Bass Re-visited: Quantifying Multi-Finality

As forecasting the diffusion of new technologies results in unreliable predictions when only few trend data are available, scholars and practitioners alike favor foresight efforts of a more qualitative nature to gauge foreseeable futures. We argue that it is feasible to reconcile (quantitative) forecasting models with (qualitative) scenario development efforts by advancing a set of heuristics building on known diffusion curves. Rather than focusing on the ultimate set of parameter estimates, we explore a multi-dimensional space consisting of a wide range of values for each parameter implied. We demonstrate this approach for Battery Electric Vehicles in Europe and the US. Resulting outcomes are assessed in terms of how well they explain current observations by means of a loss function. The loss function allows us to evaluate the presence of different end states (multi-finality), their frequency of occurrence, and the implied time frames. The insights obtained inform foresight exercises in a number of distinctive ways. First, it becomes feasible to qualify the likelihood of - and the time horizon implied by - different scenarios. Second, allowing for multiple pathways and end states directs our attention to the antecedents required for different scenarios to unfold, which can inspire backcasting efforts of a more qualitative nature. Finally, applying the advanced logic on more fine-grained levels of analysis allows assessing the (differential) impact of policies oriented towards stimulating diffusion. As such, quantitative heuristics become a complement to scenario development exercises, rather than an inferior or even neglected substitute.