Knowing the time point of large-scale diffusion of a radically new high-tech innovation is a highly relevant topic. Companies, researchers, and government institutions can plan their research and development efforts, production, as well as marketing plans according to the predicted time point of large-scale diffusion. The research is based upon the assumption that specific indicators can predict the start of large-scale diffusion. The scientific field of forecasting the start of large-scale diffusion is relatively new. Therefore, an explorative methodology was required for this research. During the explorative process, it was ensured that indicators reflect on the holistic environment of an innovation by minding the so-called data collection cube. A data selection funnel was created, narrowing scientific branches down to a list of indicators in three steps. Each of these steps has its own criteria designed to: • Select scientific branches with the highest potential to find results in the literature reviews • Derive indicators that can observe the diffusion • Select indicators that can predict the start of large-scale diffusion The last step of the data selection funnel, selecting the indicators which can actually predict, was carried out with the support of three researchers. Eight criteria were used to select the most potential indicators: (i) Prediction, (ii) Timeliness of prediction, (iii) Availability of data, (iv) Cost of data, (v) Quantifiable& Objectivity, (vi) Empirical proof, (vii) Generalizability, (viii)Simplicity. The researchers were asked to evaluate the indicators according to the criteria (i) and (vi) as part of the scientific quality gate selecting the most potential indicators. After the indicators have been evaluated, a sensitivity analysis has been performed to improve the robustness of the selection mechanisms and to rule out an arbitrary selection of the indicators. Out of 50 indicators found in the literature or derived from the literature, 38 indicators were selected according to the selection mechanism. These 38 indicators have been split into two sets of judgemental and non-judgemental indicators to prepare the design of the forecasting approach. The forecasting approach aims to guide a user towards the correct forecasting technique given an innovation and situation. Five forecasting techniques were found to befitting the: (i) assumptions-based modelling, (ii) Delphi method, (iii)analogous forecasting, (iv) time series & regression models, and (v)artificial neural networks. However, each of the five forecasting techniques have disadvantages that can be overcome by one of the other methods. Hence, the forecasting approach has two stages. First, the user is guided towards the primary method and subsequently towards an additional method overcoming the disadvantages of the first method and improving the overall reliability of the forecast. For each method, a set of indicators is recommended. Once the forecasting approach has been developed, the completeness of indicators has been checked by using Ortt & Kamp’s 14 factors influencing the pre-diffusion phase. Additionally, four validation interviews applying the research on green hydrogen have been performed to let external actors reflect. These validation interviews formed the practical quality gate forging a bridge to the earlier mentioned scientific quality gate.
Knowing the time point of large-scale diffusion of a radically new high-tech innovation is a highly relevant topic. Companies, researchers, and government institutions can plan their research and development efforts, production, as well as marketing plans according to the predicted time point of large-scale diffusion.
The research is based upon the assumption that specific indicators can predict the start of large-scale diffusion. The scientific field of forecasting the start of large-scale diffusion is relatively new. Therefore, an explorative methodology was required for this research. During the explorative process, it was ensured that indicators reflect on the holistic environment of an innovation by minding the so-called data collection cube.
A data selection funnel was created, narrowing scientific branches down to a list of indicators in three steps. Each of these steps has its own criteria designed to:
• Select scientific branches with the highest potential to find results in the literature reviews
• Derive indicators that can observe the diffusion
• Select indicators that can predict the start of large-scale diffusion The last step of the data selection funnel, selecting the indicators which can actually predict, was carried out with the support of three researchers. Eight criteria were used to select the most potential indicators: (i) Prediction, (ii) Timeliness of prediction, (iii) Availability of data, (iv) Cost of data, (v) Quantifiable& Objectivity, (vi) Empirical proof, (vii) Generalizability, (viii)Simplicity. The researchers were asked to evaluate the indicators according to the criteria (i) and (vi) as part of the scientific quality gate selecting the most potential indicators. After the indicators have been evaluated, a sensitivity analysis has been performed to improve the robustness of the selection mechanisms and to rule out an arbitrary selection of the indicators. Out of 50 indicators found in the literature or derived from the literature, 38 indicators were selected according to the selection mechanism. These 38 indicators have been split into two sets of judgemental and non-judgemental indicators to prepare the design of the forecasting approach. The forecasting approach aims to guide a user towards the correct forecasting technique given an innovation and situation. Five forecasting techniques were found to befitting the: (i) assumptions-based modelling, (ii) Delphi method, (iii)analogous forecasting, (iv) time series & regression models, and (v)artificial neural networks. However, each of the five forecasting techniques have disadvantages that can be overcome by one of the other methods. Hence, the forecasting approach has two stages. First, the user is guided towards the primary method and subsequently towards an additional method overcoming the disadvantages of the first method and improving the overall reliability of the forecast. For each method, a set of indicators is recommended. Once the forecasting approach has been developed, the completeness of indicators has been checked by using Ortt & Kamp’s 14 factors influencing the pre-diffusion phase. Additionally, four validation interviews applying the research on green hydrogen have been performed to let external actors reflect. These validation interviews formed the practical quality gate forging a bridge to the earlier mentioned scientific quality gate.