Driver Assistant systems and their importance for older people - An empirical study with people age 50 and older as a basis for the development of new business models

Type de publication:

Conference Paper


Gerpisa colloquium, Paris (2018)


advanced driver assistant systems, business models, Germany, usefulness


People of the industrial nations are getting older. In Germany older people (here: people over the age of 50) account for more than 40% of the total population (Statistisches Bundesamt, 2011) and their share of the total population will continue to rise steeply up to the year 2030 (EU, 2015). At the same time, older people have never been so mobile as today (Schlag, 2013) and account for 43% of the people who have driving licenses (ADAC, 2010). For older generations mobility provides and represents independence, social participation, activity and an increased quality of life (Burghard, 2005).
However, for older road users the accomplishment of tasks in the daily traffic gets harder not only with increasing traffic density but due to their age-related physical and cognitive limitation, which are a risk to safe driving (cf., Engin et al., 2010; Dobbs & Schopflocher, 2010). Older drivers have increasing problems in complex traffic situations which demand speedy assimila-tion and processing of information as well as decisions and actions. However, they also have problems in twilight and in the dark, and in merging into traffic at high speed. There are normal-ly fewer restrictions on, e.g., pure visual acuity and more on dynamic visual performance, i.e. night vision, the width of the field of view and sensitivity to glare and contrast. Motoric skills also decline with age. Poorer joint mobility, e.g. impaired flexibility of the neck, means that old-er drivers find it more difficult to compensate for a weakened field of view (cf. e.g. MoPact 2014). In addition, an age-related diminution in performance affects cognitive functions (Mar-kowitsch et al. 2005), particularly decision making, problem-solving capability, cognitive flexi-bility, the recognition of rules and the processing of feedback (cf. Brand & Markowitsch, 2010)), as well as memory and the acquisition of new information (cf. Brand & Markowitsch, 2004). Finally, initial cognitive symptoms of dementia, which are diagnosed in approx. 16 per-cent of 70- to 89-year-olds (Petersen et al. 2010), e.g. clear limitations in the processing of in-formation received in parallel, often set in years before a diagnosis is made and unfitness to drive determined.
Therefore, suitable technical aids are needed that meet the physiological and economic require-ments in order to increase road safety for all road users (Rudinger, 2013). Technologies are available for this. Advanced driver assistant systems (e.g. CAR, 2011) already exist to help el-derly (and other) drivers (Wild, 2014), and that are continuously improved, e.g. adaptive cruise control, adaptive light system, attention assist, blind spot detection, cross traffic alert, cruise con-trol, emergency brake assist, intersection assistant, lane keep assist, night vision, parking assis-tant, park distance control, tire-pressure monitoring system, traffic jam assistant, traffic sign recognition. They can be seen as a step in the digital transition to autonomous driving (i.e. to level 5 "no driver) and reached level 3 of autonomous driving today ("eyes off" after "feet off" and "hands off", Abb. 1).

Fig. 1: Driver assistant systems on route to autonomous driving (source: according to VDA, 2015)

Although people aged 50 and older are "relatively well-off" and form a "very attractive and promising" "silver market" at least in developed countries (Kohlbacher & Herstatt, 2011), the success of many advanced driver assistant systems is surprisingly far below the expectations of the automotive industry (Winner & Schopper, 2015).
Nevertheless, age-specific or basically age-appropriate advanced driver assistant systems appear to be an interesting angle for new business models. Decisions on resource allocation in these systems, on the desired competitive advantages, value architecture and value proposition for the customer promise higher profits and are likely to have a positive influence on the profit model (on business models and their components, see e.g. Proff & Fojcik, 2014 or Markides 2015). In a first step, we therefore asked in Germany 369 older drivers over 50 years of age in outline about the perceived usefulness of today's driver assistant systems in the context of a project about age-appropriate advanced driver assistant systems which is supported by the German Federal Ministry of Education and Research (BMBF). The people were attracted by newspaper advertisements and asked to state their perception of the usefulness of these assistant systems on a scale from one (not at all useful) to seven (very useful). However, the survey shows no clear correlation between age, or rather membership of an age group, and the perceived usefulness of the individual driver assistant systems (cf. Fig. 2).

Fig. 2: Perceived usefulness of age-appropriate advanced driver assistant system for older German peo-ple by age group

One explanation of these results is offered by the research on critical life events which occur in the context of life course theory (e.g. Elder, 1994). It first makes an empirical study of the as-sumption of psycho-social causes of mental and physical illnesses. Critical life events, such as a home removal, marriage, change of job or death of close relatives, are considered as moderator variables (stressors) which contribute to psycho-physiological disorders (cf. Katschnig & Nouzek, 1988). Such life-changing events and the course of life described as a result are indi-vidual to each different person, since the events do not occur at a certain age (retirement, for example, can occur for early retirees around the age of 50 and for the self-employed at well over 70 years of age). Therefore, sociological life course research (Elder, 1994) attempts to describe aging effects along different dimensions, e.g. life events and life satisfaction, to form groups and then analyze intergroup and intragroup heterogeneity (cf. Lachman, 1985 and McLanahan & Sorensen, 1985).
Transferred to management issues, it can be assumed that (purchasing) behavior and perceived value to the customer change over the life course depending on certain critical life events. To be able to respond to this with age-specific offerings and orientate business models, the life events and life courses of individual consumers have to be aggregated and customer groups have to be identified. Initial studies already confirm that age, but also critical life events in age have an in-fluence on willingness to pay: King, Jr. et al. (2005) prove, for example, that older customers in the face of an impending end to life with rising income are more likely to be willing to pay for a year of quality of life. The Institute of Governmental Research (AKF) in Denmark (Hjelmar, 2011) confirms that "reflexive shopping practices can be sparked by life events" such as "hav-ing children".
To estimate whether and how driver assistant systems specifically offer the potential for new business models for older people after special life events, an examination will be made whether and when they offer profit potentials and create value to the customer. Here, individual customer groups within the total group of persons over 50 years old have to be identified, whom provid-ers of vehicles with driver assistant systems have to appeal to on a differentiated basis with their business models as well as estimating the size and economic potential of this group. Appropriate discriminant factors have to be identified for this purpose. For example, as in the classification of customer groups and lifestyles by the Gesellschaft für Konsumforschung (GfK) or the Sinus Institute, life events to date in relation to income/social class and age could be conceivable. This, in turn, entails the following:
" On the one hand, information from the 369 elderly survey respondents about their sex, edu-cational qualifications and net income, the distance they drive per day, but also about their knowledge of driver assistant systems and their experience have to be clustered to identify the key influencing factors, presumably income and age, as well as relationships between the influencing factors.
" On the other hand (critical) life events have to be identified and considered from the literature which may influence mobility behavior (cf. Müggenburg, 2017) and specifically the percep-tion of the benefit, but also the acceptance of and willingness to pay for driver assistant sys-tems.
By examining the perceived value, acceptance and willingness to pay for age-appropriate driver assistant systems, indications for the design of business models can ultimately be drawn: indica-tions for the design of the value proposition from the perceived value and indications for the revenue and therefore the profit model from the willingness to pay and market potential.

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