of advertising effectiveness for renewable energy technologies: A neural network analysis

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  • Sharifi, Mehdi
  • Khazaei Pool, Javad
  • Jalilvand, Mohammad Reza
  • Tabaeeian, Reihaneh Alsadat
  • Ghanbarpour Jooybari, Mohsen

Abstract

The adoption of renewable energy technologies (RETs) as a sustainable practice in the residential construction sector depends on promotional efforts. With a modeling-based contribution, this research aims to analyze advertising effectiveness in the context of RETs adoption regarding solar water heaters. The study is based on a survey of 398 Iranian citizens. A neural network analysis was employed to identify advertising effectiveness in terms of the AIDA framework. The results indicated that the neural network is able to predict the relationships among advertising effectiveness indices; namely attention, interest, desire in the RETs setting, and action. According to the neural network analysis, attention was found to be the most significant predictor of action, followed by interest and desire.

Suggested Citation

  • Sharifi, Mehdi & Khazaei Pool, Javad & Jalilvand, Mohammad Reza & Tabaeeian, Reihaneh Alsadat & Ghanbarpour Jooybari, Mohsen, 2019. "Forecasting of advertising effectiveness for renewable energy technologies: A neural network analysis," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 154-161.
    • Handle: RePEc:eee:tefoso:v:143:y:2019:i:c:p:154-161
    DOI: 10.1016/j.techfore.2019.04.009
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