Editorial introduction to the special issue: bridging the gap between adoption theory and practice in technological forecasting

Predicting patterns of technology adoption and diffusion has been core to the field of technological forecasting since its inception. However, due to historical reasons, there has been a growing disconnect between scholars developing theory on technology adoption, and those developing and applying forecasting models. The papers in this Special Issue intend to narrow the gap in three ways: developing multi-level forecasting models, reconciling theory with results from forecasting models, and addressing human biases in judgmental methods. Their findings highlight the importance of incorporate behavioral and psychological aspects of adoption in forecasting practice, and consider tradeoffs between complexity and explanatory power of forecasting models. We see opportunities for future research to dive deeper into the dynamic character of adoption antecedents, how the nature of the technology may affect model choice, and how to use artificial intelligence techniques to leverage and combine diverse data sources.