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2 Technological Forecasting & Social Change 78 (2011) Contents lists available at ScienceDirect Technological Forecasting & Social Change Forecasting sales and product evolution: The case of the hybrid/electric car Yair Orbach, Gila E. Fruchter Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan 52900, Israel article info abstract Article history: Received 20 August 2010 Received in revised form 15 March 2011 Accepted 16 March 2011 Available online 16 April 2011 Keywords: Technology evolution Forecasting Pre-launch Diffusion Generation substitution We present a model that forecasts sales and product evolution, based on data on market and industry, which can be collected before the product is introduced. Product evolution can be incremental but can also take place by releasing new generations. In our model adoption of a new product is motivated by attribute improvements (enabled by technology evolution), and firms' attribute improvements strategies are motivated by market growth and directed by market preferences. The interdependency between attributes' improvements and cumulative adoption level makes the problem inherently dynamic. The dependency of attribute levels on adoption levels is assessed using industry and technology analysis. Market preferences and purchase intention response to attribute levels changes are assessed based on a conjoint study. The option of collecting and interpreting data about both demand and supply aspects, before the new product is introduced, enables us to estimate sales and technology progress endogenously rather than to require them as inputs. We demonstrate the method on the hybrid car market Elsevier Inc. All rights reserved. 1. Introduction Many product categories are characterized by continuous incremental improvements, with enhanced features and performance, and sometimes also by releasing new generations, with new configurations of attributes. Usually, a new generation is released when the older generations have been in the market for a while. Mobile phones, laptops, operating systems, thin flat TV, DVDs and broadband modems are just a few examples of products with frequent introduction of improved versions and market rapid growth of several generations simultaneously. As mentioned by Gruppa and Stadlerb [26], a rapid market growth is usually accompanied by both radical technological improvements and price decline. For products that failed in the market, like the Video Laser Disk or the Iridium satellite phone, this simultaneous buildup of market and technological evolution did not occur. Many times, also for succeeding products, the more advanced versions are not necessarily superior in all aspects. In many cases a more advanced generation is superior at launch in one or more attributes but falls behind existing generations in other attributes. 1 At that time only customers who value the specific attributes, where the new generation has advantage, adopt it while others favor the older generations. When technology progress drives improvements of the new generations at a faster rate than old generations the market shifts gradually to the new generations. A decision to release a new product generation, which incorporates improved or new technologies, as a mass market product, is influenced by technology and market considerations. While technology factors determine the feasibility and the costs, market factors determine the worthiness. R&D activity and technology progress are maintained at some level even when the market is low, but they are dramatically accelerated when the market starts to grow. In the current paper, we present and demonstrate a methodology for forecasting sales and product (technology) evolution based on data that can be collected before new generations are launched. We consider product improvements based on R&D as the major drive for market extension. The mutual influence between market and technology developments had been raised before (c.f. Corresponding author. Tel.: ; fax: addresses: (Y. Orbach), (G.E. Fruchter). 1 For example smart phones have more features but shorter time between recharges /$ see front matter 2011 Elsevier Inc. All rights reserved. doi: /j.techfore

3 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) [9,32,40,65]) but the influencing factors were described qualitatively or analyzed retrospectively based on econometric methods and detailed sales data. Our method is based on a quantitative assessment, prior to product launch, of industry response to market developments which is tightly linked to technology and industry structure. We chose to deal with technology forecasting by using information about industry R&D procedures and methods, as in Dahan and Hauser [12], Grant[25], Söderholm and Sundqvist [61] and Nakajima [49], and by analyzing past technology progress rates as a function of R&D investments. Cooper [11] found that there is a high correlation between R&D resources and technology achievements. While R&D results, in terms of technology progress at the individual firm's level, are a function of creativity, innovation, efficiency and luck, at the macro level, as found by Yuan et al. [72],R&Dfinancial results, which are achieved by product improvements, are a function of investment. Cooper [10] notes that the development is more certain and less risky when based on incremental, rather than radical, innovation. Given an R&D budget, experienced managers can set specific technical goals and provide an achievable roadmap for technology progress. Gagnon and Haldar [21] find a good match between experienced managers' prior estimations and actually achieved goals. The preparation of the roadmaps can be based on either experience and intuition or more structured methods, such as those developed by Daim and Hernandez [14], Fenwick et al. [20] and Kockan et al. [36]. Sensing market trends and preferences, used by managers for directing R&D efforts, can be achieved prior to actual market developments by market surveys or, after changes happen, by analyzing sales data. 2. Study approach and main results We consider a dynamic model that is based on the interdependency between attribute improvements and cumulative adoption levels of each generation. The evolution of the cumulative adoption levels over time, as a result of the products' evolution, and vice versa, is based on both the customer purchase decision process (c.f. [38,63,70]) as well as the firm's decision process (c.f. [20,47]). To find the dependency of attributes' levels on adoption levels, we conducted an industry and technology analysis; to relate adoption choices to attributes' levels, we used data collected by a conjoint study. We demonstrate the applicability of the model on the hybrid cars market, where we rely on market preference data. We show that market preferences data collected can be used for a relatively long range forecasting. Industry response is evaluated based on the interests and behavior of the players who influence the industry. We forecast the technology progress of basic hybrids, which are in the market for some time, and advanced hybrids and electric cars, which are not yet on the market. We also forecast sales of both existing and future hybrids. We foresee that the transition from conventional cars, with internal combustion engines, to hybrid and electric cars will be quite quick. While hybrids with modest all-electric ranges will be adopted first, more advanced hybrids and later electric cars will finally dominate the market. We show that the major factor behind this rapid transition is the expected decline in the cost of batteries. The cost reduction will be achieved by developing more efficient batteries and better manufacturing methods, based on the relevant firms' R&D activity. The growing market, which serves as the main incentive for R&D activity, also provides the financial support for this activity. In what follows, we first review previous research, then set up our model. Next, we show how to implement the model to forecast pre-launch in the hybrid car case, and discuss the results. We then conclude with suggestions for future research. 3. Previous research Bass [3] original model assumes that the product and marketing mix are stable along the product's life cycle and that their influence is implicitly incorporated into the diffusion model constant parameters. Later studies, including those of Robinson and Lakhani [56], Feichtinger [19], Kalish [30], Jones and Ritz [29], Bass et al. [6], and Shih and Venkatesh [59] explicitly incorporate the influence of marketing mix changes, on customers' adoption decisions, in the diffusion model. Bass [3] and other extensions who base diffusion on the communication through the social network assume constant potential market; Mahajan and Peterson [44] present an alternative diffusion model that is based on the dynamics of market growth where the parameters are estimated by fitting to actual early sales data. The influence of technology progress, or product improvements, was previously addressed in diffusion-models for substitution of successive generations. Studies in this tradition include those of Norton and Bass [53,54], Mahajan et al. [42], Mahajan and Muller [43], Maier [45], and Bass and Bass [5]. In these models the potential market, marketing mix and product attributes for each generation are stable, as in Bass [3], but may differ between generation and there is also influence between generations. The coupling of technology progress and other marketing mix variables and their influence on the diffusion was explored by Danaher et al. [16]. Partial incorporation of specific product attributes in estimating parameters on diffusion-models appears in Srivastava et al. [64] and Bass et al. [4]. Landsman and Givon [38] explore the influence of customer choices on the diffusion of new services. Studies on technology evolution and the influence of product improvements on customer decision, when the evolutionary path is obtained from external sources, were addressed even earlier by Weerahandi and Dalal [70] and later by Schmidt and Druehl [58]. A number of studies, including those of Katz and Shapiro [33,34], Loch and Huberman [40],Thunetal.[67] and Goldenberg et al. [23], show that customers' utility depends on the installed base, due to externalities. In their models, the improvements in product benefits, due to externalities, are not obtained from external sources, but are calculated by the model. Narasimhan [50] refers to how forward looking customers influence monopoly price strategy and proposes a method for estimating the price evolution endogenously. Anderson and Mansi [1] found that marketing mix and customer satisfaction influence not only customers but also investors and creditors. The influence of the market on product improvements is mentioned by Loch and Huberman [40] and Bowman and Gatignon [7]. Cerquera [9] claims that R&D activities once again product improvement activities are not exogenous, but are influenced by the market. Empiric support about the influence of market developments on R&D decisions, based on financial

4 1212 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) reports and decision supporting systems, is brought by Orbach and Fruchter [55]. Kamien and Schwartz [31] found that some of R&D is self-financed. Stadler [65] explored firms' R&D behavior over an entire product life cycle. R&D activities rise in the early stages but decline when the market matures. Stadler [65] found that when the market grows, firms accelerate R&D activity by adding resources and shortening schedules; when the market declines, firms cut R&D costs and delay the release of new models. Daim et al. [15] explore and summarize several methods of Technology Assessment (TA) and its use in forecasting diffusion and technology. Technology learning curves, which represent the industry improvements, in the cost aspects, as a function of the number of units shipped is presented by Neij [51] and Grant [25]. The mutual influence between product improvement and market growth was raised by Song and Chintagunta [62], Söderholm and Sundqvist [61] and Sriram et al. [63]. They estimate the influence parameters using econometric methods on sales data. Song and Chintagunta [62] mention that some customers are forwardlooking and can predict the availability time of better products. Being able to predict the introduction of new products shortens customers' response time, since some of the purchase preparations and actual processes can start before introduction. Su et al. [66] argue that firms benefit from transferring knowledge about future products. This is one of the reasons why, although it exposes plans to competitors, firms make pre-announcements of products, which help customers to predict a product's launch time accurately. Similarly, as argued by Madsen and Ulhoi [41] and Agarwal and Bayus [2], forward-looking firms that predict market growth and allocate R&D resources earlier can gain an advantage in a competitive market. Prior studies have dealt with the challenge of estimating diffusion based on sales data at the early introduction or even prelaunch phase. Urban et al. [69] focus on pre-launch sales forecasting of a specific brand in a highly competitive saturated market environment, where technology progress plays a minor (if any) role. Garber et al. [22] used spatial detailed sales data. Bass et al. [4] used a conjoint study to estimate the potential market; however, the rest of the parameters were estimated by analogy. Marez and Verleye [46] and Schmidt and Druehl [58] used market survey, including attribute improvements over time, to evaluate purchase intentions, assuming that the product evolution path is provided by the firms. 4. Model formulation, assumptions and notation We consider a product category, of several generations, with attributes that keep improving with market growth, mainly by improvements in the product itself, based on technology progress, but also due to externalities. The technology progress influences all generations but not necessarily in the same intensity. We have three basic assumptions that are consistent with broad empirical observations. The first assumption relates to consumers' motivation to purchase a product, the second to firms' motivation to improve their products, and the third to the dynamics of the level of adoption Consumers' motivation to purchase a product Potential product users are influenced by the product's attribute levels (including accompanied services and price), and by the utility levels they enjoy from it (c.f. the conjoint analysis and product design literature). As product's attribute levels improve, more customers want to purchase it. When there are several generations of a product category available at the same time, the likelihood of choosing a certain product depends on its attributes as well as the attributes of its competing products. Let us assume that there are k generations and that each product has l attributes. Let A=A(t) be the matrix of k l where each row corresponds to a certain generation and each item in that row corresponds to a certain attribute level of that certain generation at time t. Let m be the vector of potential adoption levels (in percentages) of all generations, where each item corresponds to the potential adoption level of a specific generation. The potential market vector is a function of the attribute level matrix A. Thus, m = ma ð Þ: ð1þ Given that an acquisition is considered, m i (A) represents the likelihood that consumers will choose the product i with attribute levels A i, j ;j {1,,l}, from among the alternatives, which also include buying older generations or not buying at all. Since manufacturers usually check the potential market of a new generation before it is released, a launch time of generation i is determined by the time t when its potential market share m i (A(t)) provides an economic justification for the launch effort. Following the studies of Weerahandi and Dalal [70] and Loch and Huberman [40], we assume that a purchase of a certain product category is not considered continuously but once in a while. When a purchase is considered a customer has to make a choice between the alternatives. When the purchase consideration rate is α(t), or the time between purchase consideration is 1 αðþ, t the probability density of actually purchasing the product i, sayφ i, will be Φ i ða; tþ = αðþ m t i ðaþ: ð1aþ Note that α(t) may change periodically with time, due to seasonal effects, which can be filtered out, at times of stable economy, when using a period of one year. Also α(t) is product specific and depends on the average lifetime of the product. 2 For estimating α(t) one can use market statistics about the products. For radically new products, one will need to rely on market surveys, where customers 2 For example, in the case of home appliances (washing machines, dishwashers, refrigerators, etc.), assuming for example a six-year average life time, α=1/6 when the rate is given per year or α=1/72 when the rate is given per month. For seasonal products the rate is not the same every month but can change from high to low and low to high and still maintain year-to-year stability.

5 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) are asked to estimate the time between the availability of a desired product and actual purchase. Survey results need to be scaled by stated-to-actual purchase ratio, as done by Bass et al. [4]. In the next section, we give more details for the case of hybrid cars. To estimate the potential adoption levels as a function of products' attribute levels, m(a), one needs to use a conjoint study. There are many conjoint analysis methods and the specific method needs to be chosen according to the nature of the product category and the way the survey is run Firms' motivation to improve their products Following Loch and Huberman [40], Bowman and Gatignon [7], Cerquera [9] and Fenwick et al. [20], we assume that a growing market motivates firms and service providers to improve attributes' levels, and consider a launch of new product generations, in order to deploy market opportunities. The launch time of a new generation is determined by technologic feasibility, its forecasted market and its profitability along time. This leads us to the assumption that product attribute levels and new product generation introduction depend on the levels of adoption. In our research, we focus on product improvements based on R&D. For many products, R&D, which is financed as a percentage of sales (see [8]), provides a powerful means for continuous product improvements. Other sources for product benefit improvements, such as direct and indirect externalities (see [34]), are also included in our model, but their influence is usually significantly weaker. Estimating R&D results as a function of R&D efforts or budgets, at the industry level, requires analyzing the technology challenges, and industry structure and interrelations, as well as relations with other industries. Ruggles [57] found that a monopoly would improve the product only if elasticity justifies it, or when it tries to drive upgrades, but at the presence of competition firms will improve the product also at low elasticity. Relations with suppliers, regulations and industry standards influence the attribute response as well. Let f=f(t) be the vector of cumulative adoption levels (in percentages), at time t then, we assume that the products' attributes improve along with market growth, due to R&D investments financed by sales and motivated by market growing attractiveness, thus, At ðþ= Aft ð ðþþ: ð2þ The probability density of actually purchasing product i, Φ i, when the matrix of attribute levels of the product is A, also becomes a function of f(t). More exactly, we have, Φ i ðaft ð ðþþ; tþ = αðþ m t i ðaft ð ðþþþ: ð2aþ To assess the industry response, or the development of products' attributes as a function of market developments, A( f(t)), there is a need to perform an industry analysis. In Fig. 1, we present general guidelines regarding how to perform this analysis. The actual formulation of A( f(t)) is product- and industry-specific since every technology has its unique nature and each industry is characterized by certain R&D investment policies and procedures. These policies usually reflect equilibrium of cost structure, competition and risks. While Fenwick et al. [20] refer to a more challenging task of outlining roadmaps at the individual firm level, and face difficulties described by Lamb et al. [37], we choose to perform the analysis on the industry level. Performing the analysis on the industry level averages some of the factors and reduces variance and uncertainties. While previous research studies refer to each monolithic firm, we refer to the different players along the value chain and their inter-relations. We detail how the general guidelines are actually formulated, for the hybrid car case, in the next section. Following Fig. 1, in Stage 1, we specify which attributes are desired by the market and which are the technologies that enable to implement them. In Stage 2, we assess the influence of the market on the product evolution. As mentioned previously, attractive markets motivate firms to improve products in order to leverage their potential. Klepper [35] found that as market growth accelerates, in addition to motivating the firms that are already involved in the market to invest more, it also attracts new entrants. The growing market also finances R&D activity, as a percentage of sales, which leads to further product improvements. Competing firms, who do not use the new technology yet, need to allocate comparable resources, raised from external investors, to acquire the new technology in order to protect their market position. In addition, product benefits improve due to indirect externalities (and some also improve due to direct externalities) but the major source for product benefit improvements is R&D. Estimating industry response relies on industry and market analysis. As noted by Levary and Han [39], the features required by the market, and also used in the conjoint study, need to be investigated by industry and technology experts to examine their implications on a product that incorporates a new technology. For example, the sound or voice quality of a mobile phone or the picture quality of a digital camera is linked to the embedded processor capabilities. One needs to estimate the processor performance required for supporting the desired quality levels and check the related implications such as cost, power consumption, and compactness. After the technological implications of each feature have been identified, we need to check the supply chain of each component. While some components are developed and manufactured by the end product manufacturers, other components are supplied by manufacturers from other industries. For example application software for mobile phones is developed by the handheld terminal manufacturers, while the modem software is usually provided by the DSP (Digital Signal Processor) component suppliers. We then need to evaluate how much R&D resources are expected to be directed towards the further development of each feature. Regarding technologies that are developed by suppliers, the influence of the specific product industry on its suppliers must be evaluated as well. After estimating the amount of resources expected to be directed towards the further development of each feature, we need to assess the predicted outcome of this effort. This assessment is based on the nature of the R&D activity and the past performance of

6 1214 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) Stage 1. Specify attributes and technologies Examine how the new technology can provide features that may be desired by the market. This stage involves technology and market experts. Reduce the number of attributes which are the most important to potential customers. This stage involves a focus group and guided by technology and market experts. These attributes will be used also for the conjoint study. Identify what is the exact technology and industry that support each feature. Distinguish between technologies that are usually developed by the product's industry and technologies that are developed by suppliers. Stage 2. Determine market influence on product evolution Market influence on R&D resources allocation For technologies that are developed in-house estimate the R&D resources allocation policies. For technologies that are developed by suppliers check also the relations between the industries (monopoly or competition at each side). For each technology evaluate the expected R&D results given a certain budget based on concept product, technology general progress, past performance and roadmaps. Technologies progress influence on product's attributes. R&D resources allocation influence on technologies progress Determine the impact of the improvement of each technology on final products attributes levels. Fig. 1. The industry analysis flow to determine A(f). R&D teams. Production effectiveness, or cost per unit, usually follows learning curves presented by Grant [25]. For electronic devices the technology progress path, when backed with sufficient resources, follows Moore's Law (see [48]) of exponential rate of quality improvements. Extending the diversity of a product line is usually linear to the marginal cost of an additional product version and can be quite accurately estimated given the resources that would be allocated for it. Regarding mid-term development, there are many time specific standards, prototypes and concept models (see [49]) that outline the development path. For mid-term development managers are expected to plan and provide achievable roadmaps, thereby turning these prototypes of concepts into marketable products. Within the context of allocated resources, we can use these standards to assess how fast these roadmaps will be realized in market-available products. Daim et al. [13] note that while patent growth can be a good indicator for emerging technologies, at the later stages of applications and launch to the market, using growth rate and system dynamics may provide a better basis for a forecast. Growth rates, as in Moore [48], can be used when the industry and market demonstrate a regular expansion pattern or for the short term product life cycle. For short term product life cycles, the time between R&D and launching an improved version is long relative to market development, so that the industry cannot respond to market developments quickly enough. For the long term lifecycle product, like the one we have investigated, the time between R&D and launching an improved version is relatively short and the market development rate, which may experience drastic changes, influences product improvements in a pattern described by technology learning curves. The feedback of the influence of product improvements on market expansion creates the system dynamics Dynamics of the level of adoption Let f i ðþ= t df iðþ t be the growth rate in the adoption fraction of generation i at time t; we assume that it will be equal to the dt probability density that the remaining fraction of potential adopters will actually purchase the product with attribute levels A(t), at time t. Thus, f i ðþ= t αðþ m t i ðaft ð ðþþþ ð1 f i ðþ t Þ; i; i f1; ; kg;f i ð0þ =0: ð3þ The model presented here integrates the customer's decision process (Eq. (1a)) and the firms' and service providers' response (Eq. (2), see Fig. 2). Fig. 2 also incorporates the dynamics as a result of the influence of product attribute improvement on the market,

7 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) Remaining potential adopters Customer purchase decision and market flow Firms and service providers flow Consider a purchase? 1-f Improved A A(f) yes α Purchase new technology with attributes A? yes m(a) New adopters R&D and other causes, like direct and indirect externalities, improve product benefits Current adopters + Earlier adopters Fig. 2. Customer purchase decision and firms' and service providers' flow. and vice versa. Within the timeframe of one time period, only a portion α of the remaining fraction of potential market, 1 f i, considers a purchase. From this portion, only m i (A(t)), depending on attributes, will actually purchase generation i.theflow of sales diffusion as a result of customer decisions is represented in Fig. 2 by a solid line, as in the study of Weerahandi and Dalal [70]. Purchasing a product creates a motivation for product improvement and cash flow on the supplier's side. Some of the revenues are allocated to R&D, which improves the product and makes it a more attractive alternative when potential adopters consider a purchase. Other factors, like direct and indirect externalities, influence actual benefits in the same way. The influence of the sales on attribute improvements is represented in Fig. 2 by a broken line. 5. Implementation of the model to pre-launch forecast: the hybrid car case In this section, we implement the model described in the previous section for the case of hybrid cars. We do this by using secondary data sources reported in Graham [24]. The report is based, as recommended by Levary and Han [39], on the technology and market analysis of auto industry experts. Following the steps of Stage 1, described in Fig. 1, Graham [24] provides us (a) market preferences data based on a conjoint study and (b) technical data such as structure, performance and components of hybrid and electric power train technology which enabled us to calculate overall performance and cost developments and the impact of battery cost reduction on hybrids' overall cost Description of the hybrid car configurations Following Graham [24], we refer to a product line of four typical hybrid cars. These cars represent different levels of reliance on the innovative electric power train. While early hybrids, available in 2007, still rely mainly on fuel and are assisted by electricity, more advanced hybrids, which are not in the market yet, will rely more, and finally almost entirely, or entirely on electricity. We analyzed all of the hybrid types together, rather than individually as separate products, since the different hybrid types share both the market, where customers decide which type of hybrid to choose, according to attributes of all hybrids, and the battery and electric power train technology, which is incorporated into all hybrids. Still, since these technologies are more significant for the more advanced hybrids, the technology progress influences the advanced hybrid types more than it influences the basic hybrid types. Following Graham [24], we refer to a product line of four hybrid configurations according to their all-electric range. Hybrids of HEV0 type use their electric motor only for acceleration assist. HEV20 can go up to 20 miles on electricity only at city driving speeds. HEV60 can go on electricity up to 60 miles also at highway speeds. BEV200 does not have a fuel engine and can go up to 200 miles before it needs to be recharged. The more a vehicle can go on electricity, the more it is environment friendly and fuel saving. For simplicity, we will identify the corresponding types HEV0, HEV20, HEV60, and BEV200, with the indices i=1,2,3, and 4, respectively Calculation of α(t) The purchase consideration frequency, α(t), is based on car market statistics. The introduction of attractive new hybrids will probably influence some of the customers who consider buying a car to choose a hybrid. The customers who do not consider

8 1216 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) purchasing a car at a certain time will continue to use their currently-owned car for that time period and will not be influenced by the new cars that are introduced in the market. The annual car market in the US is around 17 million with a cumulative car market, of about 241 million (See USA Statistics in Brief-Energy, Transportation, and Communications 2006, based on U.S. Census Bureau, at thus, when referring to a period of one year, α = 17 = 241. This is a simplification of the car market structure that includes new car buyers and trade-in buyers. Still we refer to trade in as a role exchange, where the used car adoption time remains the same but the ownership is changed. Although the market displays seasonal behavior (stronger in summer and weaker in winter), the differences on a year-to-year basis are relatively minor when the economy is stable Description of the attributes set Following Graham [24], we assume that hybrid and electric cars will look and be driven like conventional cars. The glider, chassis, air-bag, air conditioning, navigation and entertainment systems will remain more or less the same as for conventional cars. The changes in the steering system will be minor. The major change will be in the car's power-train system. Graham's [24] conjoint study refers to several attributes such as vehicle price, battery cost, fuel price, maintenance saving, recharging facility cost, fuel tank size and environmental benefits. Some other issues, not included in Graham's [24] conjoint study, are motor efficiency and battery weight, recharge time, lifetime and sensitivity to ambient conditions. We assume that improving the 96% efficiency, for existing modern electric motors, will not have a major effect on electric cars' attractiveness. The same is true for the power electronics inverting system, gear and recharging systems. We assume that the progress that will have a major impact on hybrid and electric car adoption is battery technology, which determines the range and price trade-off. The main issue that will attract battery developers, as indicated by Duong [18], is reducing battery cost. Practically, most of the battery-related issues, excluding cost, have already (see [28]) reached a level that makes the hybrid and electric vehicle quite appealing. There are many opportunities, which are worth a lot of money and might influence brand preferences, to improve these attributes but they are not critical for the decision of whether to adopt a hybrid or electric car at the industry level. The same is true for the electric motor and its auxiliaries. These changes may affect the driving experience, which is expected to improve due to progress in these technologies, but are not going to affect the most important attributes: performance, range and price. The power and responsiveness requirements of an electric vehicle, analyzed by Shukla [60], can be delivered by today's electric motors and auxiliaries. It leaves car price premium and fuel price, which were the most influencing factor and far above the other factors, as the only relevant attributes. For fuel price, we took a conservative assumption of stable prices in order to simplify calculations. A rise of fuel price will further accelerate the adoption of hybrids. This leaves price premium, which in any case was the most influencing factor, as a single parameter. The conjoint study questionnaire of Graham [24] asked the respondents to assume that there is a hybrid version for every car model or 100% availability. According to this assumption, Graham [24] determines the potential market of various hybrid configurations depending on attributes such as price premium, fuel price, and battery replacement cost. Weber [71] claims that car customers tend to stick to their favored style. This means that a hybrid car will be considered by a customer only if there is a hybrid version similar to the conventional car s/he favors. We assume that a product line of 100 car models will cover all the market segments; hence, we measure the availability by counting the number of available hybrid models and dividing it by 100. This assumption is rather conservative, since car manufacturers are likely to first develop hybrid versions for popular models, or models where hybrid technology offers more benefits. According to the first step of Stage 2 in Fig. 1, at both in-house and external suppliers, we need to analyze price premium and availability separately. While price premium is tightly coupled to battery industry developments, availability is related to auto-industry activities. For each hybrid car type i, i=1,2,3,4, we refer to a set of two attributes (A 1i,A 2i ), where A 1i denotes the price premium of i relative to a conventional car, in short price premium, and A 2i denotes the availability of hybrid models for i, in short availability Calculation of m(a) For this calculation we use Graham's [24] conjoint study with some modifications. We extended the price premium range by adding a minimum threshold, when it is less than annual fuel saving cost, at which everybody will prefer a hybrid over a conventional car. Another addition is the BEV200 vehicle, which we assume will play an important role in the hybrid market. Its practical environmental benefits are similar to those of the HEV60, so we assume that the market refers to them similarly. The small difference is due to a minor economic advantage of the BEV200 in fuel savings. The clean appeal of the BEV200 as a car with no tailpipe is not included. On the other hand, we did not refer to the advantage of the HEV60 in range, since a 200- mile range with 10 min recharge time, as in the Maya-100 (An electric SUV marketed by Electrovaya), is sufficient for most users. From Table 1 we can infer that the launch of HEV20 cars to the mass market is likely to occur only sometime after its premium price will approximately reach a level below $10,000. In the same way we can infer that HEV60 and BEV200 release will wait until their premium price will reach the levels of $20,000 and $30,000 correspondingly. Note that technologically it is easier to achieve a $10,000 premium price for HEV20 than to reach below $20,000 premium price for HEV60. We used a piece-wise linear function for interpolation. In Table 1, we summarize the potential market dependency on the first attribute, under the condition of 100% availability. We denote this dependency by m i A 1;i, i=1,2,3,4. However, even if a customer is willing to pay the price premium for the hybrid of her/his choice, it is necessary that a hybrid of this style will actually be

9 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) Table 1 Potential market as a demand function of price premium (A 1,i ). Hybrid type i Potential market m i A i;j 8 1 A 1;1 bd290 >< HEV : A 1;1 290 D290 b A 1;1 bd2029 >: 0:234 0: A 1; D2029 b A 1;1 8 1 A 1;2 bd478 >< HEV : A 1;2 478 D478 b A 1;2 bd2991 >: 0:258 0: A 1; D2991 b A 1;2 8 1 A 1;3 bd622 >< HEV : A 1;3 622 D622 b A 1;3 bd5035 >: 0:253 0: A 1; D5035 b A 1;3 BEV A 1;4 bd887 >< 1 0: A 1;4 887 D887 b A 1;4 bd5982 >: 0:264 0: A 1; D5982 b A 1;4 available [71]. In order to find the actual potential market that also depends on the availability attribute, we multiply the numbers in Table 1 by the availability attribute and obtain: m i A 1;i ; A 2;i = m i A 1;i A 2;i ; i=1; 2; 3; 4: ð4þ When there are several hybrids with different prices that are available simultaneously the market will be divided according to the environmental awareness segments. Those who are willing to pay more for clean vehicles will prefer the cleaner vehicles with the longer all-electric range. However, they may compromise on a less environment-friendly car if a more advance hybrid version of their favorite car has not been launched at that time. Others who are willing to pay a lower premium will favor the lower cost and less environment-friendly vehicles. For example, the segment that is willing to buy an HEV60, at a higher price than an HEV20, would compromise on an HEV20 if the HEV60 is not available. Those who prefer HEV20, due to its lower price, will not compromise on the expensive HEV60 if HEV20 is not available. This means that when we calculate the potential market share of each hybrid type, given the prices of each hybrid, we calculate the market share of the BEV200 first using the BEV200's price. Then we calculate the market share of both the HEV60 and the BEV200 using the HEV60's price. From this, we then subtract the share of the BEV200. In the same way, we calculate the market shares of both the HEV20 and the HEV0. Thus, m(a) becomes: ma ð Þ = m 1 A 1;1 ; A 2;1 ; A 1;2 ; A 2;2 ; A 1;3 ; A 2;3 ; A 1;4 ; A 2;4 m 2 A 1;2 ; A 2;2 ; A 1;3 ; A 2;3 ; A 1;4 ; A 2;4 = B m 3 A 1;3 ; A 2;3 ; A 1;4 ; 2;4 C B m 4 A 1;4 ; A 2;4 where m i, i=1,2,3,4, is as in formula (4) Cumulative adoption levels (in percentages) m 1 A 1;1 ; A 2;1 m 2 A 1;2 ; A 2;2 m 2 A 1;2 ; A 2;2 m 3 A 1;3 ; A 2;3 m 3 A 1;3 ; A 2;3Þ m 4 A 1;4 ; A 2;4 m 4 A 1;4 ; A 2;4 Let f=(f 1,f 2,f 3,f 4 ) be the vector representing the cumulative adoption levels (in percentages) of the four car generations (HEV0, HEV20, HEV60, and BEV200), which are assumed to be available simultaneously at least part of the time. The periodic sales vector at time t, n(t)=(n 1 (t),n 2 (t),n 3 (t),n 4 (t)) equals f ðþmultiplied t by the overall market size M. Thus, n i ðþ= t M f i ðþ= t M α m i ðþ1 f t ð i ðþ t Þ; i=1; 2; 3; 4: ð6þ According to USA Statistics in Brief-Energy, Transportation, and Communications 2005, there are about 241 million cars on the US roads, so for market size we set M=241 (in millions) Calculation of A(f) To find the industry's response to market developments, according to the last two steps at Stage 2 in Fig. 1, we need to perform a technology progress assessment for each factor. To calculate the dependency of hybrids' attributes, Price Premium and Availability, on the levels of adoption of each hybrid type, we analyze the technology and industry structure and processes. Car 1 C A ð5þ

10 1218 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) manufacturers direct a portion of the revenue to improving their products and extending the product line. Hybrid sales revenues are directed partially to component manufacturers who supply components to car manufacturers. The growing demand is expected to influence in three ways. (a) It will encourage more firms to offer components for cars and increase competition. (b) The growing demand increases the importance of components' cost reduction for car manufacturers. (c) The bargaining power of car manufacturers increases due to a demand for large quantities. The growing component sales will encourage improvements and cost reductions, and the growing revenues will finance the improvements' activities. Since the market is competitive the reduced cost is reflected in the hybrids' final prices. As explained above, the attribute Price Premium and Availability have the strongest impact on the decision whether to adopt a hybrid vehicle. The influence of other attributes of hybrids is much weaker. In the car market, which is very competitive, vehicles' price is tightly related to manufacturing costs. The costs of assembly and the components, excluding the battery, of the different hybrid types are comparable to assembly and component costs of ICE cars, see Graham [24] and Table A1 in the Appendix, and are expected to remain in the same scale. Battery cost is expected to decline by an order of magnitude, and will impact hybrids' Price Premium significantly due to its dominant share in hybrid vehicles Bill of Materials (BOM). For simplification we relate Price Premium to battery price and ignore the relatively minor impact of other cost factors changes. Following Neij [52], Grant [25] and Söderholm and Sundqvist [61] we expect battery cost to decline, according to technology learning curve, when production volumes increase and improvements driven by R&D are implemented. For the battery case, evolution, or the technology learning curve, relies more on research than on improving manufacturing methods. It causes the battery progress rate, or learning curve, to relate to R&D investments, which are proportional to revenues, rather than to production volumes in units. Battery revenues stem from hybrids' sales and are related to the type of hybrid adopted, according to its incorporated battery module size. Availability progress, or product line extension, has a different characteristic. When a hybrid drive train is already developed and implemented in other cars, incorporating it in other cars involves more design issues than basic research. We take a conservative assumption that the learning curve for availability is almost flat and that the number of models developed is proportional to design resources invested which are proportional to past sales. Note that we do not include the initial investment required for developing the first car model in a certain technology and building its assembly line. Constructing an assembly line is very expensive but it can serve for manufacturing many car models. The funding for building whole new manufacturing facilities is usually raised from investors, based on future sales, and not financed directly by short term actual sales. The technology and industry structure analysis is very technical and detailed in the Appendix. A summary of the functional dependency between hybrid cars' attributes and sales development is presented in Table Forecasting hybrid sales Forecasting sales is based on the general formula (3), when using the hybrid specific relationships developed in (4) (6). The price premium for the years is taken from actual market data and is higher than the expected price as a function of battery cost, due to lack of competition and high demand pressures. The price premium will decline gradually, due to increased competition, and we estimate that by 2010 competition will drive the hybrids' premium down to reflect costs with the ordinary profit markup. The number of hybrid models available until 2010 is based on manufacturers' announcements. For the years price premium and availability are calculated, using the model, based on Table 2. Based on premium and availability in each year, from Table 3, we can calculate the potential market shares m i A 1;i in that year under 100% availability and an exclusive hybrid type, based on the functions of Table 1, and scale them by availability using Eq. (4) for calculating m i A 1;i ; A 2;i. Based on m i A 1;i ; A 2;i, which represents the hybrid market share of an exclusive hybrid type, we can Table 2 Attributes Dependencies on Market Developments. HEV0 HEV20 HEV60 BEV Overall i Battery size (kw h) Level of adoption f i (t) f 1 (t) f 2 (t) f 3 (t) f 4 (t) f i ðþ t Market growth f i ðþ= t df iðþ t dt US market size M (cumulative) Sales n i (t)=m f i (t) f 1 ðþ t M f 1 ðþ t f 2 ðþ t M f 2 ðþ t f 3 ðþ t M f 3 ðþ t f 4 ðþ t M f 4 ðþ t i =1 4 f i ðþ t i =1 241 millions 4 M f i ðþ t Battery revenues B R (t) 0.5 n 1 (t) 4.5 n 1 (t) 13.5 n 1 (t) 40 n 1 (t) B R ðþ= t B R;i ðþ t i =1s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi! t 2 Battery cost B c (t), B c (0)=$2200 B c ð0þ exp kb R ðτþ k =0: Price premium ($)A 1, i 0.5 B R (t) B R (t) B R (t) B R (t) 2896 Assumptions R&D allocation=6%; R&D cost per version=$90 millions; Short/long term ratio=0.75/0.25; average car price=$20,000 Availability increase (# of car models) ΔA 2,i =A 2,i (t) A 2,i (t 1) 2410f 1 ðt 1Þ + 803f 1 ðt 2Þ 2410f 2 ðt 1Þ + 803f 2 ðt 2Þ 2410f 3 ðt 1Þ + 803f 3 ðt 2Þ 2410f 4 ðt 1Þ + 803f 4 ðt 2Þ i =1 4 τ =0

11 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) Table 3 Hybrid/electric vehicles attributes development forecast. Year Battery cost ($/kw h) B C (t) Premium ($)A 1, i Availability (# of cars) A 2, i HEV0 HEV20 HEV60 BEV HEV0 HEV20 HEV60 BEV calculate m(a), which represents the potential market share of each hybrid when several hybrid types are available simultaneously using Eq. (5). The estimated annual sales, n i (t), calculated using Eq. (6) are presented in Table 4. Table 4 also includes adoption fraction f, calculated by cumulative sales and divided by M= The calculation of batteries' annual revenues in Table 4 battery costs in Table 3 are based on Table Discussion of the results Table 4 contains the evolutionary path of the attributes resulting from our model. In Table 3, we marked the initial data, based on manufacturers' announced plans, with italic bold. For later years the attribute levels are calculated by the model. The price premium of the HEV0 in the years 2008 and 2009 is higher than expected by considering battery costs in a competitive market, since there is no real competition. As more car manufacturers join the hybrid trend, prices will adjust and reflect batteries' declining costs. We put an upper limit of an annual 30% on battery cost cuts. This means that the learning curve, or the cost reduction process, cannot be faster than 30% annually, even with unlimited resources. This assumed limit makes the model less sensitive to the specific concave function selected to describe the relation between battery revenues and future costs. In Table 4, we marked the initial actual sales data, from market statistics sources, with italic bold. The initial sales data of the BEV is based on pre-orders (in the years 2008 and 2009), and on market surveys for specific niches until Our model predicts that the transition of transportation vehicles to electric propulsion will be quite quick. Within little more than a decade, most of the new cars will be advanced hybrids, which consume very little fossil fuel, or BEV type vehicles, which consume no fossil fuel whatsoever. The major factor that will cause this rapid transition is the decline in battery cost based on R&D. The growing market will not only finance this R&D activity, but will also serve as the main motivation for it. The foreseen plug-in hybrid and electric car market also promotes the deployment of the recharging infrastructure. The R&D activities include incremental improvements of the existing Lithium-ion batteries, which will drive the transition of the automotive market to Table 4 Hybrid/electric vehicles market development forecast. Year Sales, n i (t), i=1,2,3,4 Adoption fraction f Batteries' revenues HEV0 HEV20 HEV60 BEV HEV0 HEV20 HEV60 BEV ($M) , , , , , , ,841, , , ,696, , , ,717,724 1,128, ,440 29, , ,333,234 2,381, , , , ,645,817 4,645, , , , ,546,508 7,908,784 2,123, , , ,099,303 8,500,543 3,675, , , ,268,338 5,139,665 6,956,672 2,032, , , ,056,993 4,187, ,303

12 1220 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) electric propulsion, and some new battery technologies, such as Zinc-air or Lithium-air, which may dominate in the long range. Some business ideas, such as leasing batteries instead of selling them, may be attractive when battery costs are still high. When battery prices decline the equilibrium between leasing and selling the batteries will probably change. We performed a sensitivity analysis to check the impact of variations of the parameter values, or some modification of the assumptions related to the car industry and market, on the model's forecasts. For the demand side we take the variation ranges, which are quite modest, and confidence levels from Graham [24]. For the supply side we take much higher variations. The forecast assumed parameter values and results serve as a reference. We calculate the RMSE of the differences between the forecast with a modified parameter and the reference forecast as a measure for the sensitivity of the forecast to that parameter. The variations and sensitivities are summarized in Table 5. From Table 5 we see that although the model is tightly linked to battery technology progress, even a significant change in industry response to market developments causes a relatively small change (in percentage) in the forecasted sales. Although a slower battery cost reduction rate will stretch out the technology transition period, and cause it to take a few more years, the trend will nevertheless continue. Other factors, such as limited manufacturing capacity, may have a similar but weaker effect. The policies of major players may also influence the market in the short term. Toyota, which is the leader in hybrid production and which has invested in its hybrid technology development program much more than any other company, is very hesitant about adopting Lithium batteries. This is due not only to the conservative nature of the industry, but also to the fact that the transition to electric power may neutralize Toyota's advantage which is based on mechanical engineering excellence. Toyota is ready to launch Lithium based vehicles and will do so either as a market leader or shortly after its competitors will launch such vehicles. New entrants, like Tesla Motors, may be important drivers of vehicle Lithium batteries. Although their sales volumes are, at present, insignificant, their vehicles will provide data about the technology maturity, reliability and safety. Traditional car manufacturers like Mitsubishi, who are not major players in the US conventional car market but have strong electrical engineering backgrounds, have already announced their plans to release electric and advanced hybrid vehicles. Once strong players like Mitsubishi join the electric vehicle trend, other manufacturers will have to follow. Industry structure, which is currently based on several manufacturers who control all the major car components, may change. Instead of a structure where the engine, transmission, and glider are manufactured by the same firm, we may see a modular design where a firm manufactures the glider but purchases the motor and batteries from a third party. Standards, driven either by industry or by regulation, may encourage modularity, which usually tends to push prices downward. The analysis is based on US market although the car market is global embeds the assumption the US will remain a leading market. The validity of the forecast will sustain, even if this assumption is released, if the US market preferences are representative of other major markets. A graphic description of market dynamics' forecasting is shown in Fig. 3. The chart shows the transition of the market to hybrid and electric and also the substitution between generations. The implementation of our model on the hybrid vehicle market outlines how pre-launch forecasting of future generations can be conducted based on market, technology, and industry data collected when only the first generation is introduced or even prelaunch. There is some debate about the validity of market preference data after some time has passed. The EPRI's (Electric Power Research Institute) thorough market survey, published by Graham [24] long before advanced hybrids were supposed to reach the market, represents a belief that market preference data is valid for a long time. Indeed, Toyota offers (in 2007) its hybrid Camry for $26,200, while a conventional Camry's price is $18,470. Taking into account a ratio of 0.5 between stated-to-actual purchases, as in Bass et al. [4], the actual sales of 7% of the hybrid Camry are aligned with the market research results. Bass et al. [4] also prove that conjoint study results remain valid for several years. For measuring market preferences for really new products one needs to follow Hoeffler [27] methods as Graham [24] did. In this section, we have demonstrated how our model can be implemented for forecasting future hybrid generations prior to their introduction. To compare our sales and attribute evolution forecast with actual sales and actual attribute evolution, we will need to wait several more years. Table 5 Sensitivity to Parameters Variations (RMSE). Category Parameter and variation HEV0 HEV20 HEV60 BEV200 Demand as a function of attributes HEV0 demand is lower by 3% 288,519 10, HEV0 demand is larger by 3% 286,681 10, HEV20 demand is lower by 3% 237, , HEV20 demand is larger by 3% 244, , HEV60 demand is lower by 3% 19, , ,329 41,988 HEV60 demand is larger by 3% 19, , ,934 42,706 Battery cost reduction rate limit 25% (rather than 30%) 699, , , ,659 20% (rather than 30%) ,056,931 15% (rather than 30%) 2,223,288 2,246,395 2,774,842 1,138,286 Car development cost 80 million (rather than 90 million) 487, , , , million (rather than 90 million) 369, , , ,068 Simplifying assumption Calculate revenues by accurate car price rather than $20,000 per car 361, , , ,313

13 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) Annual US sales HEV0 HEV20 BEV200 HEV Year Fig. 3. Hybrid sales forecasting. 6. Conclusions and future directions Lifecycles in general and time between generations in particular are becoming shorter. Products like DVDs, digital cameras, and flat TV displays have displaced well established technologies, i.e., VCR, film cameras and CRT, in less than a decade. These products, as well as others like mobile phones and laptops, continue to improve, not only by generation substitution, but also within each generation. By referring to continuous product improvements, as well as to releases of product generations, forecasting market dynamics under conditions of rapid technology change is still a challenge. Thus, our study addresses a timely and important managerial question: How should a firm forecast market acceptance and product evolution of future versions, pre-launch? Our model does not only predict market growth and generation substitution; it also predicts technology evolution. Attribute improvements, based on technology progress, are driven and directed by market forces. Hence, we can say that market forces shape technology evolution. Future research can extend our analysis in three major directions: First, future studies can collect data before the first product generation is launched. Emerging technologies in many areas, such as HDTV, photovoltaic cells, and home broadband wireless links provide many such opportunities. Second, future studies can extend the model to forecast diffusion at the brand level. For this purpose, we will need to use multiple methods, described by Daim and Hernandez [14], Tran and Daim [68], and Meade and Presley [47]. Such a forecast will require not only including brand preferences in the conjoint study, but also referring to industry responses at the firm level. Third, future studies can extend the model to include the influence of repurchase. Appendix A. Industry and technology analysis Of the attributes which influence adoption decisions involving hybrid cars the most significant are price premium and availability. These attributes are expected to change significantly with time, based on technology progress which is driven by market developments. The functional dependency of these attributes on market developments is product-specific and determined by the product and technology nature as well as industry structure. The dependency of hybrids' price premium on market development is more complex since it involves a supply chain of two industries. The progress of the core technology that determines price premium is based on basic research, and thus has some uncertainty. The dependency of availability on market development is straightforward and based on design activities, which are simpler to model, and is quite certain and performed within the automotive industry. We describe, step by step, how we assess the dependency of each attribute on market developments. A.1. Price premium and adoption level Adoption influences hybrids' price premium mainly through motivating the industry to invest in battery cost reduction. We divide the attribute price premium of each hybrid into two components: One, the cost of all components excluding the battery which is considered to be constant. Second, the component influenced by the battery cost, say B c (t), which changes over time. Although in practice the price of other components, like the motor and inverter, will also decline due to improvements and mass production, its impact on overall car price will be relatively small. The battery cost is expected to decline significantly with time (and with market growth) and to have a major impact on the car's price premium. The improvements of batteries in general, and specifically the decline in battery cost, are based on R&D and motivated by battery sales and revenues, say B R (t). The revenues B R (t) stem from hybrid sales, n i (t), i=1,2,3,4, since every hybrid incorporates a battery module component. Thus, we need to proceed in two steps (see Fig. A1):

14 1222 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) Step 1 To show how hybrids' price premium is influenced by battery costs (Step 1.1) and how hybrids' sales influence battery revenues (Step 1.2). Step 2 To show how battery cost is influenced by battery revenues through R&D. Since the R&D process takes time, the results of R&D at time t are reflected after a delay T, at time t+t. A.1.1. Price premium and battery cost To see how A 1, i (t), i=1,2,3,4, depends on battery cost B c (t), we calculate the price premium of each hybrid type according to a breakdown of car components and components' cost, detailed in Graham's report. The major components are the fuel engine, including all auxiliaries such as the cooling system, lubrication and fuel system, transmission (which includes the gear and the clutch), the electric motor (which also includes the inverter and the control system), and the glider (which includes the body and all other safety and comfort systems). Following Graham's data of car industry nature, we assume a 1.75 markup a factor that multiplies the overall component cost for price estimation. All the components, excluding the battery, are assumed to have a stable cost so each car's premium is a function of the battery cost. The components' costs from Graham [24] are detailed in Table A1, where the varying battery module cost is separated from the components with constant cost. The cost of the battery module is a multiplication of the battery cost, or the cost of energy unit storage, by the battery module size or energy capacity of the module. The overall cost, with battery included, is presented at the bottom row of Table A2. A battery module required for a car that has a long all-electric range needs to store more energy than a hybrid that has a short all-electric range. Different car configurations use different battery sizes according to the car's size and all-electric range. The typical battery size, for a mid-size car, is calculated, based on Shukla [60] and on actual concept electric cars available today, to be 200 Whr per mile. The battery size of each hybrid type is detailed in Table A2. The battery cost changes over time, due to R&D, are financed by battery revenues. Table A2 also details the current cost of the battery module (2007) (which clarifies why in 2007 there are only hybrids of the HEV0 type), the varying module cost as a function of the battery cost per energy unit ($/kw h), and the varying hybrid premium. Based on the last row of Table A2, we can calculate the hybrid premium A 1, i as a function of battery cost B c (t), thus: A 1;i ðþ= t 8 0:5B c ðþ+ t 303 i =1 >< 4:5B c ðþ+ t 341 i =2 : 13:5B c ðþ 684 t i =3 >: 40B c ðþ 2896 t i =4 ða1þ A.1.2. Battery revenues as a function of hybrid sales To complete Step 1, we must assess the relation between hybrids' market growth and battery revenues. Battery revenues B R (t) are connected not only to the overall number of hybrids sold, but also (as shown in Table A2) to the types sold. Battery revenues that stem from each hybrid type are a multiplication of the sales of that hybrid type by the battery size incorporated in each hybrid, and by battery cost. The overall battery revenue is the sum of the revenues that stem from each hybrid type's sales. Based on the first row of Table A2, we can link battery revenues B R (t) to hybrid sales, which is the sum of the multiplication of hybrid sales of each type by the battery size incorporated in it, and by battery cost, according to the following relationship: B R ðþ= t B c ðþ t ð0:5n 1 ðþ+4:5n t 2 ðþ+13:5n t 3 ðþ+40n t 4 ðþ t Þ: ða2þ Hybrid Sales ni ( t) = M Δf i ( t) HEV0 sales HEV20 sales HEV60 sales BEV200 sales Step1.2 Step2 Batteries revenues B R (t) R&D investments Batteries R&D Process (takes time T) Batteries cost B c ( t + T ) Step1.1 HEV0 price premium HEV20 price premium HEV60 price premium A i ( t + Hybrid Price Premium ) 1, T BEV200 price premium Fig. A1. From hybrid sales to hybrid price premium.

15 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) Table A1 Car price breakdown with battery excluded (The components with stable costs). Car configuration Conventional HEV0 HEV20 HEV60 BEV200 Comments Gas Engine The gas engine is smaller and simpler in hybrids, since some of the power is provided by the electric motor. +Transmission The transmission in the parallel hybrids (HEV0, HEV20) is more complex, since it combines power from two sources. +Electric Motor In more advance hybrids, the electric motor is larger, since it propels the car at all states. +Glider Basically the same for all cars. We added $200 for the hybrids' regenerative braking system. =Total cost 10,822 11,021 11,043 10, Simply a sum of the components Estimated baseline price 18,984 19,287 19,325 18,300 16,088 Price=total cost 1.75 (with battery excluded) with 1.75 markup Baseline Premium relative to a conventional car Note that without the battery advance hybrids are cheaper than conventional cars. The battery cost is the origin of their high cost. Step 1, which links hybrids' price premium to battery cost and hybrid sales to battery revenues, transforms the problem of linking the vector of hybrid premium to the vector of hybrid sales into a scalar relation between battery revenues B R (t) and battery cost B c (t). This relation will be further detailed in Step 2. A.1.3. Battery revenues and battery cost The first issue that we need to address is that R&D activities take time. The new low-cost batteries based on today's R&D efforts will be launched after some delayed time period T. We can say that battery cost at time t+t, B c (t+t), is based on R&D that is motivated and financed by sales and revenues at time t, B R (t). First, we assess the typical R&D cycle T. Industry experts and researchers estimate a typical R&D process of developing a full car battery module, to take up to two years. Since the transition to hybrids and electric cars is expected to experience a long term product life cycle, relative to the two years of development to launch time, we will use system dynamics and technology learning curves. The development of a new battery involves: up-scaling a laboratory cell; solving thermal and robustness issues; designing a protective pack that provides a protective ambient, monitors the cells' operation, and controls the energy flow; and testing the module's performance and safety. A more aggressive R&D plan, with more resources, leads to better products, since several options, including those that are more promising but at that same time more risky, are developed in parallel. Still, although higher investments enable concurrent execution of several tasks, and result in an earlier final product, there is still a limit to how much development time may actually be shortened. Based on inputs from researchers and industry experts, we assess that time limit to be approximately two years. Most of the battery-related R&D efforts today are directed at lowering the battery cost, which influences the price of the cars with longer all-electric range (the HEV60 and BEV200) more than the shorter all-electric range (the HEV0 and HEV20). The battery cost reduction forecast is based on the past achievements of major battery developers over the last three years in developing new materials, processes, and battery packs, as well as on R&D investments during that time period. The cost of the Lithium battery module (incorporated in the Tesla Roadster in 2008) is about $1,500/kW h; further R&D will reduce the price even more. Unlike NiMH batteries, used in today's hybrids, which use rare raw materials, Lithium batteries use abundant materials and their present high cost stems from the manufacturing process. The cost of Lithium battery modules includes pack costs, with a well established cost reduction path, and cells materials and structures, which have a higher uncertainty technology learning curve. As the hybrid and electric car market grows, car battery sales will grow accordingly and manufacturing will improve and become more efficient. DasGupta et al. [17] evaluate that, based on the progress of pack, materials and processes, prices will decline to $300/kW h within a few years. Battery-related R&D investments will also grow with the market and encourage the development of lower-cost batteries. In evaluating a battery's evolutionary path and rate, we made three assumptions: (a) Cost reduction requires more effort as cost declines (see [25,52,61]). This means that maintaining the same effort will cause a slowdown of the cost reduction rate and Table A2 Propulsion battery energy storage capacity (size) and hybrids' price. Car configuration Conventional HEV0 HEV20 HEV60 BEV200 Battery Size (kw h) Actual battery module cost with today's NiMH batteries' ($2200/kW h) cost 0 $1100 $9900 $29,700 $88,000 Actual battery module cost ($) with varying battery cost ($/kw h) 0 0.5B c (t) 4.5B c (t) 13.5B c (t) 40B c (t) Hybrid premium ($) (A 1 ) based on battery module cost (line above) and baseline from A B c (t) B c (t) B c (t) B c (t) 2896

16 1224 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) maintaining the same cost reduction rate requires an increase in effort. To account for the diminishing effects of the efforts, we use a square root function. (b) Battery industry R&D will continue to focus on cost (see [18]) until the benefits of cost reduction, in terms of market attractiveness, cease to be dominant. This means that R&D budgets, which are allocated as percentages of sales, will mostly be directed to achieve cost reduction. (c) The evolutionary rate of the battery components is aligned with the overall evolutionary rate. This is a simplification assumption that refers to the average cost reduction. Referring to each component (such as pack and cells) separately may be slightly more accurate, but requires much more data and is more sensitive to noise. Battery cost reduction in 2007, which is a result of cumulative R&D resources in 2005, was 30%. Cumulative sales of hybrids by 2005 were 390,000. Since all current hybrids on the roads today are of the HEV0 type and incorporate NiMH batteries, which cost $2200/kW h, cumulative battery revenues in 2005 were $430 million. If we assumed a constant cost reduction rate (in percentage) then the cost path would have an exponential characteristic. If battery costs keep declining by 30% each period, as they did in 2007, then B c (t)=0.7b c (t 1), when t is the time in years. This is equivalent to B c ðþ= t B c ð0þ 0:7 t ; or logðb c ðþ t Þ = logðb c ð0þþ + t log 0:7 ða3þ which, as explained by Söderholm and Sundqvist [61], corresponds to a linear learning curve. Following Söderholm and Sundqvist [61], we assume that the learning curve is influenced by the cumulative R&D-based knowledge stock, rather than by time. When the cost reduction is not constant, but influenced by R&D investment, which is proportional to battery revenues B R (t), we replace t t by the scaled aggregated revenues kb R ðτþ, where k is a scaling parameter that represents the basic response between revenues τ =0 and cost attribute, and incorporates both the percentage of revenues allocated to R&D and R&D team capabilities. This k parameter can be calculated by instantiating the battery cost values of two recent successive periods, and the revenues that motivated this cost change. Taking into account the diminishing effects, we used a square root function of the cumulative R&D budget to represent the increasing difficulty in cost reduction as costs decline (see Assumption (a) above). Thus, when (A3) is delayed by T, it becomes, logðb c ðt + TÞÞ = logðb c ð0þþ + sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi t kb R ðτþ log0:7: τ =0 The scaling parameter of (A4) is calculated by instantiating battery prices from 2006 to 2008 and solving the equation. When using the prices, which are $2200 and $1500, respectively, we obtain: k = We found that using a different concave function, instead of a square root, provides similar results for both the short- and midrange. A.2. Availability and adoption levels For evaluating A 2, i (f i ), i=1,2,3,4, we checked how much R&D is allocated for new cars, and how much it costs to develop a new car model. Car manufacturers, like Mitsubishi and Honda, allocate about 6% of sales to R&D. Regarding the availability attribute, we did not assume a learning curve, as was done by Söderholm and Sundqvist [61], and as we did for the battery case, since extending a product line does not become more difficult as diversity increases. We assess the availability of new hybrids as a function of previous periods' sales. Following Dahan and Hauser's [12] funnel theory, we assume that the expenses for the final development stages are larger than earlier ones. As noted by Dahan and Hauser [12], similar products, like the HEV60 and BEV200, can share early development stages. We imply that a larger share of R&D budget is directed to short term plans and a smaller share to long term plans. Based on forecasted sales, we can estimate the R&D resources allocated for developing new hybrid car models; thus, we can assess the number of new hybrid car models that will be developed each year. Firms are assumed to monitor market preferences and direct their R&D efforts to developing hybrids of the types that are desired by the market. Taking $90,000,000 for car model development, (R&D_cost), $20,000 for a mid-size car price (see [24]), 6% of sales allocation for R&D (see Mitsubishi and Honda 2006 reports), and a policy following Dahan and Hauser's [12] funnel theory of investing more (75% in our example) of the R&D budget for the short term development plan and less (25% in our example) for the longer, two years development plan, we can assess the number of available car models in a certain year, given the sales of the previous two years. We also assume that there is a limit of 60% to the growth of R&D teams' productivity due to the need to train skilled personnel. The R&D resources allocated for diversifying the hybrids' product line are: ða4þ R&D i ðþ=20; t 000 :06 ð0:75n i ðt 1Þ +0:25n i ðt 2ÞÞ; i =1; 2; 3; 4; ða5þ where n i (t) is as in Eq. (6). The total number of hybrid models at time t, which is the sum of the number available previously and the number of new hybrids models at time t, is calculated by A 2;i ðþ= t A 2;i ðt 1Þ + R&D iðþ t R&Dcost = A 2;iðt 1Þ + 20; 000 :06 0:75f i ðt 1Þ +0:25f i ðt 2Þ ; i =1; 2; 3; 4: ða6þ

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18 1226 Y. Orbach, G.E. Fruchter / Technological Forecasting & Social Change 78 (2011) [59] C. Shih, A. Venkatesh, Beyond adoption: development and application of a use-diffusion model, J. Mark. 68 (2004) [60] A.K. Shukla, Fuelling future cars, J. Indian Inst. Sci. 85 (2005) [61] P. Söderholm, T. Sundqvist, Learning curve analysis for energy technologies: theoretical and econometric issues, EMF/IEA/IIASA International Energy Workshop, 24 26, Laxenburg, Austria, [62] I. Song, P. Chintagunta, A micromodel of new production adoption with heterogeneous and forward-looking consumers: application to the digital camera category, Quant. Mark. Econ. 1 (2003) [63] S. Sriram, P. Chintagunta, R. Neelamegham, Effects of brand reference, product attributes and marketing mix variables in technology product market, Mark. Sci. 25 (2006) [64] R.K. Srivastava, V. Mahajan, S.N. Ramaswami, J. Cherian, A multi-attribute diffusion model for forecasting the adoption of investment alternatives for consumers, Technological Forecasting Soc. Change 28 (1985) [65] M. Stadler, R&D dynamics in the product life cycle, J. Evol. Econ. 1 (4) (1991) [66] C. Su, Y. Chen, D.Y. Sha, Linking innovative product development with customer knowledge: a data-mining approach, Technovation 26 (2006) [67] J.H. Thun, A. Grobler, P.M. Milling, The diffusion of goods considering network externalities, Proceeding of the 18th International Conference of the System Dynamics Society, Bergen, Norway, [68] T. Tran, T.U. Daim, A taxonomic review of methods and tools applied in technology assessment, Technological Forecasting Soc. Change 75 (9) (2008) [69] G.L. Urban, J.R. Hauser, J.H. Roberts, Prelaunch forecasting of new automobiles: models and implementation, Manag. Sci. 36 (1990) [70] S. Weerahandi, S.R. Dalal, A choice based approach to the diffusion of a service: forecasting fax penetration by market segments, Mark. Sci. 11 (1) (1992) [71] A. Weber. The hybrid challenge, Assembly Magazine 49(6) (2006) [72] D. Yuan, H. Stolowy, M. Tenenhaus, R&D productivity: an exploratory international study, Rev. Accounting Finance 6 (2007) Yair Orbach has finished his PhD in marketing at Bar-Ilan University, Israel. He teaches diffusion theory and industrial marketing at Bar-Ilan University. His research focuses on forecasting market and technology dynamics. Yair has a long experience in the electronic systems and devices industry, in development, marketing and managerial positions at Agere, ModemArt, Motorola, and Nanolayers and is familiar with marketing and technology also from the practice scope. Gila E. Fruchter is a Professor of Marketing at the School of Business Administration, at Bar-Ilan University. She held visiting positions in Marketing at Washington University in St. Louis, MIT Sloan School of Management, University of California, Berkeley, Hong Kong University of Science and Technology and the Wharton School of Business, University of Pennsylvania. Her recent papers have appeared in Marketing Science, Management Science, Production of Operations Management, Journal of Service Research, Marketing Letters, Journal of Economic, Dynamics and Control, Automatics and more. She is a member of the editorial boards of Marketing Science, Journal of Service Research and more.

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