Paper short abstract:

In this paper we analyze visions on New Mobility Services (NMS) to research how the promises about user needs were constructed and what do they entail. We identify ambiguities in the envisioned expectations and reflects on how these influence applications of NMS in experimentation phases.

Paper long abstract:

R&D funding projects such as Horizon2020 are welcoming demand-driven types of innovations, which are presented in visions where they are promised to address user needs and values. However, the envisioned user needs are often defined using ambiguous terms like sustainability. Furthermore, the visions often lack specifications about technical solutions for applying the promises. In this paper, we first conceptually analyse the emerging narratives and promises in specific demand-driven innovations and second research what implications it has for experimentation with the envisioned innovations.

We look into smart mobility which is a demand-driven ICT based innovation expected to optimise mobility flows beyond traditional traffic and infrastructure solutions to also include 'softer' user mobility needs such as quality of life and ease of use. We focus on a New Mobility Services (NMS) niche, one of the prime examples of demand-driven smart mobility innovations, consisting of a variety of innovations relying on (traffic) information for delivering more efficient, safer and convenient mobility solutions.

Empirically, we focus on the NMS visions developed by the Dutch Ministry for Infrastructure and Environment. Conducting literature study and interviews we analyze how the promises about user needs were constructed and what do they entail, including expectations about their applications. Conducting interviews with NMS actors in the Netherlands and analysing a database of experiments, we explore how the promises and expectations about user needs are applied in experimentation phases. We conclude by answering how the ambiguities embedded in the promises influence innovation processes and we draw implications for decision-makers.