Forecasting of advertising effectiveness for renewable energy technologies: A neural network analysis
| Autor: | Javad Khazaei Pool, Reihaneh Alsadat Tabaeeian, Mehdi Sharifi, Mohammad Reza Jalilvand, Mohsen Ghanbarpour Jooybari |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry 020209 energy 05 social sciences Advertising Context (language use) 02 engineering and technology Solar water Renewable energy Neural network analysis Action (philosophy) Management of Technology and Innovation 0502 economics and business 0202 electrical engineering electronic engineering information engineering Sustainable practices Business and International Management business 050203 business & management Applied Psychology |
| Zdroj: | Technological Forecasting and Social Change. 143:154-161 |
| ISSN: | 0040-1625 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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