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Calculating the price trajectory of adoption of fuel cell vehicles
International Journal of Hydrogen Energy 30 (2005) 341 – 350 www.elsevier.com/locate/ijhydene
Calculating the price trajectory of adoption of fuel cell vehicles Kerry-Ann Adamson∗ Fuel Cell and Hydrogen Research Centre, Technical University of Berlin, Energy Systems, Sekr. TA8, Einsteinufer 25, 10587 Berlin, Germany Received 2 April 2004; received in revised form 27 July 2004; accepted 28 July 2004 Available online 27 September 2004
Abstract How do you model consumer behaviour for disruptive technologies? Technologies that potentially have no antecedents and that, by their very definition, change consumer behaviour patterns? This paper outlines a methodology and results employed during a study to model the consumer willingness to pay for fuel cell vehicles, a potential disruptive innovation (DI). The first part of the study provides a short overview on DI highlighting why the fuel cell family of technologies may represent an upcoming DI. From the post ante study of successful historical disruptive innovations a number of initial ‘rules of adoption’ can be sketched. Further narrowing of the focus on economic reasons for adoption provides a framework for which the willingness to pay for the new disruptive technology, such as, here, fuel cell vehicles, can be analysed during different phases of the market. This economic framework is then applied to the potential future market of fuel cell vehicles using information from a model that was built from vehicles during the build years 1994–2002 in the subcompact, compact and luxury class. The results presented in this paper concentrate on the subcompact and compact class of vehicle and supersede the initial results previously published. Finally, there is a short discussion on different pathways that this can be taken forward and used to help in policy decisions. 䉷 2004 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved. Keywords: Disruptive innovations; Adoption; Fuel cell vehicles; Economics
1. Introduction Disruptive innovations are (DIs) innovations that at some level cause a large perturbation in the system, whether in the market structure, product or consumer behaviour. These innovations are the focus of a number of published papers, for example Refs. [1–7], internet based papers, for example Refs. [8–10], working papers, for example Refs. [11–14], books, for example [15–18] and management courses. Famous examples from the last 150 years clearly show these system perturbations. The telephone has helped ∗ Tel.: +49-30-314-79123; fax: +49-30-314-26908.
E-mail address: [email protected] (K.-A. Adamson).
society to develop condensed high-rise inner city living [7] whilst the car has encouraged urban sprawl and commuter life [19]. Production process changes such as Fords revolutionary Model T production process encouraged women into the work place, providing one of the first examples of women earning the same wage as men [20] and also enabled such an reduction in the price of the vehicle that it became available to the true mass market [21]. Xerox’s transformation happened because of their DI in their management systems and company mission, with the impact to become a market-leading product-leasing company [22]. These are all examples of innovations that caused radical perturbations within the embedded system. Fuel cells are a technology that, within limited applications, are nearing market entry. Currently still experiencing
0360-3199/$30.00 䉷 2004 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2004.07.004
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high technology push with lack of defined product attributes, and a number of substantial engineering hurdles still to be overcome, they could represent a future DI. This statement is based on the potential of fuel cells to produce distributed energy systems and also, in the longer term, potentially redefining the relationship between a car owner and the vehicle. Depending on which market they address, they could cause perturbations in the energy market and automotive industry, move towards sustainable development, electronics markets and personal communication. If fuel cell vehicles are released into the market place, they will either be part of a product family, for example, laptops, power generation, etc, therefore representing a new core purpose technology [23], or they will be released solely in one market place, most likely in the propulsion systems market place. As with all new product innovations, incremental or discontinuous, the chances of market success depend on a large range of factors and are, in general, low. The research that these results have been taken from looked at adoption patterns of a small number of successful historical market/consumer-based disruptive technologies. Any underlying economic patterns of adoption that could be drawn out of the information were then taken forward and used in a hypothetical future European fuel-cell vehicle market. The first part of the paper provides a basic taxonomy of DIs from which an overview of the economic pathway of adoption, through niche markets, for market-based DIs is evolved. The second part reproduces the results on the modelling of the level of premium that the secondary niche market adopters would be prepared to pay for a number of heavily citied attributes of fuel cell vehicles. The model covers two classes of vehicle, the subcompact and compact class, of which a number of the prototype vehicles, such as the Mercedes NECAR and the Ford Focus FCV, have been produced.
2. Taxonomy and adoption of DIs Fuel cells still face a number of substantial technical and engineering challenges. Water management and platinum loading are just two. Besides these, hydrogen, the fuel for the fuel cell, is also still some way from finding the perfect storage medium, for vehicles at least, and the infrastructure debate is still raging. Although some fuel cell technologies are close to being at market entry, these are still small scale markets, such as distributed power in island economies, rather than mainstream mass markets such as fuel cell vehicles. The reason that this paper focusses is on fuel cell vehicles, and not on the fuel cell family of products in general, is that here in this specific market it addresses a special case of lock in of an incumbent technology that is currently viewed as being efficient and having room for improvement. This competition between fuel cells and the internal combustion
engine was the focus of the study and how each impacts the chances of a paradigm shift. The current date for projected fuel cell vehicles to be economically viable for the mass market is somewhere between 2020 and 2030. This is for the mass market though. Before the mass market comes niche markets. These niche markets are critical as they provide room for product improvement and cost reduction, as well as the product being “on view” to the mass market. The rest of this section outlines the adoption through the niche markets based on the theory that fuel cells could represent a Discontinuous Innovation. It starts with a brief overview of DIs. 2.1. DI taxonomy There are different types of DI. As each type causes different impacts and follows different adoption and diffusion paths by understanding what type of DI is being represented we have a narrower research focus to understand better the adoption processes. There have been a limited number of papers on classification of DIs, with currently the most cited being Ehrnberg’s 1995 paper [24]. She classifies a number of disruptive innovations based on competence change, physical product change or change in the price or performance level. The basic taxonomy for DIs splits DIs into innovations that affect the market or impact on the product. Fuel cells are a technology that affect a product and it is the use of the product that impacts the user and market place; therefore for the rest of this paper other forms of DIs say, in management structure will no longer be considered. If DIs represent consumer technologies once integrated into products and adopted by the market place, they then cause a fundamental change in behaviour patterns. Because of this shift in behaviour patterns researchers modelling adoption decisions face a conundrum—how do you model something that alters behaviour without first knowing what the new behaviour will be—but how do you know what the new behaviour patterns will be from a product with potentially no antecedents? The current study used historical information on adoption decisions and diffusion patterns of a small number of successful market-based DIs drawing together a short list of ‘rules for adoption’ which showed that within the niche markets consumer behaviour can be modelled using standard economic techniques [25]. This is due to the paradigm shift caused by the large scale perturbation occurring during the mass market phase, not during the niche market phase. This is not to say that any perturbations do not occur until this point but that there is a build-up of small scale ‘system shocks’, whether the system is social, economic, or legislative; and these weaken the system so much so that when a larger ‘system shock’ happens during adoption in the mass market this causes a fundamental paradigm shift. As we can use standard economic techniques until the mass market we can model the niche market decision and adoption
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patterns based on a number of clear and documented assumptions. 2.2. Niche market to mass market—economic modelling of adoption A niche market is a small protected market that a new DI product enters before it reaches the mass market. It starts upon its economic and learning trajectory, which form the basis of cost reductions and product standardisation, before attempting to enter the mass market. For market-based DIs there appears to be two main consumer niches,1 the primary and secondary, both with very different reasons for product adoption, the technological niche. Also both niches have differing economic decisions and adoption/adopter profiles. 2.2.1. Primary niche market Why do some people adopt an untried, untested, new, risky, high-cost product? These first adopters, the ‘Innovators’ in Roger’s seminal work [17], adopt the product 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 adoption costs. This is shown in Eq. (1), i.e., modelling the adoption decisions of the primary niche market adopters T a = > ,
(1)
where T 1 is the adoption of technology T, during niche a, at time t; the weighted value of X factor; and the weight adoption costs. Adoption costs include price of the product, infrastructure, training, etc. Both of the X factor and adoption costs are weighted to represent so that both have different levels of importance to different people. There may be some cases in certain groups where the X factor is very highly weighted, but so also are the adoption costs. Technology adoption in third world countries might fall into this area. 2.2.2. Secondary niche market The new product here starts to compete directly with the product that is already in 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. In this niche product price is one measure of utility to be calculated alongside a number of others. 1 Though it is recognised that car fleet purchases by govern-
ments or companies are niche markets, a protected market bounded by size, they are not included here as this work is based on private consumer adoption, and although fleet markets can help with altering economics they do not directly impact upon private consumer purchase decisions.
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During this niche market it is assumed that the potential adopters are faced with the choice of staying within the known technology such as the internal combustion engine vehicle or adopting a new technology, for example a fuel-cell vehicle. 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, therefore the X factor does not come into the decision-making process as the attributes under consideration are those already offered by the incumbent technology. The relationship of this decision process therefore can be formally written as Eq. (2), i.e. adoption decision within the secondary niche market T tb = U1t + U2t + U3t + · · · + Ukt ,
(2)
where is the adoption of technology T, during niche b, at time t; Ukt the utility of attribute k in time t; k the attribute; and the weighting. From these two niches we can see that a premium of adoption is acceptable as long as the new product is economically worth adopting to the different adopter groups. Although we can make some assumptions as some of the initial adoption costs which would be faced by primary niche market adopters, others are harder to value. Also, as we would need to make ‘guesses’ as to what an X factor from a fcv might be, calculation of the premium in the primary niche market would be difficult until we are closer to the point of market entry when we have more detailed information and cost estimates. Due to the secondary niche markets, adoption decisions being based on the attributes of the incumbent product calculation of this premium is more straightforward and also avoids having to make any predictions about the characteristics of a future fuel cell vehicle. In summary, it is not until the mass market that the disruptive product needs to compete economically with the embedded product. During the prevalence of niche markets the product is adopted with a premium of adoption based directly on the products attributes. Once the product reaches the mass market it is only then that the consumer behaviour patterns start to change. Because of this we can model consumer adoptions patterns in the niche markets based on trends observed from historical post ante information. 3. Modelling the premium of adoption that secondary niche market adopters would be prepared to pay for the utility provided by fuel cell vehicles 3.1. Model background At the start of the market fuel cell vehicles will be economically more expensive than the equivalent internal combustion engine vehicle. However, how the consumer sees the premium depends on whether companies or governments subsidise the product. These subsidies will only occur if governments or regulatory bodies decide that the
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benefits due to the adoption and diffusion of the technology are high enough to provide the necessary programmes. If there does exist a market-visible adoption premium, then the fuel cell vehicle will develop along a price premium trajectory via niche markets to reduce the premium to the economic ‘break-even’ point with the internal combustion engine. Ideally this break-even point occurs at the start of the mass market. In Adamson [25] initial results from the model was discussed. Note that all results published here supersede these. The model that was created was based upon Gillriches hedonic techniques [26] which hypothesises that, all things being equal, the list price of a vehicle is made up as the summation of a number of component parts, which, provided that there is enough data, either cross-section or over time, can be disaggregated through regression analysis. Hedonic list price of a vehicle as a function of its attributes is as follows: Pit = ft (x1it , x2it x3it · · · xkit , it ),
(3)
where Pit is the price of model i, at time t; x1it the vehicle attribute 1, of model i, at time t; xkit the vehicle attribute k, of model 1, at time t; and it the residuals. Each attribute contributes a certain amount to the overall price of the vehicle, as shown in Eq. (4), i.e., Hedonic list price of a vehicle shown as a summation of attribute prices Pit = p1t + p2t + · · · + pkt , (4) where P1t is the price of attribute 1 in time t and Pkt the price of attribute k in time t. For the regression analysis the model collected information from 2 publicly available databases on a total of 12,364 vehicles over the build years 1994 to 2002. The model used three categories of vehicles—subcompact (2973 vehicles), compact (5938 vehicles) and luxury (3453 vehicles). The only rule applied as to which vehicles to include is that they had to be freely available across the European Union, which excluded a number of subcompact and luxury cars that are only available as import from Japan or the USA. This paper only covers the results from the first two vehicles classes, subcompact and compact. The information that was collected on the vehicle attributes was: • • • • • • • • • • •
list price automatic gearbox carbon dioxide emissions noise displacement rated torque rated power top speed wheel base fuel consumption acceleration
Table 1 ADAC survey values for brand name and brand quality Brand
Brand name
Brand quality
VW Vauxhall/Opel Mercedes Benz Audi Peugeot Toyota Ford Honda Skoda Nissan Seat
18 18 17 17 14 12 12 9 8 7 5
12 10 15 13 9 14 10 12 8 9 7
• weight • brand name • brand quality Brand name and brand quality measurements were taken from a 2002 ADAC (German Automobile Association) survey on the car manufacturers and their product performance. The values included in the model are shown below in Table 1. One recognised shortcoming of the model is that by using these survey results it was implied that all products from one manufacturer exhibit the same level of quality performance, and that this performance has been held static over time. Also, the model therefore assumes that the subjective values held by German car drivers, and members of the ADAC, who were polled for the value of the name and quality, are representative as an average member of the EU. As these are the most “subjective” valuations used in the model it is suggested that the results of these two variables are treated with the highest level of care. The inclusion of an automatic gearbox was measured using a dummy variable, 1 being the inclusion of an automatic gearbox. It was recognised that although there are also a number of non-technical characteristics, such as the styling of the vehicle that go into the aggregated list price these have not been analysed here. This would only be possible with a highly detailed, and consistent, survey of all the styling and trims of the vehicles included. Although the consumers adopt on decisions based on utility or usefulness, due to problems of multi-collinerarity (double counting), we cannot directly measure this utility in a single-step regression. For example, we recognise that consumers find the fuel economy of a vehicle an important adoption decision but fuel economy is directly linked to a number of mechanical attributes, such as weight and power. These in turn are also linked to other functions, such as acceleration. The impact of this is that although the consumer finds fuel economy and acceleration important criteria for selecting a car, we cannot directly model these functions
K.-A. Adamson / International Journal of Hydrogen Energy 30 (2005) 341 – 350
without running the risk of “double counting” the technical attributes, which in turn would create instabilities in the results. This is shown in Eq. (5), i.e., utility value of a vehicle as a function of its attributes
345
25
(5)
where U1it is the utility function 1 of model i, in time t and xkit the vehicle attribute k, of model i, at time t. The previous list shows the technical attributes that we make available to model while the utility attributes we would like to be able to model are:
ACCELERATION
20
U1it = f (xyit , xzit · · · xkit ),
15
10
• • • • •
fuel economy acceleration running costs emissions having an automatic gearbox.
It was assumed that the level of utility gained from zero or reduced emissions was close to zero but emissions were included under the factor, Running Costs. The reason behind this is that in some countries, such as the UK, vehicle tax is based on carbon dioxide emissions. The impact of this assumption will be returned to and discussed later on. Initially the model was set up in a single-step regression [24] with the aim of splitting the utility into two sections—the utility from running costs and the utility from mechanical attributes. It was subsequently found that this could be circumvented by using this relationship between the mechanical attributes and the utility attributes. The final model therefore was done in a three-step process. First, using equation five, the utility functions of fuel economy and acceleration were regressed against their technical attributes. Next, using equation three, the list price of the vehicles in each year and class were regressed against their technical attributes. Once these were complete, the fuel economy and acceleration were substituted into the list price equations to form the final equations on consumer willingness to pay for attributes commonly promoted for fuel cell vehicles. 3.2. Model results Due to the size of the database and the possible number of permutations of calculations, the results from the model are extensive. The results shown here are concentrated around the consumer willingness to pay for higher fuel economy and acceleration. The reason that acceleration, measured in seconds taken to accelerate to sixty miles per hour, was chosen as a focus is for two reasons: 1. Currently acceleration is directly linked to carbon dioxide emissions and is also highly correlated with the price of the vehicle, as shown in Fig. 1; the higher the price of
5 0
50000
100000
15000
LIST_PRICE Fig. 1. Time to accelerate 0–60 in seconds against Euro list price.
the vehicle the higher the proportion that comes from increased acceleration. 2. Acceleration can be seen as a partial proxy for safety, being able to accelerate out of dangerous situations. Initially the historical section of the model was completed and analysed for its ability to predict the actual list price, given the average vehicle characteristics from the year in question. Table 2 shows the results from the compact class of vehicle. To read the table the column of coefficients represent the euro value of each unit of the attribute, in the compact class. The 95% confidence intervals represent the higher and lower band within which there is a 95% chance of the value falling. The narrower the bands the higher the confidence in the predicted coefficient. Figs. 2 and 3 plot the R 2 ’s over time in the two vehicle classes and the percentage differences between the predicted and the actual euro prices. What the table highlights is that in the compact class the R 2 values were high, the lowest being 0.70, and a fairly uniform level of deviation exists between the actual and predicted prices. In the subcompact class the year 1994 was dropped from the analysis due to having the lowest levels of data available, and an unacceptable percentage difference between actual and predicted values (33%). This value is not shown on the graph. The 14% difference in 1996 can be explained by the inclusion of a couple of ‘special edition’ vehicles with higher prices but identical technical specifications, only the styling of the vehicle being altered. If these are removed from the database, then this percentage difference is substantially reduced. As already mentioned, the 14% difference in 1996 can mainly be attributed to the inclusion of a small number of special edition vehicles. If the
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Table 2 Results for the compact class vehicle attributes Coefficient
Std. error
+95% Confidence Interval
−95% Confidence Interval
t-statistic
Probability
R 2 = 0.84, Degrees of freedom=265, Difference between actual and predicted price=9% 1994 Power 76.86046 5.800955 88.2303318 Torque 36.80793 3.065028 42.81538488 Automatic gearbox 2311.179 279.4552 2858.911192 Brand name 287.9523 29.6773 346.119808 Brand quality 698.4026 50.8769 798.121324
65.4905882 13.24962 30.80047512 12.009 1763.446808 8.270302 229.784792 9.702779 598.683876 13.7273
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.84, Degrees of freedom=348, Difference between actual and predicted price=3% 1995 Power 75.81861 3.537507 82.75212372 Torque 39.94723 1.979121 43.82630716 Automatic gearbox 2206.698 167.0682 2534.151672 Brand name 262.7355 17.41002 296.8591392 Brand quality 696.5837 31.29681 757.9254476
68.88509628 36.06815284 1879.244328 228.6118608 635.2419524
21.43278 20.18433 13.20837 15.09105 22.25734
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.82, Degrees of freedom=418, Difference between actual and predicted price=5% 1996 Power 87.01517 4.355798 95.55253408 Torque 50.46475 2.842968 56.03696728 Automatic gearbox 2328.828 201.8329 2724.420484 Brand name 264.8993 20.23712 304.5640552 Brand quality 601.4794 41.60486 683.0249256
78.47780592 44.89253272 1933.235516 225.2345448 519.9338744
19.97686 17.75073 11.5384 13.08977 14.45695
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.76, Degrees of freedom=494, Difference between actual and predicted price=8% 1997 Power 77.41528 4.86512 86.9509152 Torque 61.95318 3.389777 68.59714292 Automatic gearbox 2336.723 205.9463 2740.377748 Brand name 163.1245 23.62709 209.4335964 Brand quality 681.8973 46.03033 772.1167468
67.8796448 55.30921708 1933.068252 116.8154036 591.6778532
15.91231 18.27648 11.34627 6.904131 14.81409
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.70, Degrees of freedom=559, Difference between actual and predicted price=2% 1998 Power 61.81287 6.138732 73.84478472 Torque 62.2722 4.534654 71.16012184 Automatic gearbox 1740.016 253.9339 2237.726444 Brand name 151.2598 29.23156 208.5536576 Brand quality 568.5503 65.06767 696.0829332
150.8745101 144.0084928 4639.87773 437.9967289 1429.390219
10.06932 13.73252 6.852241 5.174539 8.73783
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.77, Degrees of freedom=879, Difference between actual and predicted price=5% 1999 Power 51.05405 6.673602 64.13430992 Torque 75.17966 5.954608 86.85069168 Automatic gearbox 1956.736 234.9394 2417.217224 Brand name 86.25374 28.43523 141.9867908 Brand quality 383.6339 64.73304 510.5106584
132.3768494 176.1819637 4972.685159 306.72934 1065.33393
7.650149 12.62546 8.328686 3.033341 5.9264
0.0000 0.0000 0.0000 0.0025 0.0000
R 2 = 0.77, Degrees of freedom=1051, Difference between actual and predicted price=1% 2000 Power 82.1506 6.955487 95.78335452 Torque 50.37804 6.470654 63.06052184
194.6908619 130.0692768
11.81091 0.0000 7.785619 0.0000
K.-A. Adamson / International Journal of Hydrogen Energy 30 (2005) 341 – 350
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Table 2 (continued) Coefficient
Std. error
−95% Confidence Interval
+95% Confidence Interval
Probability
Brand quality 340.9626 71.59112 481.2811952 Automatic gearbox 1575.078 234.0752 2033.865392 Brand name 116.7554 26.92396 169.5263616 R 2 = 0.79, Degrees of freedom=1272, Difference between actual and predicted price=4% 2001 Power 83.7257 6.724676 96.90606496 Torque 41.98081 5.986859 53.71505364 Automatic gearbox 1551.583 216.2585 1975.44966 Brand name 146.0237 30.00015 204.823994 Brand quality 177.9188 68.25412 311.6968752
196.6605633 12.45052 111.2683641 7.01216 4088.139834 7.174668 431.4551782 4.867431 679.1799954 2.606712
0.0000 0.0000 0.0000 0.0000 0.0095
R 2 = 0.8, Degrees of freedom=446, Difference 2002 Power 83.38128 7.757863 Torque 45.26086 7.405706 Automatic gearbox 1415.633 229.5712 Brand name 192.6154 41.37761 Brand quality 277.8504 90.40479
200.9877783 10.74797 124.5667518 6.11162 1489.018106 6.166421 744.9206206 4.655063 437.7038313 3.073403
0.0000 0.0000 0.0000 0.0000 0.0023
98.58669148 59.77604376 642.574952 358.9505156 177.1933884
1 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 1995
1996
1997
1998
Subcompact
1999
2000
2001
2002
Compact
Fig. 2. Comparative R 2 values for the subcompact and compact class. 16 14 12 10 8 6 4 2 0 1994
1995
1996
1997 Compact
1998
1999
4.762638 0.0000 6.72894 0.0000 4.336485 0.0000
between actual and predicted price=8%
0.95
1994
1014.902263 4220.451368 359.1956287
t-statistic
2000
2001
2002
Subcompact
Fig. 3. Comparative % difference between actual and predicted prices for subcompact and compact class.
database was sufficiently large, then these outlying values would be smoothed out but as the model already incorporates information on all vehicles in the compact and subcompact classes available in Europe during this time, this smoothing effect is not possible. The compact class list price regression produced no variables that were classed as statistically insignificant, using a p-value < 0.05. As can be seen in the table the technical attributes that were used in the list price regression were power, torque, weight, automatic gearbox, brand name and brand quality. The subcompact class regressions were not as smooth, potentially because of the smaller number of data points, and the included technical attributes varied per year due to result instabilities. These first step regression results were then applied to the equation linking fuel economy and acceleration to a number of technical attributes. The final results are shown below in Tables 3 and 4. Fuel economy is measured in litres per hundred kilometre, so for example in 1994 in the subcompact class, every decrease in litre per hundred kilometres is valued at 1903 euros. Similarly for acceleration every decrease in per second needed to accelerate from 0 to 60 is valued at 2250 euros. Note that although the residuals are large and variable, the model calculations are set up only to show the impact of each of the variables on the average list price. This is not the same as the manufacturing price, but reflects the willingness of the consumer to pay for certain attributes in different years. One possible large impact on the residuals is down to styling and how much the consumer will pay for trim. As previously mentioned, it was beyond the time frame of the project to address this in sufficient depth, though this point is returned to further on.
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Table 3 Utility coefficient values in the subcompact class 1994 to 2002 Year
1994 1995 1996 1997 1998 1999 2000 2001 2002
Utility attribute coefficients subcompact class (¥) Weight
Automatic gearbox
Acceleration
Fuel economy
Brand quality
Brand name
Residuals
10 17 20 22 19 19 20 18 16
1416 1782 1775 2063 1803 2267 2133 1989 2057
2250 800 950 2350 1850 1600 1150 1350 1400
1903 1310 1350 1620 1465 1498 1432 1365 1366
193 164 130 111 166 179 287 278 300
278 149 296 351 593 764 811 893 875
41178 18596 16882 29131 23282 18009 11809 12028 13358
Table 4 Utility coefficient values in the compact class 1994 to 2002 Year
1995 1996 1997 1998 1999 2000 2001 2002
Utility attribute coefficients compact class (¥) Weight
Automatic gearbox
Acceleration
Fuel economy
Brand quality
Brand name
Residuals
10 10 8 4 6 8 9 9
2738 3005 1658 1130 1156 1762 2118 2118
2211 2412 1072 603 134 1072 1474 1474
1060 1171 1012 731 822 963 1073 1087
127 137 127 84 89 55 91 82
203 203 194 222 214 212 212 211
30835 33707 23104 15902 18835 26383 29308 29530
What these calculations avoid is predictions not only of the level of the technology of the fuel cell vehicles but also of the speed of development of internal combustion engine vehicles. If we construct two virtual vehicles, one fuel cell and one internal combustion engine, we can see if there exists in this case a premium on the fuel cell vehicle. Using the 2002 values we can calculate what the difference is between two current fuel cell prototypes, the NECAR V and the HydroGen 3 and their sister A-Class and Zafira vehicles. Table 5 provides the technical data on the four vehicles. Though this table appears to suggest that the current FCV prototypes already exhibit a premium over their internal combustion engine counterparts it must be noted that the conventional modelled vehicles are the manual vehicles and if the automatics were modelled the premiums are reduced to well below 1000 euros. We are also making the assumption that the fuel cell vehicles released in the first years of the market will be of at least the same level of reliability as the conventional propulsion vehicles. 3.3. 2010 A useful way of starting to see what extra values fcv’s could attract, with increased technical attributes, is to per-
Table 5 Characteristics of the NECAR V, HydroGen 3, A Class and Zafira
Weight Automatic gearbox Acceleration Fuel economy Brand name Brand quality Fuel FCV Premium (¥’s)
HydroGen 3 Zafiraa Ford Focus FCV
Focus
1590 1 16 3.76 18 10 LH2 3097
1695 0 11 5.5 12 10 Diesel
1505 0 13 6.6 18 10 Diesel
1727 1 12 3.5 12 10 Compressed H2 3901
a Although the official classification of the Zafira is of a “Small MPV” it is calculated here are a compact class vehicle.
form a straight line extrapolation of the results to 2010. This gives us at least an indication of the levels of value of the extra utility that might be on offer (Table 6). The extrapolation was only taken as far as 2010 and has not taken into account any potential large perturbations, such as those caused by technological breakthroughs, to the system.
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Table 6 Utility coefficient values for 2010 Class
Subcompact Compact
Utility attribute coefficients for 2010 (¥) Weight
Automatic gearbox
Acceleration
Fuel economy
Brand quality
Brand name
Residuals
17 8
1847 2032
1560 1271
1514 970
180 107
448 211
23204 26155
What we can see is that if for example in 2010 the fuel cell vehicle had a fuel economy of 4 l per hundred kilometres and an acceleration of 5 s and the equivalent internal combustion engine vehicle had a fuel economy of 5 l per hundred kilometres and acceleration of 7 s, then the fuel cell vehicle could command a premium of 4634 euros in the compact class and 3512 euros in the subcompact class. Also, if we construct two virtual vehicles, one fuel cell and one internal combustion engine, we can see if there exists in this case a premium on the fuel cell vehicle. 3.4. Utility value of zero carbon dioxide and zero tailpipe emissions One of the heavily publicised benefits of a direct hydrogen fuel cell vehicle is the zero-regulated emissions and zero carbon dioxide emissions. The unwritten hypothesis in these statements is that the consumer will view this a benefit and will be prepared to pay extra for this. The environmental benefits will be viewed as something for which a premium will be paid, along the lines of organic foods, eco washing powder, etc. From the above model we need to look at the utility to the consumer, like zero tailpipe emissions and or zero carbon dioxide emissions. As we cannot directly measure this subjective value, we need to use information from stated preference surveys where the consumers themselves state the level of utility provided from this attribute. The number of these surveys that are publicly available is low and throughout this research one survey available on internet has been relied upon [27]. This survey lists a number of vehicle attributes such as image, styling, running costs, price and the highly loaded term ‘environmental friendliness’. This value-loaded term comes out as the 10th most important in the list of adoption criteria. What is interesting though is that although we may have been able to guess the placing of the environmental friendliness of the vehicle in the list, we probably would not have guessed that styling and image were the two lowestvalued attributes on the list. Though, it could be argued, this is the logical placing for these values, subjectively it would be assumed that the importance to the consumer was higher. This, aside though, shows that the altruistic value attached to environmental qualities, of which emissions are just one part, are substantially lower than other utility values. This is the reason why the regression analysis has
not measured carbon dioxide values as having a utility and part of the list price but as part of running costs which is another separate segment of the adoption decision process.
4. Discussion and conclusions The results presented in this paper, and of this research, have tried to avoid single-point forecasting on market shares of fuel cell vehicles and adoption probabilities. The extrapolations to 2010 are just that, extrapolations based on historical data and a ‘business as usual’ future for the next seven years. If any shocks occur to the system, such as the EU introducing a European version of the Californian Clean Air Act, there being a fundamental break in the relationship with the private car, or another oil crisis, then the results breakdown. The reason behind the research was to question the commonly held argument, often seen in literature on fuel cell vehicles, which focuses on the need to bring down the costs of fuel cell vehicles to that comparable with the internal combustion engine vehicle, either list price or lifetime costs. This research shows that there is a market segment, and time in the adoption process, when the costs are not the critical adoption factor but the usefulness or utility offered by the vehicle is. Instead of trying to predict the rate of change of the new product and its competing product, the research has developed a methodology of allowing calculations to be made during different market segments. The research results presented above are only part of measuring the utility of the vehicle. The price is only one segment of the overall utility. Running costs, safety and reliability also are important adoption criteria from which the consumer makes an overall decision to adopt or not. What this research has so far shown is that certain utility attributes, such as a higher fuel economy, could command a significant premium before the product reaches the mass market. The caveat in this statement is that the fuel economy attribute would need to be higher than that of its “competitor”, an internal combustion engine powered vehicle. Potentially the results could also be used to help derive subsidy levels if governments or companies decide that they want to directly support the adoption process of technology with defined attributes, such as 3 l per 100 km fuel economy, by fiscal means. This could avoid the issue of technology
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forcing as it would mean that any vehicle with the required fuel economy could be eligible to receive the subsidy that the market, here the consumer, would be willing to pay.
References [1] Abernathy WJ, Clark KB. Innovation—mapping the winds of creative destruction. Res Policy 1985;14(1):3–22. [2] Ayres R. On forecasting discontinuities. Technol Forecast Soc Change 2000;65:81–97. [3] Bower JL, Christensen CM. Disruptive technologies— catching the wave. Har Bus Rev 1995;73(1):43–53. [4] Hughes PM, Cosier G. What makes a revolution? Disruptive technology and social change. BT Technol J 2001;19(4):24–8. [5] Linton JD. Forecasting the market diffusion of disruptive and discontinuous innovation. IEEE Trans Eng Manage 2002;49(4):365–74. [6] Rothaermal FT. Technological discontinuities and interfirm cooperation: what determines a start-up’s attractiveness as alliance partner. IEEE Trans Eng Manage 2002;49(4):388–97. [7] Walsh ST, Kirchhoff BA, Newbert S. Differentiating market strategies for disruptive technologies. IEEE Trans Eng Manage 2002;49(4):341–51. [8] Chen H, Crowston K. Comparative diffusion of the telephone and the world wide web: an analysis of rates of adoption. First accessed on 02/07/2003 available at: www.bnet. fordham.edu/public/mrktg/mflicker/comparative.html [9] Community Intelligence. Radical innovation with communities of practice. First accessed on 04/08/2003 available at: http://www.communityintelligence.co.uk/ [10] Martinich L. Commercialising and managing innovations. First accessed on 04/08/2003 available at: www. competitivefocus.com/paper1.htm [11] Adner R. When are technologies disruptive? a demand-based view of the emergence of competition. INSEAD Working Papers, 2001. [12] Adner R, Zemsky P. Disruptive technologies and the emergence of competition. INSEAD Working Papers, 2001. [13] Arnold HM. Can great companies survive technology shocks? A literature overview. Working Paper, Fakultät für Betriebwirtschaft, Ludwig-Maximilians-Universität, 2001.
[14] Mytelka LK. New wave technologies: their emergence diffusion and impact. UNU/INTECH Discussion Paper, ISSN 1564-8370, 2002. [15] Christensen CM. The innovator’s dilemma: when new technologies cause great firms to fail. Watertown: Harvard Business School Press; 1997. [16] Freeman C, Soete L. The economics of industrial innovation. London: Thomson Learning; 1997. [17] Rogers EM. Diffusion of innovation. New York: The Free Press; 1995. [18] Utterback JM. Mastering the dynamics of innovation: how companies can seize opportunities in the face of technological change. Watertown: Harvard Business School Press; 1994. [19] Flink JJ. America adopts the automobile, 1895–1910. Cambridge: The MIT Press; 1970. [20] Information on Ford Model T workshops, such as “Phoenix Mill”, can be found at various sources on the Internet. For example: http://www.uaw.org/solidarity/03/0503/feature08. cfm, first accessed on 05/01/2003. [21] Preiss B. Forbes greatest business stories of all time. New York: Wiley; 1996. [22] Bounds G, Ranney G, Yorks L, Adams M. Beyond total quality management: toward the emerging paradigm. New York: McGraw-Hill Education; 1994. [23] David P, Wright G. General purpose technologies and surges in productivity: historical reflections on the future of the ICT revolution. Presented at economic challenges of the 21st century in historical perspective, Oxford, 2–4 July 1999. [24] Ehrnberg E. On the definition and measurement of technological discontinuities. Technovation 1995;15(7): 437–52. [25] Adamson K-A. An examination of consumer demand in the secondary niche market for fuel cell vehicles in Europe. Int J Hydrogen Energy 2003;28(7):771–80. [26] Gillriches Z. Hedonic price indexes for automobiles: an econometric analysis of quality change. In: Stigler G (Chairman) Price statistics of the federal government. New York: National Bureau of Economic Research; 1961. pp. 137–196 [27] Claritas Automotive Report. Understanding the motivational influencing and loyalty factors of GB car buyers, Claritas, London, 2001.
Calculating the price trajectory of adoption of fuel cell vehicles Kerry-Ann Adamson∗ Fuel Cell and Hydrogen Research Centre, Technical University of Berlin, Energy Systems, Sekr. TA8, Einsteinufer 25, 10587 Berlin, Germany Received 2 April 2004; received in revised form 27 July 2004; accepted 28 July 2004 Available online 27 September 2004
Abstract How do you model consumer behaviour for disruptive technologies? Technologies that potentially have no antecedents and that, by their very definition, change consumer behaviour patterns? This paper outlines a methodology and results employed during a study to model the consumer willingness to pay for fuel cell vehicles, a potential disruptive innovation (DI). The first part of the study provides a short overview on DI highlighting why the fuel cell family of technologies may represent an upcoming DI. From the post ante study of successful historical disruptive innovations a number of initial ‘rules of adoption’ can be sketched. Further narrowing of the focus on economic reasons for adoption provides a framework for which the willingness to pay for the new disruptive technology, such as, here, fuel cell vehicles, can be analysed during different phases of the market. This economic framework is then applied to the potential future market of fuel cell vehicles using information from a model that was built from vehicles during the build years 1994–2002 in the subcompact, compact and luxury class. The results presented in this paper concentrate on the subcompact and compact class of vehicle and supersede the initial results previously published. Finally, there is a short discussion on different pathways that this can be taken forward and used to help in policy decisions. 䉷 2004 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved. Keywords: Disruptive innovations; Adoption; Fuel cell vehicles; Economics
1. Introduction Disruptive innovations are (DIs) innovations that at some level cause a large perturbation in the system, whether in the market structure, product or consumer behaviour. These innovations are the focus of a number of published papers, for example Refs. [1–7], internet based papers, for example Refs. [8–10], working papers, for example Refs. [11–14], books, for example [15–18] and management courses. Famous examples from the last 150 years clearly show these system perturbations. The telephone has helped ∗ Tel.: +49-30-314-79123; fax: +49-30-314-26908.
E-mail address: [email protected] (K.-A. Adamson).
society to develop condensed high-rise inner city living [7] whilst the car has encouraged urban sprawl and commuter life [19]. Production process changes such as Fords revolutionary Model T production process encouraged women into the work place, providing one of the first examples of women earning the same wage as men [20] and also enabled such an reduction in the price of the vehicle that it became available to the true mass market [21]. Xerox’s transformation happened because of their DI in their management systems and company mission, with the impact to become a market-leading product-leasing company [22]. These are all examples of innovations that caused radical perturbations within the embedded system. Fuel cells are a technology that, within limited applications, are nearing market entry. Currently still experiencing
0360-3199/$30.00 䉷 2004 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2004.07.004
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high technology push with lack of defined product attributes, and a number of substantial engineering hurdles still to be overcome, they could represent a future DI. This statement is based on the potential of fuel cells to produce distributed energy systems and also, in the longer term, potentially redefining the relationship between a car owner and the vehicle. Depending on which market they address, they could cause perturbations in the energy market and automotive industry, move towards sustainable development, electronics markets and personal communication. If fuel cell vehicles are released into the market place, they will either be part of a product family, for example, laptops, power generation, etc, therefore representing a new core purpose technology [23], or they will be released solely in one market place, most likely in the propulsion systems market place. As with all new product innovations, incremental or discontinuous, the chances of market success depend on a large range of factors and are, in general, low. The research that these results have been taken from looked at adoption patterns of a small number of successful historical market/consumer-based disruptive technologies. Any underlying economic patterns of adoption that could be drawn out of the information were then taken forward and used in a hypothetical future European fuel-cell vehicle market. The first part of the paper provides a basic taxonomy of DIs from which an overview of the economic pathway of adoption, through niche markets, for market-based DIs is evolved. The second part reproduces the results on the modelling of the level of premium that the secondary niche market adopters would be prepared to pay for a number of heavily citied attributes of fuel cell vehicles. The model covers two classes of vehicle, the subcompact and compact class, of which a number of the prototype vehicles, such as the Mercedes NECAR and the Ford Focus FCV, have been produced.
2. Taxonomy and adoption of DIs Fuel cells still face a number of substantial technical and engineering challenges. Water management and platinum loading are just two. Besides these, hydrogen, the fuel for the fuel cell, is also still some way from finding the perfect storage medium, for vehicles at least, and the infrastructure debate is still raging. Although some fuel cell technologies are close to being at market entry, these are still small scale markets, such as distributed power in island economies, rather than mainstream mass markets such as fuel cell vehicles. The reason that this paper focusses is on fuel cell vehicles, and not on the fuel cell family of products in general, is that here in this specific market it addresses a special case of lock in of an incumbent technology that is currently viewed as being efficient and having room for improvement. This competition between fuel cells and the internal combustion
engine was the focus of the study and how each impacts the chances of a paradigm shift. The current date for projected fuel cell vehicles to be economically viable for the mass market is somewhere between 2020 and 2030. This is for the mass market though. Before the mass market comes niche markets. These niche markets are critical as they provide room for product improvement and cost reduction, as well as the product being “on view” to the mass market. The rest of this section outlines the adoption through the niche markets based on the theory that fuel cells could represent a Discontinuous Innovation. It starts with a brief overview of DIs. 2.1. DI taxonomy There are different types of DI. As each type causes different impacts and follows different adoption and diffusion paths by understanding what type of DI is being represented we have a narrower research focus to understand better the adoption processes. There have been a limited number of papers on classification of DIs, with currently the most cited being Ehrnberg’s 1995 paper [24]. She classifies a number of disruptive innovations based on competence change, physical product change or change in the price or performance level. The basic taxonomy for DIs splits DIs into innovations that affect the market or impact on the product. Fuel cells are a technology that affect a product and it is the use of the product that impacts the user and market place; therefore for the rest of this paper other forms of DIs say, in management structure will no longer be considered. If DIs represent consumer technologies once integrated into products and adopted by the market place, they then cause a fundamental change in behaviour patterns. Because of this shift in behaviour patterns researchers modelling adoption decisions face a conundrum—how do you model something that alters behaviour without first knowing what the new behaviour will be—but how do you know what the new behaviour patterns will be from a product with potentially no antecedents? The current study used historical information on adoption decisions and diffusion patterns of a small number of successful market-based DIs drawing together a short list of ‘rules for adoption’ which showed that within the niche markets consumer behaviour can be modelled using standard economic techniques [25]. This is due to the paradigm shift caused by the large scale perturbation occurring during the mass market phase, not during the niche market phase. This is not to say that any perturbations do not occur until this point but that there is a build-up of small scale ‘system shocks’, whether the system is social, economic, or legislative; and these weaken the system so much so that when a larger ‘system shock’ happens during adoption in the mass market this causes a fundamental paradigm shift. As we can use standard economic techniques until the mass market we can model the niche market decision and adoption
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patterns based on a number of clear and documented assumptions. 2.2. Niche market to mass market—economic modelling of adoption A niche market is a small protected market that a new DI product enters before it reaches the mass market. It starts upon its economic and learning trajectory, which form the basis of cost reductions and product standardisation, before attempting to enter the mass market. For market-based DIs there appears to be two main consumer niches,1 the primary and secondary, both with very different reasons for product adoption, the technological niche. Also both niches have differing economic decisions and adoption/adopter profiles. 2.2.1. Primary niche market Why do some people adopt an untried, untested, new, risky, high-cost product? These first adopters, the ‘Innovators’ in Roger’s seminal work [17], adopt the product 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 adoption costs. This is shown in Eq. (1), i.e., modelling the adoption decisions of the primary niche market adopters T a = > ,
(1)
where T 1 is the adoption of technology T, during niche a, at time t; the weighted value of X factor; and the weight adoption costs. Adoption costs include price of the product, infrastructure, training, etc. Both of the X factor and adoption costs are weighted to represent so that both have different levels of importance to different people. There may be some cases in certain groups where the X factor is very highly weighted, but so also are the adoption costs. Technology adoption in third world countries might fall into this area. 2.2.2. Secondary niche market The new product here starts to compete directly with the product that is already in 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. In this niche product price is one measure of utility to be calculated alongside a number of others. 1 Though it is recognised that car fleet purchases by govern-
ments or companies are niche markets, a protected market bounded by size, they are not included here as this work is based on private consumer adoption, and although fleet markets can help with altering economics they do not directly impact upon private consumer purchase decisions.
343
During this niche market it is assumed that the potential adopters are faced with the choice of staying within the known technology such as the internal combustion engine vehicle or adopting a new technology, for example a fuel-cell vehicle. 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, therefore the X factor does not come into the decision-making process as the attributes under consideration are those already offered by the incumbent technology. The relationship of this decision process therefore can be formally written as Eq. (2), i.e. adoption decision within the secondary niche market T tb = U1t + U2t + U3t + · · · + Ukt ,
(2)
where is the adoption of technology T, during niche b, at time t; Ukt the utility of attribute k in time t; k the attribute; and the weighting. From these two niches we can see that a premium of adoption is acceptable as long as the new product is economically worth adopting to the different adopter groups. Although we can make some assumptions as some of the initial adoption costs which would be faced by primary niche market adopters, others are harder to value. Also, as we would need to make ‘guesses’ as to what an X factor from a fcv might be, calculation of the premium in the primary niche market would be difficult until we are closer to the point of market entry when we have more detailed information and cost estimates. Due to the secondary niche markets, adoption decisions being based on the attributes of the incumbent product calculation of this premium is more straightforward and also avoids having to make any predictions about the characteristics of a future fuel cell vehicle. In summary, it is not until the mass market that the disruptive product needs to compete economically with the embedded product. During the prevalence of niche markets the product is adopted with a premium of adoption based directly on the products attributes. Once the product reaches the mass market it is only then that the consumer behaviour patterns start to change. Because of this we can model consumer adoptions patterns in the niche markets based on trends observed from historical post ante information. 3. Modelling the premium of adoption that secondary niche market adopters would be prepared to pay for the utility provided by fuel cell vehicles 3.1. Model background At the start of the market fuel cell vehicles will be economically more expensive than the equivalent internal combustion engine vehicle. However, how the consumer sees the premium depends on whether companies or governments subsidise the product. These subsidies will only occur if governments or regulatory bodies decide that the
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benefits due to the adoption and diffusion of the technology are high enough to provide the necessary programmes. If there does exist a market-visible adoption premium, then the fuel cell vehicle will develop along a price premium trajectory via niche markets to reduce the premium to the economic ‘break-even’ point with the internal combustion engine. Ideally this break-even point occurs at the start of the mass market. In Adamson [25] initial results from the model was discussed. Note that all results published here supersede these. The model that was created was based upon Gillriches hedonic techniques [26] which hypothesises that, all things being equal, the list price of a vehicle is made up as the summation of a number of component parts, which, provided that there is enough data, either cross-section or over time, can be disaggregated through regression analysis. Hedonic list price of a vehicle as a function of its attributes is as follows: Pit = ft (x1it , x2it x3it · · · xkit , it ),
(3)
where Pit is the price of model i, at time t; x1it the vehicle attribute 1, of model i, at time t; xkit the vehicle attribute k, of model 1, at time t; and it the residuals. Each attribute contributes a certain amount to the overall price of the vehicle, as shown in Eq. (4), i.e., Hedonic list price of a vehicle shown as a summation of attribute prices Pit = p1t + p2t + · · · + pkt , (4) where P1t is the price of attribute 1 in time t and Pkt the price of attribute k in time t. For the regression analysis the model collected information from 2 publicly available databases on a total of 12,364 vehicles over the build years 1994 to 2002. The model used three categories of vehicles—subcompact (2973 vehicles), compact (5938 vehicles) and luxury (3453 vehicles). The only rule applied as to which vehicles to include is that they had to be freely available across the European Union, which excluded a number of subcompact and luxury cars that are only available as import from Japan or the USA. This paper only covers the results from the first two vehicles classes, subcompact and compact. The information that was collected on the vehicle attributes was: • • • • • • • • • • •
list price automatic gearbox carbon dioxide emissions noise displacement rated torque rated power top speed wheel base fuel consumption acceleration
Table 1 ADAC survey values for brand name and brand quality Brand
Brand name
Brand quality
VW Vauxhall/Opel Mercedes Benz Audi Peugeot Toyota Ford Honda Skoda Nissan Seat
18 18 17 17 14 12 12 9 8 7 5
12 10 15 13 9 14 10 12 8 9 7
• weight • brand name • brand quality Brand name and brand quality measurements were taken from a 2002 ADAC (German Automobile Association) survey on the car manufacturers and their product performance. The values included in the model are shown below in Table 1. One recognised shortcoming of the model is that by using these survey results it was implied that all products from one manufacturer exhibit the same level of quality performance, and that this performance has been held static over time. Also, the model therefore assumes that the subjective values held by German car drivers, and members of the ADAC, who were polled for the value of the name and quality, are representative as an average member of the EU. As these are the most “subjective” valuations used in the model it is suggested that the results of these two variables are treated with the highest level of care. The inclusion of an automatic gearbox was measured using a dummy variable, 1 being the inclusion of an automatic gearbox. It was recognised that although there are also a number of non-technical characteristics, such as the styling of the vehicle that go into the aggregated list price these have not been analysed here. This would only be possible with a highly detailed, and consistent, survey of all the styling and trims of the vehicles included. Although the consumers adopt on decisions based on utility or usefulness, due to problems of multi-collinerarity (double counting), we cannot directly measure this utility in a single-step regression. For example, we recognise that consumers find the fuel economy of a vehicle an important adoption decision but fuel economy is directly linked to a number of mechanical attributes, such as weight and power. These in turn are also linked to other functions, such as acceleration. The impact of this is that although the consumer finds fuel economy and acceleration important criteria for selecting a car, we cannot directly model these functions
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without running the risk of “double counting” the technical attributes, which in turn would create instabilities in the results. This is shown in Eq. (5), i.e., utility value of a vehicle as a function of its attributes
345
25
(5)
where U1it is the utility function 1 of model i, in time t and xkit the vehicle attribute k, of model i, at time t. The previous list shows the technical attributes that we make available to model while the utility attributes we would like to be able to model are:
ACCELERATION
20
U1it = f (xyit , xzit · · · xkit ),
15
10
• • • • •
fuel economy acceleration running costs emissions having an automatic gearbox.
It was assumed that the level of utility gained from zero or reduced emissions was close to zero but emissions were included under the factor, Running Costs. The reason behind this is that in some countries, such as the UK, vehicle tax is based on carbon dioxide emissions. The impact of this assumption will be returned to and discussed later on. Initially the model was set up in a single-step regression [24] with the aim of splitting the utility into two sections—the utility from running costs and the utility from mechanical attributes. It was subsequently found that this could be circumvented by using this relationship between the mechanical attributes and the utility attributes. The final model therefore was done in a three-step process. First, using equation five, the utility functions of fuel economy and acceleration were regressed against their technical attributes. Next, using equation three, the list price of the vehicles in each year and class were regressed against their technical attributes. Once these were complete, the fuel economy and acceleration were substituted into the list price equations to form the final equations on consumer willingness to pay for attributes commonly promoted for fuel cell vehicles. 3.2. Model results Due to the size of the database and the possible number of permutations of calculations, the results from the model are extensive. The results shown here are concentrated around the consumer willingness to pay for higher fuel economy and acceleration. The reason that acceleration, measured in seconds taken to accelerate to sixty miles per hour, was chosen as a focus is for two reasons: 1. Currently acceleration is directly linked to carbon dioxide emissions and is also highly correlated with the price of the vehicle, as shown in Fig. 1; the higher the price of
5 0
50000
100000
15000
LIST_PRICE Fig. 1. Time to accelerate 0–60 in seconds against Euro list price.
the vehicle the higher the proportion that comes from increased acceleration. 2. Acceleration can be seen as a partial proxy for safety, being able to accelerate out of dangerous situations. Initially the historical section of the model was completed and analysed for its ability to predict the actual list price, given the average vehicle characteristics from the year in question. Table 2 shows the results from the compact class of vehicle. To read the table the column of coefficients represent the euro value of each unit of the attribute, in the compact class. The 95% confidence intervals represent the higher and lower band within which there is a 95% chance of the value falling. The narrower the bands the higher the confidence in the predicted coefficient. Figs. 2 and 3 plot the R 2 ’s over time in the two vehicle classes and the percentage differences between the predicted and the actual euro prices. What the table highlights is that in the compact class the R 2 values were high, the lowest being 0.70, and a fairly uniform level of deviation exists between the actual and predicted prices. In the subcompact class the year 1994 was dropped from the analysis due to having the lowest levels of data available, and an unacceptable percentage difference between actual and predicted values (33%). This value is not shown on the graph. The 14% difference in 1996 can be explained by the inclusion of a couple of ‘special edition’ vehicles with higher prices but identical technical specifications, only the styling of the vehicle being altered. If these are removed from the database, then this percentage difference is substantially reduced. As already mentioned, the 14% difference in 1996 can mainly be attributed to the inclusion of a small number of special edition vehicles. If the
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Table 2 Results for the compact class vehicle attributes Coefficient
Std. error
+95% Confidence Interval
−95% Confidence Interval
t-statistic
Probability
R 2 = 0.84, Degrees of freedom=265, Difference between actual and predicted price=9% 1994 Power 76.86046 5.800955 88.2303318 Torque 36.80793 3.065028 42.81538488 Automatic gearbox 2311.179 279.4552 2858.911192 Brand name 287.9523 29.6773 346.119808 Brand quality 698.4026 50.8769 798.121324
65.4905882 13.24962 30.80047512 12.009 1763.446808 8.270302 229.784792 9.702779 598.683876 13.7273
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.84, Degrees of freedom=348, Difference between actual and predicted price=3% 1995 Power 75.81861 3.537507 82.75212372 Torque 39.94723 1.979121 43.82630716 Automatic gearbox 2206.698 167.0682 2534.151672 Brand name 262.7355 17.41002 296.8591392 Brand quality 696.5837 31.29681 757.9254476
68.88509628 36.06815284 1879.244328 228.6118608 635.2419524
21.43278 20.18433 13.20837 15.09105 22.25734
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.82, Degrees of freedom=418, Difference between actual and predicted price=5% 1996 Power 87.01517 4.355798 95.55253408 Torque 50.46475 2.842968 56.03696728 Automatic gearbox 2328.828 201.8329 2724.420484 Brand name 264.8993 20.23712 304.5640552 Brand quality 601.4794 41.60486 683.0249256
78.47780592 44.89253272 1933.235516 225.2345448 519.9338744
19.97686 17.75073 11.5384 13.08977 14.45695
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.76, Degrees of freedom=494, Difference between actual and predicted price=8% 1997 Power 77.41528 4.86512 86.9509152 Torque 61.95318 3.389777 68.59714292 Automatic gearbox 2336.723 205.9463 2740.377748 Brand name 163.1245 23.62709 209.4335964 Brand quality 681.8973 46.03033 772.1167468
67.8796448 55.30921708 1933.068252 116.8154036 591.6778532
15.91231 18.27648 11.34627 6.904131 14.81409
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.70, Degrees of freedom=559, Difference between actual and predicted price=2% 1998 Power 61.81287 6.138732 73.84478472 Torque 62.2722 4.534654 71.16012184 Automatic gearbox 1740.016 253.9339 2237.726444 Brand name 151.2598 29.23156 208.5536576 Brand quality 568.5503 65.06767 696.0829332
150.8745101 144.0084928 4639.87773 437.9967289 1429.390219
10.06932 13.73252 6.852241 5.174539 8.73783
0.0000 0.0000 0.0000 0.0000 0.0000
R 2 = 0.77, Degrees of freedom=879, Difference between actual and predicted price=5% 1999 Power 51.05405 6.673602 64.13430992 Torque 75.17966 5.954608 86.85069168 Automatic gearbox 1956.736 234.9394 2417.217224 Brand name 86.25374 28.43523 141.9867908 Brand quality 383.6339 64.73304 510.5106584
132.3768494 176.1819637 4972.685159 306.72934 1065.33393
7.650149 12.62546 8.328686 3.033341 5.9264
0.0000 0.0000 0.0000 0.0025 0.0000
R 2 = 0.77, Degrees of freedom=1051, Difference between actual and predicted price=1% 2000 Power 82.1506 6.955487 95.78335452 Torque 50.37804 6.470654 63.06052184
194.6908619 130.0692768
11.81091 0.0000 7.785619 0.0000
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Table 2 (continued) Coefficient
Std. error
−95% Confidence Interval
+95% Confidence Interval
Probability
Brand quality 340.9626 71.59112 481.2811952 Automatic gearbox 1575.078 234.0752 2033.865392 Brand name 116.7554 26.92396 169.5263616 R 2 = 0.79, Degrees of freedom=1272, Difference between actual and predicted price=4% 2001 Power 83.7257 6.724676 96.90606496 Torque 41.98081 5.986859 53.71505364 Automatic gearbox 1551.583 216.2585 1975.44966 Brand name 146.0237 30.00015 204.823994 Brand quality 177.9188 68.25412 311.6968752
196.6605633 12.45052 111.2683641 7.01216 4088.139834 7.174668 431.4551782 4.867431 679.1799954 2.606712
0.0000 0.0000 0.0000 0.0000 0.0095
R 2 = 0.8, Degrees of freedom=446, Difference 2002 Power 83.38128 7.757863 Torque 45.26086 7.405706 Automatic gearbox 1415.633 229.5712 Brand name 192.6154 41.37761 Brand quality 277.8504 90.40479
200.9877783 10.74797 124.5667518 6.11162 1489.018106 6.166421 744.9206206 4.655063 437.7038313 3.073403
0.0000 0.0000 0.0000 0.0000 0.0023
98.58669148 59.77604376 642.574952 358.9505156 177.1933884
1 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 1995
1996
1997
1998
Subcompact
1999
2000
2001
2002
Compact
Fig. 2. Comparative R 2 values for the subcompact and compact class. 16 14 12 10 8 6 4 2 0 1994
1995
1996
1997 Compact
1998
1999
4.762638 0.0000 6.72894 0.0000 4.336485 0.0000
between actual and predicted price=8%
0.95
1994
1014.902263 4220.451368 359.1956287
t-statistic
2000
2001
2002
Subcompact
Fig. 3. Comparative % difference between actual and predicted prices for subcompact and compact class.
database was sufficiently large, then these outlying values would be smoothed out but as the model already incorporates information on all vehicles in the compact and subcompact classes available in Europe during this time, this smoothing effect is not possible. The compact class list price regression produced no variables that were classed as statistically insignificant, using a p-value < 0.05. As can be seen in the table the technical attributes that were used in the list price regression were power, torque, weight, automatic gearbox, brand name and brand quality. The subcompact class regressions were not as smooth, potentially because of the smaller number of data points, and the included technical attributes varied per year due to result instabilities. These first step regression results were then applied to the equation linking fuel economy and acceleration to a number of technical attributes. The final results are shown below in Tables 3 and 4. Fuel economy is measured in litres per hundred kilometre, so for example in 1994 in the subcompact class, every decrease in litre per hundred kilometres is valued at 1903 euros. Similarly for acceleration every decrease in per second needed to accelerate from 0 to 60 is valued at 2250 euros. Note that although the residuals are large and variable, the model calculations are set up only to show the impact of each of the variables on the average list price. This is not the same as the manufacturing price, but reflects the willingness of the consumer to pay for certain attributes in different years. One possible large impact on the residuals is down to styling and how much the consumer will pay for trim. As previously mentioned, it was beyond the time frame of the project to address this in sufficient depth, though this point is returned to further on.
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Table 3 Utility coefficient values in the subcompact class 1994 to 2002 Year
1994 1995 1996 1997 1998 1999 2000 2001 2002
Utility attribute coefficients subcompact class (¥) Weight
Automatic gearbox
Acceleration
Fuel economy
Brand quality
Brand name
Residuals
10 17 20 22 19 19 20 18 16
1416 1782 1775 2063 1803 2267 2133 1989 2057
2250 800 950 2350 1850 1600 1150 1350 1400
1903 1310 1350 1620 1465 1498 1432 1365 1366
193 164 130 111 166 179 287 278 300
278 149 296 351 593 764 811 893 875
41178 18596 16882 29131 23282 18009 11809 12028 13358
Table 4 Utility coefficient values in the compact class 1994 to 2002 Year
1995 1996 1997 1998 1999 2000 2001 2002
Utility attribute coefficients compact class (¥) Weight
Automatic gearbox
Acceleration
Fuel economy
Brand quality
Brand name
Residuals
10 10 8 4 6 8 9 9
2738 3005 1658 1130 1156 1762 2118 2118
2211 2412 1072 603 134 1072 1474 1474
1060 1171 1012 731 822 963 1073 1087
127 137 127 84 89 55 91 82
203 203 194 222 214 212 212 211
30835 33707 23104 15902 18835 26383 29308 29530
What these calculations avoid is predictions not only of the level of the technology of the fuel cell vehicles but also of the speed of development of internal combustion engine vehicles. If we construct two virtual vehicles, one fuel cell and one internal combustion engine, we can see if there exists in this case a premium on the fuel cell vehicle. Using the 2002 values we can calculate what the difference is between two current fuel cell prototypes, the NECAR V and the HydroGen 3 and their sister A-Class and Zafira vehicles. Table 5 provides the technical data on the four vehicles. Though this table appears to suggest that the current FCV prototypes already exhibit a premium over their internal combustion engine counterparts it must be noted that the conventional modelled vehicles are the manual vehicles and if the automatics were modelled the premiums are reduced to well below 1000 euros. We are also making the assumption that the fuel cell vehicles released in the first years of the market will be of at least the same level of reliability as the conventional propulsion vehicles. 3.3. 2010 A useful way of starting to see what extra values fcv’s could attract, with increased technical attributes, is to per-
Table 5 Characteristics of the NECAR V, HydroGen 3, A Class and Zafira
Weight Automatic gearbox Acceleration Fuel economy Brand name Brand quality Fuel FCV Premium (¥’s)
HydroGen 3 Zafiraa Ford Focus FCV
Focus
1590 1 16 3.76 18 10 LH2 3097
1695 0 11 5.5 12 10 Diesel
1505 0 13 6.6 18 10 Diesel
1727 1 12 3.5 12 10 Compressed H2 3901
a Although the official classification of the Zafira is of a “Small MPV” it is calculated here are a compact class vehicle.
form a straight line extrapolation of the results to 2010. This gives us at least an indication of the levels of value of the extra utility that might be on offer (Table 6). The extrapolation was only taken as far as 2010 and has not taken into account any potential large perturbations, such as those caused by technological breakthroughs, to the system.
K.-A. Adamson / International Journal of Hydrogen Energy 30 (2005) 341 – 350
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Table 6 Utility coefficient values for 2010 Class
Subcompact Compact
Utility attribute coefficients for 2010 (¥) Weight
Automatic gearbox
Acceleration
Fuel economy
Brand quality
Brand name
Residuals
17 8
1847 2032
1560 1271
1514 970
180 107
448 211
23204 26155
What we can see is that if for example in 2010 the fuel cell vehicle had a fuel economy of 4 l per hundred kilometres and an acceleration of 5 s and the equivalent internal combustion engine vehicle had a fuel economy of 5 l per hundred kilometres and acceleration of 7 s, then the fuel cell vehicle could command a premium of 4634 euros in the compact class and 3512 euros in the subcompact class. Also, if we construct two virtual vehicles, one fuel cell and one internal combustion engine, we can see if there exists in this case a premium on the fuel cell vehicle. 3.4. Utility value of zero carbon dioxide and zero tailpipe emissions One of the heavily publicised benefits of a direct hydrogen fuel cell vehicle is the zero-regulated emissions and zero carbon dioxide emissions. The unwritten hypothesis in these statements is that the consumer will view this a benefit and will be prepared to pay extra for this. The environmental benefits will be viewed as something for which a premium will be paid, along the lines of organic foods, eco washing powder, etc. From the above model we need to look at the utility to the consumer, like zero tailpipe emissions and or zero carbon dioxide emissions. As we cannot directly measure this subjective value, we need to use information from stated preference surveys where the consumers themselves state the level of utility provided from this attribute. The number of these surveys that are publicly available is low and throughout this research one survey available on internet has been relied upon [27]. This survey lists a number of vehicle attributes such as image, styling, running costs, price and the highly loaded term ‘environmental friendliness’. This value-loaded term comes out as the 10th most important in the list of adoption criteria. What is interesting though is that although we may have been able to guess the placing of the environmental friendliness of the vehicle in the list, we probably would not have guessed that styling and image were the two lowestvalued attributes on the list. Though, it could be argued, this is the logical placing for these values, subjectively it would be assumed that the importance to the consumer was higher. This, aside though, shows that the altruistic value attached to environmental qualities, of which emissions are just one part, are substantially lower than other utility values. This is the reason why the regression analysis has
not measured carbon dioxide values as having a utility and part of the list price but as part of running costs which is another separate segment of the adoption decision process.
4. Discussion and conclusions The results presented in this paper, and of this research, have tried to avoid single-point forecasting on market shares of fuel cell vehicles and adoption probabilities. The extrapolations to 2010 are just that, extrapolations based on historical data and a ‘business as usual’ future for the next seven years. If any shocks occur to the system, such as the EU introducing a European version of the Californian Clean Air Act, there being a fundamental break in the relationship with the private car, or another oil crisis, then the results breakdown. The reason behind the research was to question the commonly held argument, often seen in literature on fuel cell vehicles, which focuses on the need to bring down the costs of fuel cell vehicles to that comparable with the internal combustion engine vehicle, either list price or lifetime costs. This research shows that there is a market segment, and time in the adoption process, when the costs are not the critical adoption factor but the usefulness or utility offered by the vehicle is. Instead of trying to predict the rate of change of the new product and its competing product, the research has developed a methodology of allowing calculations to be made during different market segments. The research results presented above are only part of measuring the utility of the vehicle. The price is only one segment of the overall utility. Running costs, safety and reliability also are important adoption criteria from which the consumer makes an overall decision to adopt or not. What this research has so far shown is that certain utility attributes, such as a higher fuel economy, could command a significant premium before the product reaches the mass market. The caveat in this statement is that the fuel economy attribute would need to be higher than that of its “competitor”, an internal combustion engine powered vehicle. Potentially the results could also be used to help derive subsidy levels if governments or companies decide that they want to directly support the adoption process of technology with defined attributes, such as 3 l per 100 km fuel economy, by fiscal means. This could avoid the issue of technology
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forcing as it would mean that any vehicle with the required fuel economy could be eligible to receive the subsidy that the market, here the consumer, would be willing to pay.
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