Increasing the Acceptance of Assistive Robots for Older People Through Marketing Strategies Based on Stakeholder Needs

Abstract

Modern assistive systems, such as robots, will have increasing relevance for support at home in the future due to changes in society, such as ageing. Older people, especially, can benefit from assistive robots that give physical, cognitive and emotional support. However, thus far, little is understood of how to increase currently low acceptance of assistive robots through marketing (DAA Design Brief, Acceptance of assistive technology, 2014). Therefore, marketing strategies need to be developed addressing needs and fears of the stakeholders, which is especially critical regarding utopian-appearing assistive robots. To understand what drives acceptance, conscious and subconscious needs, wants and barriers of use of the relevant stakeholders have to be analysed. As such, in this intelligence gathering process not only end-users should be integrated. Also other stakeholders (e.g. as users, decision makers and buyers might not be identical) should be identified and their needs understood. In this paper we report our findings on marketing factors for different stakeholder clusters for assistive robots that we identified during the EU-co-funded (FP7) Robot-Era project. We employed a user-centred way of identifying stakeholders and marketing strategies by analysing different stakeholders in an iterative design process from an early stage (Mollenkopf et al., AAL in der alternden Gesellschaft: Anforderungen, Akzeptanz und Perspektiven, 2010) with quantitative and qualitative methods. The most important acceptance factors we identified for assistive robotics include functionality, usability, safety, costs and financing, (non-) stigmatization and ethical aspects. The structure of the paper is the following: first we look at the relevance of assistive robotics and the challenge of missing acceptance. We then look at the 4p concept in marketing to structure our approach of user-centered marketing. We then describe our data collection and the results to end with a discussion.