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An examination of consumer demand in the secondary niche market for fuel cell vehicles in Europe
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International Journal of Hydrogen Energy 28 (2003) 771 – 780 www.elsevier.com/locate/ijhydene
An examination of consumer demand in the secondary niche market for fuel cell vehicles in Europe K.-A. Adamson∗ Fuel Cell and Hydrogen Research Centre, Technical University of Berlin, Energiesysteme Sekr TA8, Einsteinufer 25, 10587 Berlin, Germany
Abstract If fuel cell vehicles (FCVs) are to reach the mass market they must -rst be adopted into and pass through the niche market phase. The term niche market has, in the current study, been separated into two di1erent levels, here termed as primary and secondary niche markets. Both of these will be critical for FCVs and are examined in the paper. These two niche markets provide time for the mass market to see the product and build up a mass market pull, for the technological trajectory to start, and, time where the economics of the product are not the overriding concern. This paper gives an overview of the framework of adoption within the two niche markets and then concentrates on the second of these niche markets and its implications for FCVs. It is within the secondary niche market that the FCV will -rst come into direct competition with the internal combustion engine vehicle, and it is here that its utility, or usefulness, will be assessed by the adopter. The secondary niche market, unlike the mass market, does not operate under conditions of constrained utility maximisation, but are prepared to pay a premium of adoption. Modelling of the premium that the secondary niche market adopters would be prepared to pay today for FCVs provides us with an indication of how much further, technologically, they need to be before they are consumer ready. ? 2003 International Association for Hydrogen Energy. Published by Elsevier Science Ltd. All rights reserved. Keywords: Niche markets; Adoption; Fuel cell vehicles
1. Introduction If fuel cell vehicles (FCVs) are to be a success in the market place they must -rst contend with the statistic that states that the probability of success of a new product in an open market place lies between 1% and 30% [1,2]. Though this -gure provides stark warning to new products there exists in the literature a ‘pro-innovation bias’ [3], in which the majority of research is carried out using post ante data and information from examples of successful innovations. Though, through the expansion of innovation and di1usion research, there is a greater understanding of the adoption and di1usion of successful ‘evolutionary’ innovations Ayres [4] points to a further lack of understand
and theorisation concerning discontinuities. Technological discontinuities occur only periodically but their impact, if adopted, on the socio-economics is much stronger. Examples of these discontinuities and their impacts are the radio and telephone which enabled society to ‘feel’ closer together [5] and the transistor which jump started the computer revolution, with computers now in 80 million homes in Western Europe [6]. These examples highlight that the impacts are not only quantitative, such as the number of computers in homes but also subjective, for example feelings in society towards technology. 2. Market launch of a discontinuous product 2.1. A note on terminology: discontinuities
∗
Tel.: +49-30-314-79123; fax: +49-30-314-26908. E-mail address: [email protected] (K.-A. Adamson).
Though the two words, product and technology are often used interchangeably it is important to separate the two, the consumer buys a product which is usually a combinations of
0360-3199/03/$ 30.00 ? 2003 International Association for Hydrogen Energy. Published by Elsevier Science Ltd. All rights reserved. PII: S 0 3 6 0 - 3 1 9 9 ( 0 2 ) 0 0 2 4 6 - X
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technologies. Similarly it is important to understand when discussing discontinuities what the de-nition being used is. The word ‘discontinuity’ means at some level a dramatic change, a paradigm shift. To complicate the issue slightly there are three words that are sometimes used in the debate with similar meanings, these are disruptive and revolutionary, as well as discontinuous. The choice of which word to use can depend on the study in progress or sometimes simply the message to be conveyed. Ehrnberg’s [7] paper which categorises the range of de-nitions of discontinuities describes three common classes of de-nition: competence change, physical product or process change, or change in the price or performance level. These classi-cations and de-nitions that are provided are useful when dealing with management of discontinuities but as the focus of this paper is the consumer another de-nition is used. This paper adopts the de-nition of discontinuous products as being: Products that, through the use of new technologies, create within the user group a paradigm shift in beliefs, attitudes and use As can be seen from this the de-nition refocuses on the user group of the technology, and is adapted from Kuhn [8], in his work on paradigm shifts in scienti-c theory. This de-nition is also similar to that adopted by Mackay and Metcalfe [9] on their paper on forecasting methodology for discontinuous innovations (DIs) which states that DIs are ‘products or services that shift market structure, represent new technologies, require consumer learning and induce behavioural change’. The importance of this refocus is that this paper, and overall research, is based on examining why discontinuous products are adopted and di1used within a user group, and what are the impacts of adoption. The result of adoption and diffusion of any discontinuous product therefore by de-nition sees a change in society. Changes not just in the pattern of the work force and GDP but also in the way society interacts with each other and with the product. These socio-economic impacts of product adoption and di1usion is another critical area of research where, until recently, there has existed a bias that implies that adoption of new products, and or, technologies will result in positive impacts. As Rogers [3] pointed though there are a number of cases where the results from adoption of a product has had severe negative impacts on society, to the point where the product had to be actively ‘deadopted’. Now though with the debate over for example, gene manipulation this assumption is being actively questioned and we may see this bias weaken substantially, if not be neutralised. 2.1.1. Fuel cells and fuel cell vehicles Fuel cells are a potential discontinuous technology, of which FCVs are a discontinuous product. They are a discontinuity in that they o1er a window of opportunity to change the relationship between the user, the energy providers and
automotive industry. Also they could o1er the market place new attributes on the vehicle that could change the relationship between the consumer and the vehicle. Fuel cells have the potential to form the basis of a number of discontinuous products, each of which needs to be adopted as such. In this then fuel cells could be classed as a new ‘core technology’ [10]. Core technologies are technologies which have the potential to be incorporated into a number of current and new products. By introducing a range of di1erent products the resistance to change in the market may be decreased as the consumers become accustomed to what fuel cells are, but they still adopt the di1erent products not the technology. 2.2. Demand pull, market push Product acceptance is dependent on market push and demand pull. Which is more important, market push, demand pull, is an open question with a number of research papers stating that, in di1erent industries, one or the other is more important [see for example [11–13]]. What we do know is that for the product to be a success we need both, a demand pull from the consumer as well as market push for the manufacturer. In an idealised system the process of adoption of many technologies and products, evolutionary and discontinuous, is shown from empirical research to follow an S-curve. During the S-curve adoption starts o1 slow, over time speeds up, reaches a point of inLection, from which adoption slows down again till it reaches market saturation point. The main problem with this picture is that it is idealised. In reality the crossover point from Stage 1, niche markets, to Stage 2, mass market, is sometimes referred to as either ‘Death Valley’ or ‘Chasm of Commercialism’, as this is where most new products fail. This point will be returned to later. The process of adoption therefore has three potential pathways: 1. Non-adoption—products fail, either at the crossover from niche to mass market, or to reach niche market level successfully. 2. Deadoption—when the process of di1usion is halted, usually through government intervention, and reversed. 3. Adoption and di1usion—S-curve adoption and di1usion pattern, referred to as the norm.
3. Framework of adoption of discontinuous products This overall framework of adoption of discontinuous products is being developed during the current research and is based on the di1usion norm of the S-curve. Work on adopter categories has been merged with that of Rogers to form an initial picture of the path FCVs may follow to reach the mass market. The overview of the framework
K.-A. Adamson / International Journal of Hydrogen Energy 28 (2003) 771 – 780
of adoption that is presented here therefore is still under development. The current study separates the term niche market into 2 levels, primary and secondary niche market and then splits these levels further into the adopter groups, societal niche, and reason for adoption, technological niche. Both of these niche markets and their subsets need to be addressed before the technology reaches the boundary to the mass market. In terms of S-curve adoption therefore we expand the path to: Innovation →
Invention →
Primary Niche Market
3.1. Niche market adoption Niche markets are small protected markets in which a new product upon entry can start upon a technological trajectory → Secondary Niche Market → Mass Market
Societal Niche Technological Niche
and in terms of market demand we have three adopter categories, primary niche market adopters, secondary niche market adopters and mass market adopters. Modelling market pull for discontinuous products: DPt = xPNMAt + ySNMAt + zMMt − t ;
The rest of this paper concentrates on the niche market adoption, speci-cally on the secondary niche market, but will -rst discuss the critical di1erences between the two niches.
Societal Niche Technological Niche
773
(1)
where x; y; z is the weighting of importance; the resistance to change, and t the time. This equation states that the overall level of demand pull for a product, in time t, is a cumulative function of the primary niche market adopters, secondary niche market adopters and the mass market. The level of importance of each of these adopter groups, to the overall demand pull, is shown in the weightings of importance. At di1erent times within the adoption process the value of the demand pull changes and the values in the three di1erent adopter groups change. Demand pull also has a weighting indicating that in the overall change to a new product demand pull is only one function (government intervention and market push are the other two that have been mentioned here). The resistance to change is a feeling in society of why switch to a new product when the old one does the job satisfactorily. For example the market sees, in advertisements, that the current internal combustion engine vehicle (ICE) is becoming cleaner, that tailpipe emissions are being reduced and fuel consumption is decreasing. The impact of this is to create a level of belief in the market that the ICE will continue to get better and that there is no need to switch to a unknown new technology to answer such problems as emissions and climate change. This level of resistance to change is not static but changes over time with learning and network e1ects. This resistance to change has been shown in management literature [14,15] and papers on discontinuities [16,17]. The impact of this is that the new product needs to be more than better than the current one for adoption to take place, the level of resistance indicating how much better. If we ignore this issue we run the risk of, potentially seriously, overestimating the market pull for a new product.
of learning by doing, economies of scale and to begin the creation of network e1ects. It is these niche markets that are easier for a new product to enter before it attempts to enter the mass market. 3.2. Primary societal and technological niche market In the primary niche the new product enters because it provides a function that cannot be replicated by any other product on the market place—the technological niche. The group—the societal niche—that adopts it places such a high economic value on the new function that it overrides the issue of the high adoption costs associated with adopting a brand new product. An example adopter group of this type was the scienti-c community, and the technological niche that was being -lled was by the -rst computers. These leviathans were created and adopted by the scienti-c community to perform speci-c computational functions that it was deemed impossible for human ‘computers’ to perform [18]. The adoption of these computers by the scienti-c community was also a good example of a failure to recognise the future potential of a technology. It was famously assumed that only a very small number of room size computers would ever be needed to handle all the computational requirements by universities, and that there would be no other use for computers [18]. This is not to say that economics did not play a part in the adoption, but that the economic value of this new function, the computational ability, was high enough to ensure adoption by this particular group. This example of the -rst niche market, into which the product is adopted on its new functions, highlights the importance of the ‘X-factor’, the function that cannot be replicated by the current market, in discontinuous product adoption. It is this initial X-factor that overrides the high adoption costs, this new function has to have a high enough economic value to the primary niche market adopters. Therefore whatever function that FCVs vehicles produce that an ICE with petrol or diesel cannot needs to be identi-ed and ‘sold’ to the societal group which places the highest economic value on this function.
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3.3. Secondary niche market Once this initial niche market is saturated the product moves into a further larger niche market. Though this is again a niche market, in that it is size restricted, the new product here starts to compete directly with the product that is already on the market. In this niche the secondary niche adopter compares the utility provided by each of the competing products, and the product with the highest subjective utility gets adopted. This adoption is not based on price but utility maximisation. To put this another way, the product that is the most subjectively useful is adopted. As this niche is the subject of the rest of this paper it will be expanded on in much greater detail in the following sections. The di1erence between the mass market and the secondary niche market is that the mass market works under constrained utility maximisation, the constraint normally being budget, whilst the secondary niche market works under utility maximisation but are willing to pay an adoption premium. The framework that is under development, around the process of adoption for discontinuous technologies, has a number of critical points which are listed here in short form: • Though the societal and technological markets are not independent they both must be addressed by a new product. • Often two or more sibling technologies 1 are launched at the same time, or within a short time period of each other. • Initially these sibling technologies compete with each other in the primary societal and technological niche market. When this niche is full and it is time to move into the secondary niche phase there is generally one product with a substantial market lead. • By the time the product enters the secondary niche market the sibling technology with the market lead de-nes the product and its attributes. • The primary technological market niche is by de-nition a protected space because it is entered by the product though a function that the incumbent product cannot produce. This X-factor therefore creates the space within the niche for the new products. • The primary societal niche market therefore is a group of consumers who -nd this extra function that the new product o1ers attractive enough to invest in. • In the secondary niche market phase the new technology starts to compete with the incumbent technology on its attributes performance. These competing attributes are judged by the secondary societal niche market and the technology with the subjective highest utility, provided by these attributes, gets adopted.
• If the new technology makes it through the secondary market niche it is then attempts to enter the mass market. • The paradigm shift to the new product takes place during this cross over from niche to mass market product.
4. Adoption of fuel cell vehicles by the secondary niche market The usefulness of modelling FCV adoption parameters is that once complete these can used to analyse where the technical boundaries are to adoption by this group. For example we know that to make it technically viable, the fuel cell had to have a minimum output of 65 kW, but, does the consumer also require a minimum output of 65 kW, or is this higher? Also as the ICE represents a moving target for the FCV we can use this model to see how much better the FCV needs to be overall to ‘keep up’. As already discussed the adoption of the product in this niche is based on utility maximisation and is not constrained by a budget. In other words, this group are prepared to pay a premium of adoption for the product with the highest subjective usefulness. The issue of secondary niche market adoption of FCVs can be subsplit into two questions: 1. Probabilities of adoption where adoption is based on utility maximisation. 2. Calculation of the premium for adoption would be prepared to pay on adoption. 4.1. Utility maximisation and FCV adoption In this niche market it is assumed that the potential adopters are faced by the choice of staying within the known technology of the internal combustion engine or adopting the new technology of a FCV. The consumer decision to adopt is based on the cumulative utility of the attributes represented in the incumbent product as compared with that of the incoming product. Here the X-factor does not come into the decision making process as the attributes under consideration are those already o1ered by the incumbent technology. The relationship of this decision process therefore can be formally written as Probability of adoption of discontinuous product based on utility maximisation: Tt = U1t + U2t + U3t + · · · Ukt ;
1 Sibling Technologies: technologies that come from the same family of products, provide some of the same functions but have di1ering attributes. An example of sibling technologies is the VHS and Betamax video systems. They both provided the function of recording onto video but had di1erent attributes such as size of tape.
(2)
where T is the technology, ICE or FCV; the Adoption of technology T in time t; Ukt the Utility of attribute k in time t; k the attribute; and the weighting, this represents that fact that the consumer places di1erent levels of importance on di1erent attributes.
K.-A. Adamson / International Journal of Hydrogen Energy 28 (2003) 771 – 780
In terms of adoption of a FCV over an ICE therefore the equation would look like: Probability of adoption of fuel cell vehicle based on utility maximisation: Tt = Upower + Utorque + Uacceleration + Ufuel economy + · · · Uk :
(3)
Decision procedure for adoption of fuel cell vehicle: UkFCV ⇒ ICE UkICE ¿
UkICE ¡
UkFCV ⇒ FCV :
(4)
It is assumed that in the niche market as well as the mass market adoption process there is a resistance to change; here represented by the utility provided by the FCV needing to higher, and not just equal to, that of the ICE before adoption takes place. It is important here that we clarify what assumptions, regarding the weightings, have been made. Publicly available surveys on purchase decisions by consumers, such as Claritas [19] use stated preference techniques to elicit from a range of consumers what is important to them in a decision purchase. Di1erent surveys split the consumers up in di1erent ways, such as banded by income level, or type of vehicle intending to purchase. Because these survey’s use importance criteria for existing vehicles we can use these stated preferences when calculating the probability of adoption of an FCV, but we need to make clear how we are splitting the consumers. In this study the assumption that is made regarding attribute importance level is: Across the market in Europe the rating of importance of attributes in vehicle purchase choice is dependent on vehicle class, not consumer segment. What this is the saying is that both the mass market and the secondary niche market both rate power, safety, etc., the same but these levels of importance are di1erent between vehicle classes. Using the Claritas survey Eq. (2), for compact vehicles, such as the A Class Mercedes, becomes: Weightings of importance in vehicle adoption decisions: T = 2:88Ureliability + 2:72Usafety + 2:68Uprice +2:58Urunning costs + 2:48Usecurity + 2:42Uperformance +2:23Ucustomer service + 2:23Uprevious experience +2:18Uoverall size + 2:17Uenv:friendly + 1:95Uspecs +1:73Uimgae :
(5)
A number of points need discussed from this equation: 1. There are a number of attributes such as customer service, image and security that are not directly measurable. How do we class and measure customer service? As with utility these attributes are to all intense and purposes unit less,
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but we can make some assumptions or extrapolations from these. What needs remembering is that this study is an example of ‘what happens if: : :’. The results from the work are not to produce the adoption curve but to produce a range of adoption curves based on varying di1erent input variables. So if we assume that a current ICE has a safety and customer service of 1, we can model the FCV having a safety level of double, 2, half, 0.5, and see how this impacts the overall level of utility and adoption. 2. Previous experience—from this we would assume that this is previous experience with a particular model of car, such as a Ford Focus and not previous experience with a diesel engine or a LPG vehicle. This returns to the point that consumers purchase vehicles, not drive trains. So if we have an owner of a Ford Focus looking into purchasing a Ford Focus FCV they are still purchasing a Ford Focus, it is the attributes that this new Focus FCV provides that are important. Using this variable we can see the impact of vehicles such as the GM Motion which is a new optimised FCV concept vehicle, of which there is no previous experience. 3. Using this equation we must remember that utility has no units of measurement so instead of using overall -gures, such as 65 kw Power, we use incremental increases and decreases from the level of a comparable ICE for each utility measurement. It is important to remember that though the weightings for di1erent countries in Europe may be similar to the weightings from consumers in America will be di1erent. 4.2. Calculation of the adoption premium The method by which the secondary niche adopters adoption premium is being modelled uses the revealed preference Hedonics technique. This technique works on the theory that the basic price of a product is a combination of the costs of a set of identi-able attributes, with each attribute forming a percentage of the price [20]. For example, these attributes for cars would include horsepower, size and torque. So when di1erent products exhibit di1erent levels of these attributes then there is a price di1erential. The further assumption is then made in that as the consumer purchases these vehicles with the price di1erentials they are willing to pay this amount for the varying levels of attributes. In an ideal situation we would have focused sales data on how many of each type of vehicle, not just class and model, is sold but due to commercial diSculties we have to work around this with just the publicly available list prices. Using regression analysis we can extract from vehicle list price data the ‘price’ associated with each attribute so the basic regression equation is of the form: Base regression equation for calculating premium of adoption: Pit = + 1it + 2it + · · · kit + ;
(6)
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260 240
km/s
220 200 180 160 140 120 0
20000 40000 60000 80000 100000 120000 list price (€)
Fig. 2. Top speed as a function of list price.
25
Fig. 1. Vehicle attributes related to list price.
23 21
4.2.1. List price and vehicle attributes What can be seen from Fig. 1, which shows the list price of a vehicle as a summation of the prices of a range of attributes, is that a number of the attributes are direct combinations of others. In regression analysis this causes a high level of mutilcolliniarity resulting in instability in the results. The solution to the issue, that was used here, was to split the premium into 2 sections: willingness to pay for technical
19 km/l
where is the intercept; kit the price of attribute k in vehicle i at time t; and the error term. Because to perform a regression analysis on singular vehicles, or ranges of models, cannot with any level of certainty produce robust results the vehicles that were used in the regression analysis were done so based on vehicle class. The three classes that were modelled were subcompact/microminis, for example the Smart, compact, such as the Ford Focus and the luxury/executive class, such as the BMW 7 Series. The reason behind these choices is that most of the FCV prototypes are based on the platforms of subcompact and compact vehicles, and BMW’s hydrogen vehicles are based on their executive and micromini vehicles. In total 760 vehicles from the build year 2001 were included in the regression. This initial point in time study, build year 2001, is to look at the di1erence between today’s FCV prototypes and the Consumer Premium that would be paid today. This allows us to gauge an idea of how much further the technological development must proceed to meet this premium. Extrapolation of future premiums will require the dataset to be expanded backward over time to include information from a range of years to allow a measurement of how the attributes prices have changed. This would then allow extrapolation into the near term future of potential attribute prices and therefore premiums.
17 15 13 11 9 7 5 0
20000 40000 60000 80000 100000 120000 list price (€)
Fig. 3. Fuel consumption as a function of list price.
attributes, such as power and torque, and willingness to pay for reduced running costs. Here it was assumed that running costs are a summation of fuel costs and carbon dioxide based tax. This carbon dioxide based tax is included as Eq. (3) highlights that the altruistic environmentally friendliness of a vehicle is not intrinsically an important adoption criteria as running costs are. 4.3. Results from technical premium Figs. 2–4, show that what we know logically bears out in the results. Attributes such as acceleration (derived mostly from the vehicles torque) and top speed (derived from the power) increase with vehicle price whilst fuel economy drastically decreases. We can extrapolate this to hypothesis that the running costs in the subcompact and compact classes are of far higher importance than in the executive class, whilst power and torque are more critical to the executive class. Initially the regression was performed on the data using the same attributes for each class. These where automatic
K.-A. Adamson / International Journal of Hydrogen Energy 28 (2003) 771 – 780
19 17
m/s
15 13 11 9 7 5 0
20000 40000 60000 80000 100000 120000 list price (€)
Fig. 4. Acceleration measured in 0 – 60 as a function of list price.
gearbox, brand quality, measured using a ADAC (German Automobile Association) survey, power, torque and wheel base. The impact on price of the presence of an automatic gearbox was modelled as a proxy for FCVs not having a gearbox. The Brand Quality attribute has the inbuilt assumption that all vehicles made by the same manufacturer exhibit the same quality level. Using the t statistic distribution and relevant degrees of freedom the 95% con-dence intervals were calculated. At the 95% con-dence level the probability (p) that the null hypothesis 2 is correct is over 0.05. In other words the higher the p value the less con-dence we can place in the attribute being a statistically signi-cant component of the list price. The results where that in each of the vehicle categories di1erent attributes fail at the 95% con-dence interval. Taking the ‘all together’ vehicle category -rst that was a high R2 value of 0.87, indicating that 87% of the price can be explained by the modelled attributes. The fuel attribute (0 for petrol, 1 for diesel) has a very high distribution spread and p value, so can be dropped from the analysis. All the other attributes have much closer con-dence intervals and signi-cantly low, ¿ 0:01; p values, indicating high levels of con-dence. For the category of subcompact the R2 is the lowest of the four at only 0.69 indicating that within this category over 30% of the list price comes from non-modelled attributes. It was interesting here that brand quality has a very high p value indicating that the null hypothesis here is correct. This though could be more to do with the low spread of brands in the vehicle section, though there were 189 vehicles modelled they came from only a range of 15 models. What was of slight concern is that two other attributes, torque and fuel, also fail at the 95% con-dence level. Fuel, here petrol or diesel, can to a degree indicate fuel eSciency. For the subcompact vehicles the petrol vehicles had an average fuel eSciency of 16 km=l whilst the diesel’s was 21 km=l. 2 Here the null hypothesis is that the attribute under consideration has no statistical relationship with the list price of the vehicle.
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Because of the assumption that fuel eSciency would be critical in this category the failure of fuel to pass the 95% con-dence interval is of concern. The con-dence intervals for all three of these attributes switch from a positive, indicating that the attribute adds ’s to the price, to negative, the attributes lowers the price. For the second round of regressions power was removed from the subcompact division but torque and fuel remained in to see if these inconsistencies could be removed. Again this statistical deviations could be a result of the smaller dataset used in this vehicle group. The compact class has the greatest range of vehicles, some 357, and the results from this class appear to the most statistically sound. Only one of the initial modelled attributed failed at the 95% signi-cant level, that interestingly of wheel base. This could indicate that the price of a small vehicle is made of attributes that are linked to the size of the vehicle and engine but not intrinsically the size of the vehicle itself. The luxury/executive segment, like the subcompact, represent smaller vehicle markets than the middle the range compact so again had a smaller number of data points from which to compile the model. Taking this into account, the main result from the initial regression was intuitively contradictive with evidence that the power of the vehicle is not only statistically important, but also that is has a negative sign. This indicates that with a power increase comes a price decrease! Fuel, which is linked to power, in that diesel engines tend not to be as powerful (there are only 29 diesel vehicles in this group) seems to represent a disproportionally high value, some 9230 drop in price for a diesel, as compared with a petrol vehicle. Even though fuel passed the test on statistical signi-cance it was decided to drop this in the focused round of regressions and see what impact that had on the power attribute. Table 1 produces the results from the focused regressions which have removed a number of attributes from the di1erent classes that had high spreads in the con-dence intervals or did not reach the 95% level of signi-cance. The -rst thing to note about Table 1 is that none of the R2 goodness of -t values have changed, which indicates again that the other included attributes did not contribute to the regression. Secondly, by removing a small number of the variables in each of the di1erent sections, all the attributes that remain pass the test of the 95% con-dence interval, including the worrisome fuel in the subcompact section. Now we can look at the di1erent attributes values. Fuel, a potential proxy for the level of fuel eSciency in the vehicle is very important in the subcompact and compact class with a diesel vehicle being worth 686 and 730 ’s more respectively than an equivalent petrol vehicle. In terms of the impact of FCVs therefore we can hypothesis here that the consumer would be willing to pay both in the technical attributes and the running costs for extra fuel eSciency in the smaller vehicles. In the executive class where an increase to diesel saw a drop in power the fuel variable has been dropped with the impact of making the power variable highly signi-cant. It is interesting to note that the top value for power is located in
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Table 1 Focused regressions of vehicle attributes Variable
CoeScient
+ 95% CI
−95% CI
t-statistic
Probability
sAll together: = 0:87; 754 degrees of freedom Automatic gearbox 2830.36 Brand quality 390.32 Power 93.96 Torque 48.62 Wheel base 0.00033
3634.63 479.21 103.51 57.31 0.00053
2026.09 301.42 84.41 39.92 0.00013
6.898 8.606 19.292 10.960 3.277
0.000 0.000 0.000 0.000 0.001
Subcompact: R2 = 0:69; 184 degrees of freedom Automatic gearbox 1105.27 Power 64.13 Wheel base 0.000257 Fuel 686.07
1529.84 70.81 0.00048 1154.27
680.69 57.44 0.00004 217.88
5.102 18.807 2.286 2.872
0.000 0.000 0.023 0.005
Compact: R2 = 0:76; 348 degrees of freedom Automatic gearbox 1479.71 Brand quality 517.72 Power 54.45 Torque 12.91 Fuel 730.21
1935.00 565.90 63.45 18.55 1223.51
1024.43 469.54 45.45 7.28 236.91
6.370 21.061 11.853 4.492 2.901
0.000 0.000 0.000 0.000 0.004
Executive: R2 = 0:80; 209 degrees of freedom Automatic gearbox 3365.57 Brand quality 393.21 Power 46.57 Torque 107.64 Wheel base 0.00057
5539.90 676.51 69.08 130.90 0.00096
1191.24 109.91 24.05 84.37 0.00018
3.034 2.7201 4.054 9.068 2.864
0.003 0.007 0.000 0.000 0.005
R2
the subcompact vehicle, not as might have expected in the executive class. But the executive class has the highest R2 value indicating that overall power and the other four variables measured have the greatest importance. In none of the vehicle classes modelled was the presence of an automatic gearbox a price negative, a bene-t for FCVs. Torque, which is related to acceleration, is critical in the executive class with a value of slightly over double that of power. In terms of equations we can write the following, including the values for the intercept and residuals, which have not been included in the tables: The price of subcompact vehicle as a summation of its component parts: = 1105 × automatic gearbox + 64 × power +686 × fuel type + 0:000257wheelbase + 3800:
(7)
The price of compact vehicle as a summation of its component parts: = 1480 × automatic gearbox + 54 × power +730 × fuel type + 13 × torque +518 × brand quality + 2900:
(8)
The price of compact vehicle as a summation of its component parts: = 3366 × automatic gearbox + 47 × power +0:000567 × wheelbase + 108 × torque +393 × brand quality − 14153:
(9)
Bearing in mind that these -gures are from the build year 2001 we can calculate the -rst half of the premium, the technical premium, for prototype FCVs. The calculation based on the Ford Focus FCV prototype realises a value of 16; 378 , whilst the same calculation on the NECAR 5 produces 16; 916 . The positive result from this is that on the technical premium alone, not taking into account the running costs premium, the values from the FCV prototype already met or nearly equal the current list prices of 16; 371 and 20; 000 , respectively. Working on the assumption that before these vehicles enter the secondary niche market they will improve technically, then potentially there will be a substantial premium on these vehicles, but, this is dependent not just on the rate of improvement of the FCV but critically also on the rate of improvement of the internal combustion engine.
K.-A. Adamson / International Journal of Hydrogen Energy 28 (2003) 771 – 780
5. Discussion and conclusions For market launch of FCVs this paper has highlighted a number of critical points. The product will have to address three di1erent adopter groups with di1erent requirements from the product. The -rst, the primary niche market adopters will require that the FCV will provide a new function that has a suSciently high economic value to overcome the costs of adoption. The second, the secondary niche market adopters will require that the subjective usefulness of the FCV is greater than that of the current vehicle technology, but at the point of adoption this group is prepared to pay a premium for adoption. To enter the mass market the FCV will have to develop, during the niche market phases, a pull from the mass market as well as bringing down costs, due to adoption being constrained by budget. In terms of economic performance of FCVs therefore there is potentially a ‘breathing space’ in which the adoption of these vehicles is not based on a constrained economic decision. But, in terms of technological performance the FCV must continue to improve at least at the same rate of change of the current technology. Also the unique function that a FCV can produce, if it is to be a discontinuous product, needs to be identi-ed and marketed now. From the regression results, on the technical premium that the secondary niche market adopters would be willing to pay, there appears to be an interesting trade o1 between cost and performance in the di1erent vehicle segments. The fuel choice, in subcompact and compact vehicles, is of high importance. If it is assumed that this is a proxy for fuel eSciency, that the consumer has no intrinsic desire for diesel above petrol, then the bene-ts that FCVs could offer on fuel eSciency need to be marketed in these vehicle segments. But these are the vehicle segments that have the lowest list price. The excellent torque and acceleration of these vehicles are of minimal importance in these vehicle classes but critical in the luxury/executive vehicle groups, which also are a much higher price. So if the fuel cell power train is promoted through the executive class vehicle, not only can it be bigger due to an increase in size being statistically signi-cant in this group, but it can be promoted on performance. If though it is continued to be promoted as being a move towards sustainable transport and the environmentally friendliness of increased fuel eSciency then it needs to be marketed in the smaller vehicle categories, but this implies stricter cost targets. Instead of discussing statistical values and frameworks of adoption what the research has shown so far can be best illustrated in a short example path to market for FCVs. 6. Example path to mass market for fuel cell vehicles based on the framework of adoption At the launch of fuel cell vehicles three sibling technologies are launched. These are fuel cell vehicles using
779
direct hydrogen, FCVs using an onboard reformer and direct methanol FCVs. Each of these FCVs is marketed to di1erent societal groups, which have di1erent requirements from the product. Initially the vehicles are launched into the compact vehicle segments only, as second or third vehicles for the adopters. Through a number of small competitive advantages, stemming from network e1ects such as fuel availability and localised legislation, hydrogen FCVs start to dominate the market. At this point it is critical that there are no hydrogen related accidents to stigmatise the fuel and product. The initial rush of manufacturers to place technologies on the market slows down and more partnerships are created towards standardisation of the product and refuelling stations. A large number of the smaller manufacturers are either bought out by their rivals or go out of business. The three vehicle types move to the secondary niche market with hydrogen exhibiting a strong lead, methanol and petrol’s market share already starting to decrease. By now the attributes of what a FCV, the standardisation of the product, is de-ned by the attributes of a direct hydrogen FCV. In the secondary niche market executive class vehicles are released alongside the now well known compact FCVs. The usefulness of the vehicles in terms of high fuel eSciency and excellent torque are promoted to the secondary niche market adopters. Because of the slowing down of the rate of change of the ICE the FCV has been able to surpass it on technical qualities and, has, with its lower running costs, a higher subjective utility. The impact of adoption in both the niche markets is to reduce costs, through learning e1ects, improvements in the technology and standardisation. By the time the FCV reaches the border to the mass market the hydrogen FCV is the only FCV left in the market. Government incentives and tax breaks ensure that the last remaining hurdle of economics is not insurmountable and the FCV attempts to cross ‘death valley’. By now it is no longer a product for limited groups and there is a ground swell of desire for the product, and at this point that the FCV starts to seriously erode the market shares of the ICE vehicle. It is only now that the environmental bene-ts of the FCV are marketed to the consumer. This is one potential short qualitative pathway to market for FCVs. Once the model is complete we will be able to construct a range of quantitative pathways using a range of di1erent assumptions.
References [1] Marchetti C. Branching out into the Universe. In: Nakicenovic N, GrVubler A, editors. Di1usion of technologies and social behaviour. Berlin: Springer, 1991. [2] Mahajan V, Muller E, Bass FM. New product di1usion models in marketing: a review and directions for research. In: Nakicenovic N, GrVubler A, editors. Di1usion of technologies and social behaviour. Berlin: Springer, 1991.
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[3] Rogers EM. Di1usion of Innovations, 4th ed. New York: Free Press, 1995. [4] Ayres R. On forecasting discontinuities, technological forecasting and social change 2000;65:81–97. [5] De Sola Pool I. The social impact of the telephone. Cambridge: MIT, 1977. [6] Computer Industry Almanac press release, available at www.c-i-a.com/pr0302.htm [7] Ehrnberg E. On the de-nition and measurement of technological discontinuities. Technovation 1995;15(7):437–542. [8] Kuhn T. On the structure of scienti-c revolutions. Chicago: The University of Chicago Press, 1972. [9] Mackay MM, Metcalfe M. Multiple methods forecasts for discontinuous innovations. Technol Forecast Soc Change 2002;69:221–32. [10] RHpke I. New technology in everyday life—social processes and environmental impact. Ecol Econ 2001;38:403–22. [11] Norberg-Bohn V. Creating incentives for environmentally enhancing technological change: lessons from 30 years of US energy technology policy. Technol Forecast Soc Change 2000;65:211–34. [12] Chidamber SR, Kon HB. A research retrospective of innovation inception and success—the technology push, demand pull question. Int J Technol Manage 1994;9(1):94–112.
[13] Walsh V. Invention and innovation in the chemical industry: demand pull or discovery push. Res Policy 1984;13:211–34. [14] Macri DM, Tagliaventi MR, Bertolotti F. A grounded theory for resistance to change in a small organisation. J Org Change Manage 2002;15(3):292–310. [15] Gersick CJG. Revolutionary change theories: a multilevel exploration of the punctuated equilibrium paradigm. Acad Manage Rev 1991;16(1):10–35. [16] Loch CH, Huberman BA. A punctuated equilibrium model of technology di1usion. Manage Sci 1999;45(2):160–77. [17] Tushman ML, Anderson P. Technological discontinuities and organisational environments. Admin Sci Q 1986;31: 439–65. [18] Ceruzzi P. An unforeseen revolution: computers and expectations 1935 –1985. In: Teich AH, editor. Technology and the future. New York: American Association for the Advancement of Science, 1993. [19] Claritas Automotive Report. Understanding the Motivational InLuencing and Loyalty Factors of GB Car Buyers. Claritas, London, 2001. [20] Gillriches Z. Hedonic price indexes for automobiles: an econometric analysis of quality change. in: Price Statistics of the Federal Government. New York: National Bureau of Economic Research, 1961.
International Journal of Hydrogen Energy 28 (2003) 771 – 780 www.elsevier.com/locate/ijhydene
An examination of consumer demand in the secondary niche market for fuel cell vehicles in Europe K.-A. Adamson∗ Fuel Cell and Hydrogen Research Centre, Technical University of Berlin, Energiesysteme Sekr TA8, Einsteinufer 25, 10587 Berlin, Germany
Abstract If fuel cell vehicles (FCVs) are to reach the mass market they must -rst be adopted into and pass through the niche market phase. The term niche market has, in the current study, been separated into two di1erent levels, here termed as primary and secondary niche markets. Both of these will be critical for FCVs and are examined in the paper. These two niche markets provide time for the mass market to see the product and build up a mass market pull, for the technological trajectory to start, and, time where the economics of the product are not the overriding concern. This paper gives an overview of the framework of adoption within the two niche markets and then concentrates on the second of these niche markets and its implications for FCVs. It is within the secondary niche market that the FCV will -rst come into direct competition with the internal combustion engine vehicle, and it is here that its utility, or usefulness, will be assessed by the adopter. The secondary niche market, unlike the mass market, does not operate under conditions of constrained utility maximisation, but are prepared to pay a premium of adoption. Modelling of the premium that the secondary niche market adopters would be prepared to pay today for FCVs provides us with an indication of how much further, technologically, they need to be before they are consumer ready. ? 2003 International Association for Hydrogen Energy. Published by Elsevier Science Ltd. All rights reserved. Keywords: Niche markets; Adoption; Fuel cell vehicles
1. Introduction If fuel cell vehicles (FCVs) are to be a success in the market place they must -rst contend with the statistic that states that the probability of success of a new product in an open market place lies between 1% and 30% [1,2]. Though this -gure provides stark warning to new products there exists in the literature a ‘pro-innovation bias’ [3], in which the majority of research is carried out using post ante data and information from examples of successful innovations. Though, through the expansion of innovation and di1usion research, there is a greater understanding of the adoption and di1usion of successful ‘evolutionary’ innovations Ayres [4] points to a further lack of understand
and theorisation concerning discontinuities. Technological discontinuities occur only periodically but their impact, if adopted, on the socio-economics is much stronger. Examples of these discontinuities and their impacts are the radio and telephone which enabled society to ‘feel’ closer together [5] and the transistor which jump started the computer revolution, with computers now in 80 million homes in Western Europe [6]. These examples highlight that the impacts are not only quantitative, such as the number of computers in homes but also subjective, for example feelings in society towards technology. 2. Market launch of a discontinuous product 2.1. A note on terminology: discontinuities
∗
Tel.: +49-30-314-79123; fax: +49-30-314-26908. E-mail address: [email protected] (K.-A. Adamson).
Though the two words, product and technology are often used interchangeably it is important to separate the two, the consumer buys a product which is usually a combinations of
0360-3199/03/$ 30.00 ? 2003 International Association for Hydrogen Energy. Published by Elsevier Science Ltd. All rights reserved. PII: S 0 3 6 0 - 3 1 9 9 ( 0 2 ) 0 0 2 4 6 - X
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technologies. Similarly it is important to understand when discussing discontinuities what the de-nition being used is. The word ‘discontinuity’ means at some level a dramatic change, a paradigm shift. To complicate the issue slightly there are three words that are sometimes used in the debate with similar meanings, these are disruptive and revolutionary, as well as discontinuous. The choice of which word to use can depend on the study in progress or sometimes simply the message to be conveyed. Ehrnberg’s [7] paper which categorises the range of de-nitions of discontinuities describes three common classes of de-nition: competence change, physical product or process change, or change in the price or performance level. These classi-cations and de-nitions that are provided are useful when dealing with management of discontinuities but as the focus of this paper is the consumer another de-nition is used. This paper adopts the de-nition of discontinuous products as being: Products that, through the use of new technologies, create within the user group a paradigm shift in beliefs, attitudes and use As can be seen from this the de-nition refocuses on the user group of the technology, and is adapted from Kuhn [8], in his work on paradigm shifts in scienti-c theory. This de-nition is also similar to that adopted by Mackay and Metcalfe [9] on their paper on forecasting methodology for discontinuous innovations (DIs) which states that DIs are ‘products or services that shift market structure, represent new technologies, require consumer learning and induce behavioural change’. The importance of this refocus is that this paper, and overall research, is based on examining why discontinuous products are adopted and di1used within a user group, and what are the impacts of adoption. The result of adoption and diffusion of any discontinuous product therefore by de-nition sees a change in society. Changes not just in the pattern of the work force and GDP but also in the way society interacts with each other and with the product. These socio-economic impacts of product adoption and di1usion is another critical area of research where, until recently, there has existed a bias that implies that adoption of new products, and or, technologies will result in positive impacts. As Rogers [3] pointed though there are a number of cases where the results from adoption of a product has had severe negative impacts on society, to the point where the product had to be actively ‘deadopted’. Now though with the debate over for example, gene manipulation this assumption is being actively questioned and we may see this bias weaken substantially, if not be neutralised. 2.1.1. Fuel cells and fuel cell vehicles Fuel cells are a potential discontinuous technology, of which FCVs are a discontinuous product. They are a discontinuity in that they o1er a window of opportunity to change the relationship between the user, the energy providers and
automotive industry. Also they could o1er the market place new attributes on the vehicle that could change the relationship between the consumer and the vehicle. Fuel cells have the potential to form the basis of a number of discontinuous products, each of which needs to be adopted as such. In this then fuel cells could be classed as a new ‘core technology’ [10]. Core technologies are technologies which have the potential to be incorporated into a number of current and new products. By introducing a range of di1erent products the resistance to change in the market may be decreased as the consumers become accustomed to what fuel cells are, but they still adopt the di1erent products not the technology. 2.2. Demand pull, market push Product acceptance is dependent on market push and demand pull. Which is more important, market push, demand pull, is an open question with a number of research papers stating that, in di1erent industries, one or the other is more important [see for example [11–13]]. What we do know is that for the product to be a success we need both, a demand pull from the consumer as well as market push for the manufacturer. In an idealised system the process of adoption of many technologies and products, evolutionary and discontinuous, is shown from empirical research to follow an S-curve. During the S-curve adoption starts o1 slow, over time speeds up, reaches a point of inLection, from which adoption slows down again till it reaches market saturation point. The main problem with this picture is that it is idealised. In reality the crossover point from Stage 1, niche markets, to Stage 2, mass market, is sometimes referred to as either ‘Death Valley’ or ‘Chasm of Commercialism’, as this is where most new products fail. This point will be returned to later. The process of adoption therefore has three potential pathways: 1. Non-adoption—products fail, either at the crossover from niche to mass market, or to reach niche market level successfully. 2. Deadoption—when the process of di1usion is halted, usually through government intervention, and reversed. 3. Adoption and di1usion—S-curve adoption and di1usion pattern, referred to as the norm.
3. Framework of adoption of discontinuous products This overall framework of adoption of discontinuous products is being developed during the current research and is based on the di1usion norm of the S-curve. Work on adopter categories has been merged with that of Rogers to form an initial picture of the path FCVs may follow to reach the mass market. The overview of the framework
K.-A. Adamson / International Journal of Hydrogen Energy 28 (2003) 771 – 780
of adoption that is presented here therefore is still under development. The current study separates the term niche market into 2 levels, primary and secondary niche market and then splits these levels further into the adopter groups, societal niche, and reason for adoption, technological niche. Both of these niche markets and their subsets need to be addressed before the technology reaches the boundary to the mass market. In terms of S-curve adoption therefore we expand the path to: Innovation →
Invention →
Primary Niche Market
3.1. Niche market adoption Niche markets are small protected markets in which a new product upon entry can start upon a technological trajectory → Secondary Niche Market → Mass Market
Societal Niche Technological Niche
and in terms of market demand we have three adopter categories, primary niche market adopters, secondary niche market adopters and mass market adopters. Modelling market pull for discontinuous products: DPt = xPNMAt + ySNMAt + zMMt − t ;
The rest of this paper concentrates on the niche market adoption, speci-cally on the secondary niche market, but will -rst discuss the critical di1erences between the two niches.
Societal Niche Technological Niche
773
(1)
where x; y; z is the weighting of importance; the resistance to change, and t the time. This equation states that the overall level of demand pull for a product, in time t, is a cumulative function of the primary niche market adopters, secondary niche market adopters and the mass market. The level of importance of each of these adopter groups, to the overall demand pull, is shown in the weightings of importance. At di1erent times within the adoption process the value of the demand pull changes and the values in the three di1erent adopter groups change. Demand pull also has a weighting indicating that in the overall change to a new product demand pull is only one function (government intervention and market push are the other two that have been mentioned here). The resistance to change is a feeling in society of why switch to a new product when the old one does the job satisfactorily. For example the market sees, in advertisements, that the current internal combustion engine vehicle (ICE) is becoming cleaner, that tailpipe emissions are being reduced and fuel consumption is decreasing. The impact of this is to create a level of belief in the market that the ICE will continue to get better and that there is no need to switch to a unknown new technology to answer such problems as emissions and climate change. This level of resistance to change is not static but changes over time with learning and network e1ects. This resistance to change has been shown in management literature [14,15] and papers on discontinuities [16,17]. The impact of this is that the new product needs to be more than better than the current one for adoption to take place, the level of resistance indicating how much better. If we ignore this issue we run the risk of, potentially seriously, overestimating the market pull for a new product.
of learning by doing, economies of scale and to begin the creation of network e1ects. It is these niche markets that are easier for a new product to enter before it attempts to enter the mass market. 3.2. Primary societal and technological niche market In the primary niche the new product enters because it provides a function that cannot be replicated by any other product on the market place—the technological niche. The group—the societal niche—that adopts it places such a high economic value on the new function that it overrides the issue of the high adoption costs associated with adopting a brand new product. An example adopter group of this type was the scienti-c community, and the technological niche that was being -lled was by the -rst computers. These leviathans were created and adopted by the scienti-c community to perform speci-c computational functions that it was deemed impossible for human ‘computers’ to perform [18]. The adoption of these computers by the scienti-c community was also a good example of a failure to recognise the future potential of a technology. It was famously assumed that only a very small number of room size computers would ever be needed to handle all the computational requirements by universities, and that there would be no other use for computers [18]. This is not to say that economics did not play a part in the adoption, but that the economic value of this new function, the computational ability, was high enough to ensure adoption by this particular group. This example of the -rst niche market, into which the product is adopted on its new functions, highlights the importance of the ‘X-factor’, the function that cannot be replicated by the current market, in discontinuous product adoption. It is this initial X-factor that overrides the high adoption costs, this new function has to have a high enough economic value to the primary niche market adopters. Therefore whatever function that FCVs vehicles produce that an ICE with petrol or diesel cannot needs to be identi-ed and ‘sold’ to the societal group which places the highest economic value on this function.
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3.3. Secondary niche market Once this initial niche market is saturated the product moves into a further larger niche market. Though this is again a niche market, in that it is size restricted, the new product here starts to compete directly with the product that is already on the market. In this niche the secondary niche adopter compares the utility provided by each of the competing products, and the product with the highest subjective utility gets adopted. This adoption is not based on price but utility maximisation. To put this another way, the product that is the most subjectively useful is adopted. As this niche is the subject of the rest of this paper it will be expanded on in much greater detail in the following sections. The di1erence between the mass market and the secondary niche market is that the mass market works under constrained utility maximisation, the constraint normally being budget, whilst the secondary niche market works under utility maximisation but are willing to pay an adoption premium. The framework that is under development, around the process of adoption for discontinuous technologies, has a number of critical points which are listed here in short form: • Though the societal and technological markets are not independent they both must be addressed by a new product. • Often two or more sibling technologies 1 are launched at the same time, or within a short time period of each other. • Initially these sibling technologies compete with each other in the primary societal and technological niche market. When this niche is full and it is time to move into the secondary niche phase there is generally one product with a substantial market lead. • By the time the product enters the secondary niche market the sibling technology with the market lead de-nes the product and its attributes. • The primary technological market niche is by de-nition a protected space because it is entered by the product though a function that the incumbent product cannot produce. This X-factor therefore creates the space within the niche for the new products. • The primary societal niche market therefore is a group of consumers who -nd this extra function that the new product o1ers attractive enough to invest in. • In the secondary niche market phase the new technology starts to compete with the incumbent technology on its attributes performance. These competing attributes are judged by the secondary societal niche market and the technology with the subjective highest utility, provided by these attributes, gets adopted.
• If the new technology makes it through the secondary market niche it is then attempts to enter the mass market. • The paradigm shift to the new product takes place during this cross over from niche to mass market product.
4. Adoption of fuel cell vehicles by the secondary niche market The usefulness of modelling FCV adoption parameters is that once complete these can used to analyse where the technical boundaries are to adoption by this group. For example we know that to make it technically viable, the fuel cell had to have a minimum output of 65 kW, but, does the consumer also require a minimum output of 65 kW, or is this higher? Also as the ICE represents a moving target for the FCV we can use this model to see how much better the FCV needs to be overall to ‘keep up’. As already discussed the adoption of the product in this niche is based on utility maximisation and is not constrained by a budget. In other words, this group are prepared to pay a premium of adoption for the product with the highest subjective usefulness. The issue of secondary niche market adoption of FCVs can be subsplit into two questions: 1. Probabilities of adoption where adoption is based on utility maximisation. 2. Calculation of the premium for adoption would be prepared to pay on adoption. 4.1. Utility maximisation and FCV adoption In this niche market it is assumed that the potential adopters are faced by the choice of staying within the known technology of the internal combustion engine or adopting the new technology of a FCV. The consumer decision to adopt is based on the cumulative utility of the attributes represented in the incumbent product as compared with that of the incoming product. Here the X-factor does not come into the decision making process as the attributes under consideration are those already o1ered by the incumbent technology. The relationship of this decision process therefore can be formally written as Probability of adoption of discontinuous product based on utility maximisation: Tt = U1t + U2t + U3t + · · · Ukt ;
1 Sibling Technologies: technologies that come from the same family of products, provide some of the same functions but have di1ering attributes. An example of sibling technologies is the VHS and Betamax video systems. They both provided the function of recording onto video but had di1erent attributes such as size of tape.
(2)
where T is the technology, ICE or FCV; the Adoption of technology T in time t; Ukt the Utility of attribute k in time t; k the attribute; and the weighting, this represents that fact that the consumer places di1erent levels of importance on di1erent attributes.
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In terms of adoption of a FCV over an ICE therefore the equation would look like: Probability of adoption of fuel cell vehicle based on utility maximisation: Tt = Upower + Utorque + Uacceleration + Ufuel economy + · · · Uk :
(3)
Decision procedure for adoption of fuel cell vehicle: UkFCV ⇒ ICE UkICE ¿
UkICE ¡
UkFCV ⇒ FCV :
(4)
It is assumed that in the niche market as well as the mass market adoption process there is a resistance to change; here represented by the utility provided by the FCV needing to higher, and not just equal to, that of the ICE before adoption takes place. It is important here that we clarify what assumptions, regarding the weightings, have been made. Publicly available surveys on purchase decisions by consumers, such as Claritas [19] use stated preference techniques to elicit from a range of consumers what is important to them in a decision purchase. Di1erent surveys split the consumers up in di1erent ways, such as banded by income level, or type of vehicle intending to purchase. Because these survey’s use importance criteria for existing vehicles we can use these stated preferences when calculating the probability of adoption of an FCV, but we need to make clear how we are splitting the consumers. In this study the assumption that is made regarding attribute importance level is: Across the market in Europe the rating of importance of attributes in vehicle purchase choice is dependent on vehicle class, not consumer segment. What this is the saying is that both the mass market and the secondary niche market both rate power, safety, etc., the same but these levels of importance are di1erent between vehicle classes. Using the Claritas survey Eq. (2), for compact vehicles, such as the A Class Mercedes, becomes: Weightings of importance in vehicle adoption decisions: T = 2:88Ureliability + 2:72Usafety + 2:68Uprice +2:58Urunning costs + 2:48Usecurity + 2:42Uperformance +2:23Ucustomer service + 2:23Uprevious experience +2:18Uoverall size + 2:17Uenv:friendly + 1:95Uspecs +1:73Uimgae :
(5)
A number of points need discussed from this equation: 1. There are a number of attributes such as customer service, image and security that are not directly measurable. How do we class and measure customer service? As with utility these attributes are to all intense and purposes unit less,
775
but we can make some assumptions or extrapolations from these. What needs remembering is that this study is an example of ‘what happens if: : :’. The results from the work are not to produce the adoption curve but to produce a range of adoption curves based on varying di1erent input variables. So if we assume that a current ICE has a safety and customer service of 1, we can model the FCV having a safety level of double, 2, half, 0.5, and see how this impacts the overall level of utility and adoption. 2. Previous experience—from this we would assume that this is previous experience with a particular model of car, such as a Ford Focus and not previous experience with a diesel engine or a LPG vehicle. This returns to the point that consumers purchase vehicles, not drive trains. So if we have an owner of a Ford Focus looking into purchasing a Ford Focus FCV they are still purchasing a Ford Focus, it is the attributes that this new Focus FCV provides that are important. Using this variable we can see the impact of vehicles such as the GM Motion which is a new optimised FCV concept vehicle, of which there is no previous experience. 3. Using this equation we must remember that utility has no units of measurement so instead of using overall -gures, such as 65 kw Power, we use incremental increases and decreases from the level of a comparable ICE for each utility measurement. It is important to remember that though the weightings for di1erent countries in Europe may be similar to the weightings from consumers in America will be di1erent. 4.2. Calculation of the adoption premium The method by which the secondary niche adopters adoption premium is being modelled uses the revealed preference Hedonics technique. This technique works on the theory that the basic price of a product is a combination of the costs of a set of identi-able attributes, with each attribute forming a percentage of the price [20]. For example, these attributes for cars would include horsepower, size and torque. So when di1erent products exhibit di1erent levels of these attributes then there is a price di1erential. The further assumption is then made in that as the consumer purchases these vehicles with the price di1erentials they are willing to pay this amount for the varying levels of attributes. In an ideal situation we would have focused sales data on how many of each type of vehicle, not just class and model, is sold but due to commercial diSculties we have to work around this with just the publicly available list prices. Using regression analysis we can extract from vehicle list price data the ‘price’ associated with each attribute so the basic regression equation is of the form: Base regression equation for calculating premium of adoption: Pit = + 1it + 2it + · · · kit + ;
(6)
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260 240
km/s
220 200 180 160 140 120 0
20000 40000 60000 80000 100000 120000 list price (€)
Fig. 2. Top speed as a function of list price.
25
Fig. 1. Vehicle attributes related to list price.
23 21
4.2.1. List price and vehicle attributes What can be seen from Fig. 1, which shows the list price of a vehicle as a summation of the prices of a range of attributes, is that a number of the attributes are direct combinations of others. In regression analysis this causes a high level of mutilcolliniarity resulting in instability in the results. The solution to the issue, that was used here, was to split the premium into 2 sections: willingness to pay for technical
19 km/l
where is the intercept; kit the price of attribute k in vehicle i at time t; and the error term. Because to perform a regression analysis on singular vehicles, or ranges of models, cannot with any level of certainty produce robust results the vehicles that were used in the regression analysis were done so based on vehicle class. The three classes that were modelled were subcompact/microminis, for example the Smart, compact, such as the Ford Focus and the luxury/executive class, such as the BMW 7 Series. The reason behind these choices is that most of the FCV prototypes are based on the platforms of subcompact and compact vehicles, and BMW’s hydrogen vehicles are based on their executive and micromini vehicles. In total 760 vehicles from the build year 2001 were included in the regression. This initial point in time study, build year 2001, is to look at the di1erence between today’s FCV prototypes and the Consumer Premium that would be paid today. This allows us to gauge an idea of how much further the technological development must proceed to meet this premium. Extrapolation of future premiums will require the dataset to be expanded backward over time to include information from a range of years to allow a measurement of how the attributes prices have changed. This would then allow extrapolation into the near term future of potential attribute prices and therefore premiums.
17 15 13 11 9 7 5 0
20000 40000 60000 80000 100000 120000 list price (€)
Fig. 3. Fuel consumption as a function of list price.
attributes, such as power and torque, and willingness to pay for reduced running costs. Here it was assumed that running costs are a summation of fuel costs and carbon dioxide based tax. This carbon dioxide based tax is included as Eq. (3) highlights that the altruistic environmentally friendliness of a vehicle is not intrinsically an important adoption criteria as running costs are. 4.3. Results from technical premium Figs. 2–4, show that what we know logically bears out in the results. Attributes such as acceleration (derived mostly from the vehicles torque) and top speed (derived from the power) increase with vehicle price whilst fuel economy drastically decreases. We can extrapolate this to hypothesis that the running costs in the subcompact and compact classes are of far higher importance than in the executive class, whilst power and torque are more critical to the executive class. Initially the regression was performed on the data using the same attributes for each class. These where automatic
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19 17
m/s
15 13 11 9 7 5 0
20000 40000 60000 80000 100000 120000 list price (€)
Fig. 4. Acceleration measured in 0 – 60 as a function of list price.
gearbox, brand quality, measured using a ADAC (German Automobile Association) survey, power, torque and wheel base. The impact on price of the presence of an automatic gearbox was modelled as a proxy for FCVs not having a gearbox. The Brand Quality attribute has the inbuilt assumption that all vehicles made by the same manufacturer exhibit the same quality level. Using the t statistic distribution and relevant degrees of freedom the 95% con-dence intervals were calculated. At the 95% con-dence level the probability (p) that the null hypothesis 2 is correct is over 0.05. In other words the higher the p value the less con-dence we can place in the attribute being a statistically signi-cant component of the list price. The results where that in each of the vehicle categories di1erent attributes fail at the 95% con-dence interval. Taking the ‘all together’ vehicle category -rst that was a high R2 value of 0.87, indicating that 87% of the price can be explained by the modelled attributes. The fuel attribute (0 for petrol, 1 for diesel) has a very high distribution spread and p value, so can be dropped from the analysis. All the other attributes have much closer con-dence intervals and signi-cantly low, ¿ 0:01; p values, indicating high levels of con-dence. For the category of subcompact the R2 is the lowest of the four at only 0.69 indicating that within this category over 30% of the list price comes from non-modelled attributes. It was interesting here that brand quality has a very high p value indicating that the null hypothesis here is correct. This though could be more to do with the low spread of brands in the vehicle section, though there were 189 vehicles modelled they came from only a range of 15 models. What was of slight concern is that two other attributes, torque and fuel, also fail at the 95% con-dence level. Fuel, here petrol or diesel, can to a degree indicate fuel eSciency. For the subcompact vehicles the petrol vehicles had an average fuel eSciency of 16 km=l whilst the diesel’s was 21 km=l. 2 Here the null hypothesis is that the attribute under consideration has no statistical relationship with the list price of the vehicle.
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Because of the assumption that fuel eSciency would be critical in this category the failure of fuel to pass the 95% con-dence interval is of concern. The con-dence intervals for all three of these attributes switch from a positive, indicating that the attribute adds ’s to the price, to negative, the attributes lowers the price. For the second round of regressions power was removed from the subcompact division but torque and fuel remained in to see if these inconsistencies could be removed. Again this statistical deviations could be a result of the smaller dataset used in this vehicle group. The compact class has the greatest range of vehicles, some 357, and the results from this class appear to the most statistically sound. Only one of the initial modelled attributed failed at the 95% signi-cant level, that interestingly of wheel base. This could indicate that the price of a small vehicle is made of attributes that are linked to the size of the vehicle and engine but not intrinsically the size of the vehicle itself. The luxury/executive segment, like the subcompact, represent smaller vehicle markets than the middle the range compact so again had a smaller number of data points from which to compile the model. Taking this into account, the main result from the initial regression was intuitively contradictive with evidence that the power of the vehicle is not only statistically important, but also that is has a negative sign. This indicates that with a power increase comes a price decrease! Fuel, which is linked to power, in that diesel engines tend not to be as powerful (there are only 29 diesel vehicles in this group) seems to represent a disproportionally high value, some 9230 drop in price for a diesel, as compared with a petrol vehicle. Even though fuel passed the test on statistical signi-cance it was decided to drop this in the focused round of regressions and see what impact that had on the power attribute. Table 1 produces the results from the focused regressions which have removed a number of attributes from the di1erent classes that had high spreads in the con-dence intervals or did not reach the 95% level of signi-cance. The -rst thing to note about Table 1 is that none of the R2 goodness of -t values have changed, which indicates again that the other included attributes did not contribute to the regression. Secondly, by removing a small number of the variables in each of the di1erent sections, all the attributes that remain pass the test of the 95% con-dence interval, including the worrisome fuel in the subcompact section. Now we can look at the di1erent attributes values. Fuel, a potential proxy for the level of fuel eSciency in the vehicle is very important in the subcompact and compact class with a diesel vehicle being worth 686 and 730 ’s more respectively than an equivalent petrol vehicle. In terms of the impact of FCVs therefore we can hypothesis here that the consumer would be willing to pay both in the technical attributes and the running costs for extra fuel eSciency in the smaller vehicles. In the executive class where an increase to diesel saw a drop in power the fuel variable has been dropped with the impact of making the power variable highly signi-cant. It is interesting to note that the top value for power is located in
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Table 1 Focused regressions of vehicle attributes Variable
CoeScient
+ 95% CI
−95% CI
t-statistic
Probability
sAll together: = 0:87; 754 degrees of freedom Automatic gearbox 2830.36 Brand quality 390.32 Power 93.96 Torque 48.62 Wheel base 0.00033
3634.63 479.21 103.51 57.31 0.00053
2026.09 301.42 84.41 39.92 0.00013
6.898 8.606 19.292 10.960 3.277
0.000 0.000 0.000 0.000 0.001
Subcompact: R2 = 0:69; 184 degrees of freedom Automatic gearbox 1105.27 Power 64.13 Wheel base 0.000257 Fuel 686.07
1529.84 70.81 0.00048 1154.27
680.69 57.44 0.00004 217.88
5.102 18.807 2.286 2.872
0.000 0.000 0.023 0.005
Compact: R2 = 0:76; 348 degrees of freedom Automatic gearbox 1479.71 Brand quality 517.72 Power 54.45 Torque 12.91 Fuel 730.21
1935.00 565.90 63.45 18.55 1223.51
1024.43 469.54 45.45 7.28 236.91
6.370 21.061 11.853 4.492 2.901
0.000 0.000 0.000 0.000 0.004
Executive: R2 = 0:80; 209 degrees of freedom Automatic gearbox 3365.57 Brand quality 393.21 Power 46.57 Torque 107.64 Wheel base 0.00057
5539.90 676.51 69.08 130.90 0.00096
1191.24 109.91 24.05 84.37 0.00018
3.034 2.7201 4.054 9.068 2.864
0.003 0.007 0.000 0.000 0.005
R2
the subcompact vehicle, not as might have expected in the executive class. But the executive class has the highest R2 value indicating that overall power and the other four variables measured have the greatest importance. In none of the vehicle classes modelled was the presence of an automatic gearbox a price negative, a bene-t for FCVs. Torque, which is related to acceleration, is critical in the executive class with a value of slightly over double that of power. In terms of equations we can write the following, including the values for the intercept and residuals, which have not been included in the tables: The price of subcompact vehicle as a summation of its component parts: = 1105 × automatic gearbox + 64 × power +686 × fuel type + 0:000257wheelbase + 3800:
(7)
The price of compact vehicle as a summation of its component parts: = 1480 × automatic gearbox + 54 × power +730 × fuel type + 13 × torque +518 × brand quality + 2900:
(8)
The price of compact vehicle as a summation of its component parts: = 3366 × automatic gearbox + 47 × power +0:000567 × wheelbase + 108 × torque +393 × brand quality − 14153:
(9)
Bearing in mind that these -gures are from the build year 2001 we can calculate the -rst half of the premium, the technical premium, for prototype FCVs. The calculation based on the Ford Focus FCV prototype realises a value of 16; 378 , whilst the same calculation on the NECAR 5 produces 16; 916 . The positive result from this is that on the technical premium alone, not taking into account the running costs premium, the values from the FCV prototype already met or nearly equal the current list prices of 16; 371 and 20; 000 , respectively. Working on the assumption that before these vehicles enter the secondary niche market they will improve technically, then potentially there will be a substantial premium on these vehicles, but, this is dependent not just on the rate of improvement of the FCV but critically also on the rate of improvement of the internal combustion engine.
K.-A. Adamson / International Journal of Hydrogen Energy 28 (2003) 771 – 780
5. Discussion and conclusions For market launch of FCVs this paper has highlighted a number of critical points. The product will have to address three di1erent adopter groups with di1erent requirements from the product. The -rst, the primary niche market adopters will require that the FCV will provide a new function that has a suSciently high economic value to overcome the costs of adoption. The second, the secondary niche market adopters will require that the subjective usefulness of the FCV is greater than that of the current vehicle technology, but at the point of adoption this group is prepared to pay a premium for adoption. To enter the mass market the FCV will have to develop, during the niche market phases, a pull from the mass market as well as bringing down costs, due to adoption being constrained by budget. In terms of economic performance of FCVs therefore there is potentially a ‘breathing space’ in which the adoption of these vehicles is not based on a constrained economic decision. But, in terms of technological performance the FCV must continue to improve at least at the same rate of change of the current technology. Also the unique function that a FCV can produce, if it is to be a discontinuous product, needs to be identi-ed and marketed now. From the regression results, on the technical premium that the secondary niche market adopters would be willing to pay, there appears to be an interesting trade o1 between cost and performance in the di1erent vehicle segments. The fuel choice, in subcompact and compact vehicles, is of high importance. If it is assumed that this is a proxy for fuel eSciency, that the consumer has no intrinsic desire for diesel above petrol, then the bene-ts that FCVs could offer on fuel eSciency need to be marketed in these vehicle segments. But these are the vehicle segments that have the lowest list price. The excellent torque and acceleration of these vehicles are of minimal importance in these vehicle classes but critical in the luxury/executive vehicle groups, which also are a much higher price. So if the fuel cell power train is promoted through the executive class vehicle, not only can it be bigger due to an increase in size being statistically signi-cant in this group, but it can be promoted on performance. If though it is continued to be promoted as being a move towards sustainable transport and the environmentally friendliness of increased fuel eSciency then it needs to be marketed in the smaller vehicle categories, but this implies stricter cost targets. Instead of discussing statistical values and frameworks of adoption what the research has shown so far can be best illustrated in a short example path to market for FCVs. 6. Example path to mass market for fuel cell vehicles based on the framework of adoption At the launch of fuel cell vehicles three sibling technologies are launched. These are fuel cell vehicles using
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direct hydrogen, FCVs using an onboard reformer and direct methanol FCVs. Each of these FCVs is marketed to di1erent societal groups, which have di1erent requirements from the product. Initially the vehicles are launched into the compact vehicle segments only, as second or third vehicles for the adopters. Through a number of small competitive advantages, stemming from network e1ects such as fuel availability and localised legislation, hydrogen FCVs start to dominate the market. At this point it is critical that there are no hydrogen related accidents to stigmatise the fuel and product. The initial rush of manufacturers to place technologies on the market slows down and more partnerships are created towards standardisation of the product and refuelling stations. A large number of the smaller manufacturers are either bought out by their rivals or go out of business. The three vehicle types move to the secondary niche market with hydrogen exhibiting a strong lead, methanol and petrol’s market share already starting to decrease. By now the attributes of what a FCV, the standardisation of the product, is de-ned by the attributes of a direct hydrogen FCV. In the secondary niche market executive class vehicles are released alongside the now well known compact FCVs. The usefulness of the vehicles in terms of high fuel eSciency and excellent torque are promoted to the secondary niche market adopters. Because of the slowing down of the rate of change of the ICE the FCV has been able to surpass it on technical qualities and, has, with its lower running costs, a higher subjective utility. The impact of adoption in both the niche markets is to reduce costs, through learning e1ects, improvements in the technology and standardisation. By the time the FCV reaches the border to the mass market the hydrogen FCV is the only FCV left in the market. Government incentives and tax breaks ensure that the last remaining hurdle of economics is not insurmountable and the FCV attempts to cross ‘death valley’. By now it is no longer a product for limited groups and there is a ground swell of desire for the product, and at this point that the FCV starts to seriously erode the market shares of the ICE vehicle. It is only now that the environmental bene-ts of the FCV are marketed to the consumer. This is one potential short qualitative pathway to market for FCVs. Once the model is complete we will be able to construct a range of quantitative pathways using a range of di1erent assumptions.
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