Marta Ventura

Christine Toh

Carlos Parra

Dr Gul Kremer

A Study of Sustainability Adoption Trends in the Transportation Market

Due to an increasing concern for sustainability, automobile manufacturers have been introducing hybrid variants of popular vehicles in an attempt to reduce the environmental impact of vehicles. However, it is still unclear how this 'hybridization' of popular car models affects the overall success of the vehicle. This paper seeks to address these gaps in knowledge by implementing a demand modeling approach, game theory framework, and a technology forecasting model. In the demand model, it was found that the percent of hybrid cars sold, the year and the model of the car are significant predictors of the total sales. In the game theory model, all possible scenarios suggest that an increase in hybrid sales would encourage market share growth. Lastly, using the technology forecasting model, S-curves were generated to show how the energy impact score (EIS) of automobile engines decreases over time as technology improves, and provide justification for the investment in hybrid engine development.

Introduction

Although the hybrid electric vehicle was first developed as an alternative to traditional internal combustion engine vehicles in as early as the 1900s, it has only recently gained momentum as a core segment of the automobile industry [START_REF] Lowery | Energy Diversity as a Business Imperative[END_REF]. Hybrid Electric Vehicles (HEVs) combine the conventional internal combustion engine and an electric propulsion system that that uses energy stored in a battery. This combination allows the vehicle to have same power and range as other vehicles while getting better fuel economy and lowering negative emissions since the combustion engine is reduced in size and capacity [START_REF] Rius | Electric Drive Design for Hybrid Electric Vehicle Optimum Fuel Efficiency[END_REF]. Although only comprising of a small percentage of the automobiles that are driven today, the United States is home to the largest fleet of HEVs [START_REF] Schweinsberg | Hybrid Sales Hit 2 Million Mark[END_REF], making it a significant part of any sustainability adoption consideration.

Since the HEVs are considered to be a step in the direction for a more sustainable driving future [START_REF] Khaligh | Plug-in hybrid electric vehicles: sustainable driving to 21st energy future[END_REF], an increasing number of consumers are adopting hybrid technology. Thus, automobile manufacturers are beginning to offer hybrid variations of their top selling models. Examples of this 'hybridization' of popular vehicle models include the Toyota Camry, Honda Accord, and Nissan Altima. While this strategy provides consumers with a more sustainable option for trusted vehicle models, it is still unclear how this 'hybridization' affects consumer adoption of HEVs.

In other words, since automobile makers largely influence consumer behavior (i.e., availability can drive desirability), the increase in hybrid sales should be investigated for its impact on the automobile maker's total sales. In addition, consumer perception of a particular automobile maker's brand could be influenced by this 'hybridization', ultimately affecting the success of the vehicle. Thus, this paper seeks to examine the role of HEV adoption on the success of a model of vehicle by using a Demand Modeling framework, as has been previously done by other researchers who seek to facilitate product planning in competitive markets [START_REF] Cook | Decision-Based Design: Making Effective Decisions in Product and SystemsDesign, The Role of Demand Modeling in Product Planning[END_REF][START_REF] Wassenaar | Enhancing discrete choice demand modeling for decision-based design[END_REF].

In addition to the proportion of hybrid cars sold by a single automobile maker, the behavior of other automobile makers have the potential to influence overall sales. One method of analyzing the market dynamics of a few major players is through the Game Theory framework. Formally, game theory involves the study of "mathematical models of conflict and cooperation between intelligent rational decision-makers" [START_REF] Myerson | Game Theory: Analysis of Conflict[END_REF]. The rational decision makers to be considered in this paper are the individual automobile manufacturers, and game theory can be used to uncover the most effective and profitable strategy to be taken by multiple players (automobile manufacturers). Thus, one of the main goals of this paper is to examine the relative payoffs (sales) for each major automobile manufacturer in when considering varying levels of hybrid car sales.

While the impact of hybrid sales and competitors' behavior can certainly affect the success of a particular vehicle, the life-cycle of hybrid technology needs to also be examined. This is important for justifying investment in HEVs, given that most all technologies are surpassed by new technologies eventually. One method of analyzing the life-cycle of technologies is through the use of S-curves that depict how the technology has evolved over time [START_REF] Meyer | A Premier on Logistic Growth and Substitutions: The Mathematics of the Loglet Lab Software[END_REF]. The start of the S-curve depicts "market adoption" that relates to the birth of a new market opportunity. In this specific case, Toyota Camry first introduced hybrid cars in 2006. Typically, "early adopters" are consumers that are interested in testing and trying out new products. Following this phase, the technology moves into a mass manufacturing and distribution phase. Lastly, as the market matures, at which point "late adopters" who are risk adverse, begin to purchase the product. Therefore, this paper seeks to examine the growth of hybrid technology over time and provides justification for investing in a larger proportion of hybrid sales within the next ten years.

The main objective of this paper is to explore trends in hybrid technology adoption and examine the impact of competitors' behavior on the success of different models of vehicles. Therefore, this paper seeks to address the following research questions:

(1) How does the percentage of hybrid cars affect the overall sales for that model? (2) How do competitors' behavior with regards to the proportion of hybrid cars being sold affect overall sales? (3) What is the future of hybrid technology and is there justification for continual investment in HEVs?

Methodology

In order to answer our research questions, a method of analyzing the current trends in hybrid vehicle technology and predicting the competitive landscape is presented in this section. Since the current problem involves uncovering trends in hybrid technology, three different example vehicles are used to illustrate the process, namely, the Toyota Camry, Honda Accord, and Nissan Altima vehicles. These vehicles were chosen because of the similarities in quality (belonging to the same 'tier' within each brand), and the availability of sales data. In addition, all three models are available as non-hybrid and hybrid alternatives. Thus, the first step of the proposed methodology involves utilizing a Demand Modeling approach to analyze the relationship between the proportion of hybrid cars sold and the total sales. Next, a Game Theory framework is implemented in order to gain a better understanding of the impact of competitors' behavior on sales. Lastly, the future of hybrid technology is analyzed from a sustainability perspective using Technology Forecasting. These steps are demonstrated and discussed in the following sections.

Demand Modeling of Hybrid Sales

The first step in determining the best strategy for maximizing sales is to examine the impact of various factors on the total sales of the model of vehicle. In particular, the proportion of hybrid sales is studied in this paper since it provides a normalized indication of the amount of hybrid cars being purchased for each model. In other words, we seek to determine if the proportion of hybrid sales can significantly predict total sales for the three chosen models of vehicles. Thus, the amount of sales of the hybrid variations of each model of vehicle (S hybrid ), as well as the total sales of each model of vehicle (S total ) were obtained through the companies' sales reports. Then, the proportion of hybrid sales (P hybrid ) is simply computed using Equation 1for the years that each model had a hybrid alternative available.

(1)

A linear regression analysis was then performed using S total as the dependent variable, and P hybrid , model, and year as independent variables. The year was included as a variable in the analysis in order to account for fluctuations in consumer behavior throughout the years. Furthermore, since the hybrid variants of the different car models were introduced at different points in time (different year ranges), the years that saw both regular gas-powered engines and hybrid variants of the same model were included in the model. For example, from the year 2007 to 2010, Toyota produced both regular gas-powered Toyota Camrys and Hybrid Toyota Camrys. Thus, the year ranges were adjusted for each car model accordingly. In addition, the model variable was included in the analysis in order to account for inherent differences in sales between different vehicle models. Since the model variable is categorical in nature (Toyota Camry, Honda Accord, and Nissan Altima), 'Dummy Coding' was used to analyze the variable in linear regression. The total sales for each vehicle model, the model's hybrid sales, and the proportion of hybrid sales for each model are shown in Table 1. The results of the linear regression analysis conducted to model the impact of the proportion of hybrid sales revealed a significant relationship between total sales and the proportion of hybrid sales, model, and year variables (R 2 = 0.90, p < 0.00). This result indicates that the proportion of hybrid sales indeed affects the total sales of the vehicle. In addition, differences in the model and year of the vehicle can be said to affect the total sales of the vehicle as well. This is unsurprising since different vehicle models are bound to have different sales figures, affected by changes in consumer behavior from year to year. In particular, total sales appear to decrease following each year, but tend to increase as the proportion of hybrid sales increase, see Equation 2.

(2)

Implementation of Game Theory Framework

Implementing game theory is a good way to analyze how the sales of a car will be impacted depending on which "move" or policy the competitors make. From demand modeling, it was found that overall sales were influenced by the percentage of hybrid cars the company produced. Taking this information into account, a game theory scenario was constructed where two companies were evaluated at two different levels of percentage of hybrid cars produced, out of their total number of cars. The two levels chosen were 3% and 10%. There were three pairwise comparisons performedone for each pair of vehicles (Toyota Camry, Honda Accord, Nissan Altima). The assumed strategy of the game was to maximize market share among the competing companies, by examining all perceivable strategies and their corresponding payoffs for each manufacturer [START_REF] Leyton-Brown | Essentials of Game Theory: A Concise, Multidisciplinary Introduction[END_REF]. The values were obtained directly from the multiple-linear regression developed for demand modeling. A unique Nash Equilibrium point (NE) was found, which means that neither company has an incentive to deviate from this strategy, thus both companies should be at 10% hybrid in order to increase their market shares, regardless of the strategy of the opponent. This result was found when performing pairwise comparisons of all car companies, see Figure 1. This is an indication that hybrid technology is becoming a vital segment of the automobile industry, and could likely benefit automobile makers even in a competitive environment. It should be noted that Toyota Camry has the highest market share values regardless of the 'game scenario', and can be said to 'dominate' the market, possibly due to the overall brand size and popularity.

Technology Forecasting of Hybrid Engines

In order to analyze the technology life-cycle of hybrid engines, the Energy Impact Score (EIS) was used to measure the sustainability of the engine. This metric is used as an industry standard for measuring the number of barrels of petroleum consumed by a vehicle in one year [START_REF] Cities | Vehicle Buyer's Guide[END_REF]. Thus, the lower the EIS, the more sustainable the vehicle is said to be. The 'growth' of the technology, or in this case, the decrease in the EIS, provides an indication of trends in the sustainability of vehicle engines, and can serve as a method of predicting the sustainability of these engines. This trend is captured using S-curves, a mathematical function that models changes in parameters over time using a logistic form. In our study, the EIS generates a backwards S-curve, because the EIS decreases over time, as technology improves. Therefore, the logistic growth functions used in this paper are monotonically decreasing, reflecting and improvement in the EIS score, as seen in Equation 3.

= (3)

Where F is the Energy Impact Score, P is the saturation point (determined through optimization), and α and β are coefficients of the exponential function. Then, for every point in the data set, we compute as well as ln( ). This transformed form of the exponential function is shown in Equation 4.

ln( ) (4)

Values of ln(

) are plotted to find the slope, and intercept, . Equation 5 is then solved for F, treating this new value as an estimator, .

The points for are plotted to visually illustrate the trend of this technology and how it evolves, if it continues at its current pace. The sum of square error between the actual data point (EIS) and is then minimized to find the lowest energy impact score that a car can achieve. The S-curves is created for the hybrid car engines as well as the non-hybrid car engines and are shown in Figures 2 and3. Based on the S-curve, the lowest EIS that hybrid cars can achieve is 6.68, around year 2065. Effectively, hybrid cars achieve 95% of their potential for sustainability around the year 2025 (EIS = 7.01). For non-hybrid cars, the lowest EIS that can be achieved is 11.86, which saturates around year 2053. Non-hybrid cars achieve 95% of this value around the year 2016 (EIS = 12.45). To create an S-curve that combined the S-curves of hybrid and non-hybrid into one growth by means of weighted averages, a new EIS metric needed to be formulated. For the years where the hybrid and non-hybrid cars overlap (2007-2012), we compute a weighted EIS value (WEIS) for each year i, see Equation 6.

WEISi = EISnon-hybrid (Snon-hybrid ) + EIShybrid (Shybrid ) (6)
Where EIS non-hybrid and EIS hybrid correspond to the EIS values of the respective types of vehicles, and S non-hybrid and S hybrid correspond to the sales of the respective types of vehicles. This weighted EIS score is computed for all years with sales in both hybrid and non-hybrid vehicles, for each model of vehicle. For years where only non-hybrid cars were sold, a regular average was used to compute EIS. Using these new values

Discussion

From our results, it was found that there was a significant relationship between total sales and the proportion of hybrid sales, model, and year variables. In particular, the higher the percentage of hybrid cars sold, the higher the overall sales for that model of vehicle. This result is important, because while the proportion of hybrid sales may not directly predict total sales, it indicates a positive trend between these two variables. In other words, a higher proportion of hybrid sales is associated with more success for a model of vehicle, encouraging car manufacturers to consider strategies for increasing hybrid car sales. This result could be attributed to the relationship between the proportion of hybrid cars produced and the success of a particular car manufacturer. From a policy-making and consumer standpoint, identifying factors that affect the overall sales of vehicles could prove useful for making decisions regarding the purchasing of vehicles.

From the game theory implementation, it was found that it would benefit all competing companies to have a higher percentage of hybrid sales. Since we found that the proportion of hybrid cars affects sales in the demand model, then we looked at different strategies that companies can make regarding the proportion of hybrid cars to produce and how it would affect their sales. For all three scenarios, the results suggest that the best strategy for all companies is to increase hybrid sales. These results show how hybrid technology is imperative for automobile companies to incorporate in order to be competitive. Further analysis could include a more in-depth analysis on the effect of brand positioning and company size on market shares.

The third step of the methodology involved technology forecasting of hybrid and non-hybrid engines. Using the sustainability metric, Energy Impact Score, we generated a total of 3 S-curves corresponding to the EIS of the Toyota Camry hybrid, the Toyota Camry non-hybrid, and a combined hybrid and non-hybrid curve. It is important to note that the backwards S-curve shown in the model represents the tailend of the S-curve because it already passed the inflection point for the years 2002- Our results showed that the saturation level of the EIS for hybrid vehicles is almost half of non-hybrid vehicles. This can be taken as justification as for why automotive makers should invest in a larger proportion of hybrid sales. That is, nonhybrid vehicles will likely never achieve the same level of sustainability as hybrid vehicles. In addition, since hybrid engines are predicted to continually improve in sustainability for the next 10 years, there is further motivation for automotive manufacturers to invest in hybrid vehicles.

Conclusion

This paper explored the factors that impact overall sales, and found that the proportion of hybrid sales, the year, and the type of car significantly influenced sales. It was also found that behavior of competitors' impacted the overall sales of a particular vehicle, highlighting the need for companies to consider the proportion of hybrid sales in competitive analysis. Lastly, we found justification for investment in hybrid technologies since improvement to sustainability scores are predicted to increase substantially for the next 10 years. Future work should incorporate larger databases of vehicle sales information in order to achieve a more generalizable model for predicting sales trends. In addition, we recommend that policies that encourage adoption of hybrid technologies (such as tax incentives) could benefit both automobile makers and consumers. Other measures of sustainability (such as amount of energy used to manufacture batteries) should be considered in future research regarding sustainability in order to increase the validity of the results.

Figure 1 :

 1 Figure 1: Market share values for the NE point across all pairwise comparison scenarios.

Figure 2 :

 2 Figure 2: S-curve of EIS for hybrid vehicles.

Figure 3 :

 3 Figure 3: S-curve for EIS for non-hybrid vehicles

  the S-curve for both hybrid and non-hybrid cars was constructed and is shown in Figure4. From this curve, the lowest Energy Impact score for weighted Hybrid and Non-hybrid cars can achieve is 10, which saturates around year 2075. Practically, 95% of the sustainability would be achieved around the year 2042 (EIS = 10.5).

Figure 4 :

 4 Figure 4: Combined Hybrid and Non-Hybrids S-curve.

Table 1 :

 1 Proportion of hybrid sales computed using sales of hybrid vehicles and total sales for each model of vehicle, for each year.

	Vehicle Model	Year	S hybrid	S total	P hybrid
	Toyota Camry	2007	24477	418631	0.1151
		2008	6272	430345	0.0144
		2009	22887	333937	0.0641
		2010	14587	313217	0.0445
	Honda Accord	2004	1061	358709	0.00274
		2005	16826	352467	0.0456
		2006	5598	348843	0.0158
	Nissan Altima	2007	8388	276374	0.0295
		2008	8819	260849	0.0327
		2009	9357	194211	0.0460
		2010	6710	22253	0.0293


Simon W Miller

Paolo Pecorario

Lisa M Ulan

Sustainability Adoption Trend Analysis

Keywords: sustainability, game theory, technology forecasting

The sustainability trend in the automotive market has been analyzed. A dataset of cars was elicited from select companies that have increased their market share in the United States automotive market over the past decade. In the first part, a linear regression model is developed to evaluate how the market share is influenced by key vehicle characteristics, and in particular, to evaluate the role of sustainability in that analysis. For the second part, game theory has been applied to see how market dynamics change in relation to sustainability moves of two competitors. The third part uses technology forecasting techniques to suggest which technology to invest. The results of the paper show that, in the long term, sustainability will be a significant factor in determining a company's market share. Investing in manufacturing processes that reduce the cost of battery systems can support the competitiveness of hybrid vehicles but so too can investing in research and development to reduce the energy density gap between gasoline and batteries.

Introduction

As a society, we are becoming more aware of non-renewable resource depletion. Future generations are discussing the utility and potential of sustainable resources in the current market. Behavior regarding sustainability is extremely complex as it deals with factors such as politics, beliefs versus actions, and socio-economic status. Several set-backs in industry exist in regard to producing sustainable products such as lack of clarity on how to implement sustainability in industry, and possibly most important, when to release a sustainable product into the market. Industry needs to know how the market will behave and react.

Increased awareness and education levels impact sustainable behavior; however, this is an ongoing research topic. Our objective is to research sustainability trends in terms two automotive competitors to see how the market reacts. This will be done through demand modeling and predicting market share of certain vehicles and their attributes, game theory, and technology forecasting of a sustainable vehicle attribute and how it pertains to investment. This paper is divided into background information, the perception of sustainability, a game theory application, battery technology forecasting, and conclusions.

Background

The Environmental Protection Agency (EPA) has been categorizing sustainability for some time and has designated carbon dioxide (CO2) as a ". . . greenhouse gas that traps the earth's heat and contributes to the potential for global warming" [START_REF]Automobile Emissions: An Overview[END_REF]. There are many contributors to this effect from the automotive industry including the development, manufacture, transportation, use, and infrastructure required to maintain a fleet of vehicles both in the U.S. and across the globe. Omar [START_REF] Omar | The Automotive Body Manufacturing Systems and Processes[END_REF] performed an energy audit of the amount of energy and CO2 generated during an energy audit of a typical light passenger vehicle over ten years and concluded that end-user use corresponds with approximately 90% of the total energy and greenhouse gas emitted during the life of a vehicle.

Manufacturing and transportation costs are a difficult metric to reduce in the abstract so a quick investigation into why material and consumer use contribute so heavily to these sustainable metrics is explored. The energy audit by [START_REF] Omar | The Automotive Body Manufacturing Systems and Processes[END_REF] showed a typical breakdown of materials by weight for a typical sedan. The standout is that mild steel contributes 44% of the total weight of the vehicle. There is a trend in manufacturing to incorporate composites and plastic to reduce vehicle weight and to increase fuel economy by allowing for more aerodynamic shapes to be designed. The metalworking required to generate steel and the machine it is energy intensive and can relate to global effects.

The weight of the vehicle has a direct correlation with energy consumption for both manufacturing and consumer use. Data collected by [START_REF] Omar | The Automotive Body Manufacturing Systems and Processes[END_REF] demonstrate the relationship between vehicles' energy consumption per kilometer of travel and fuel economy versus curb weight, and greenhouse emission versus energy consumption for a variety hybrid and non-hybrid vehicles. It suggests that decreases in weight of the vehicle as well as increases in fuel economy can lead to more sustainable vehicles.

Vehicles are a commodity and their influence on sustainability can be controlled by consumers through the purchase of vehicles that are lightweight and have good fuel economy. There are many other factors affecting the automotive industry to address sustainability, and the reader is encouraged to read through [START_REF] Mayyas | Design for sustainability in automotive industry: A comprehensive review[END_REF] for an excellent review discussing design for sustainability in the automotive industry. In the end, consumers play a large role in determining the viability of a product and so Sect. 3 discusses a method to determine if sustainability is important or even relevant to consumers during their vehicle purchasing decision.

Perception of Sustainability

How has the consumer-perceived value of sustainability changed across time? What is to be expected in the near future?

We first focus attention on the customer perceived value of sustainability, how this has changed across time, and how it is expected to be in the near future. The focus is on the value that sustainability has for a customer, how sustainability contributes to make a specific product more desirable, and how sustainability increasing the value of a product for a customer. The analysis presented here is based on the automotive market, but the principles can be implemented in many other different fields, and for different kind of products.

Methodology

To understand if and how sustainability contributes value for the customer, a benchmark was defined and data were collected. We focus on two car makers that have been able to increase their market share in the last decade, based on trends collected from [START_REF] Wardsauto | The Information Center For And About The Global Auto Industry[END_REF], namely Toyota and Honda. These two car makers have increased from 9.1 to 14.1% for Toyota, and from 6.5 to 9.6% for Honda in this time. They are considered in our analysis as two companies that seem to be trending together and thereby making them direct competitors. A sample dataset is collected of their vehicles, including a hybrid and a gasoline vehicle. The combination of hybrid and non-hybrid is used to determine if there are synergies for the automaker via a halo effect. Two models of cars from each manufacturer have been taken into account in the dataset, the Toyota Prius, Toyota Corolla, Honda Civic Hybrid, and Honda Accord.

Since these two companies have increased in market share, they may have successful features that other companies do not have. A regression model is developed for the analysis as [START_REF]Automobile Emissions: An Overview[END_REF], where the dependent variable (y) is the market share. The regression is a linear model with two-factor interactions.

y(x) = β 0 + n i=1 β i x i + n i=1 n j>i β ij x i x j (1)
The market share depends on many variables including primary (e.g., price, reputation, and fuel economy), secondary (e.g., style), etc. A subset of characteristics was chosen that could be readily gathered by consumers online. The independent variables included are as follows:

• Year -the model year of the vehicle. • Manufacturer's Suggested Retail Price (MSRP) -nominal price of the car.

• Curb weight -total mass of the vehicle with standard equipment. It can be viewed as a surrogate metric for car dimension, perceived safety, sustainability, and reliability of a vehicle. • Combined Fuel Economy -in miles per gallons, a well know index of efficiency performance of the engine, which can be used as an estimation of fuel consumption and expenditure for a particular kind of car. • Greenhouses Gases Emitted -in tons per year, a direct metric of sustainability of a car. It includes carbon dioxide, methane, and nitrous oxide.

These variables are some of the most common attributes that are accessible to a consumer when evaluating a vehicle. These variables are advertised by the car makers and the metrics are well known. Other variables can be elicited for this problem, but these five attributes are simple and are common to consumers when describing a vehicle purchasing decision. These attributes were collected from [START_REF][END_REF] and [6] for 2001 through 2012 when available.

Results

A regression analysis was performed using best subset analysis, a method that examines all possible combinations of predictor variables using (1) as the model, with centered predictors. The model with the highest coefficient of determination (R 2 ) is selected -the generated model has an R 2 of 0.916 with 43 samples. Model coefficients and statistical measures is shown in Table 1. The model incorporates interactions which make interpretations more complex, but we can observe that the model suggests several features relevant to sustainability and describe them here briefly. We note that Greenhouse has a strong negative coefficient and, when coupled with Year, MSRP, and Weight, suggest that market share may decrease if it is not addressed. This is consistent with a greener perspective that has been touted in recent years. MPG also has a general trend of negative impact on market share implying that consumers are concerned with fuel economy in the current economy; improvement in fuel economy is expected through time. MSRP when coupled with Year has a negative coefficient, suggesting that decreases in prices over time should increase market share. MSRP when coupled with Greenhouse has a positive coefficient suggesting that greenhouses gases emitted are a metric related to engine performance or luxury, i.e., more performance tends to yield more emissions. MPG coupled with MSRP has a positive coefficient suggesting that as fuel economy increases, the more the customer is willing to pay. This can be viewed as a basic metric of sustainability; the customer may not be directly interested in environmental sustainability, but may likely be interested in saving money with fuel economy.

Discussion

The linear regression model suggests that sustainability has increased customer perceived value throughout years based on market share and a few consumer predictors. This result supports push for competitors to develop more sustainable vehicles by investing in sustainable energy systems and features. The regression describes overall market trends, but how does a company position itself to gain more market share? Game theory, described next, is a tool that can help.

Game Theory

How do you expect the market dynamics to change in relation to sustainability moves of the two competitors?

A game theory matrix is a game that has two players that have a finite strategy set with a specified number of elements, and the payoffs are posed in an S 1 × S 2 matrix. Player 1 and player 2 independently and simultaneously choose a strategy s 1 ∈ S 1 , s 2 ∈ S 2 , respectively and receive their designated payoff, u i (s 1 , s 2 ). Toyota and Honda are competitors in the car industry and will be represented as two players in our game.

Methodology

A model was used to predict market share for the two companies, Honda and Toyota. The model includes the same dependent variables as in Sect. 3, and the dependent variable output predicted by the model is the percentage of the market share of a hypothetical vehicle. The sustainability aspect of this model is the amount of greenhouse gases emitted. The purpose of this game is to see how market dynamics (or market share) change in relation to sustainability moves, and in particular greenhouse gases emitted from different vehicle designs.

Results

Different hypothetical designs are created for each company, and a regression model is used to predict the market share of that vehicle for each company. The application of the game theory model is presented using the scenario in which both companies use the same model to investigate interactions between one another. The method can also be applied independently to look at individual strengths.

Five car designs were chosen for each company based upon the five attributes: Year, MSRP, Weight, MPG, and Greenhouse. These car designs can be viewed in Table 2.

The Nash Equilibrium is Toyota T2, Honda H5. This indicates that if Toyota were to choose their T2 vehicle design, Honda should choose their H5 vehicle design, and vice versa. This is the optimal scenario for each company as neither company has an incentive to deviate. The game can become more complicated by inserting external factors such as competition for customers, customer loyalty, and so on.

Discussion

Based upon these games, we can see that in the in the long term sustainability should increase market share, but in the short term it does not. If the company is to invest long term, a strategy can emerge that requires development time and cost now, but will have dividends later. What technology will provide the most "green" alternative? Or will better hedge against competition? A forecasting technique is now used to determine which technology might bolster a company's green appeal.

Technology Forecast

What is one of the technologies you are able to justify investing into? How do you expect to see the performance (and may be cost) to change of this technology? An issue that Hybrid electric vehicles have is that their competition (gasoline powered automobiles) uses a very energy dense fuel (∼12800 Wh/kg) to provide power and range. Battery technology has yet to progress at a pace with Moore's Law [START_REF] Jogaleker | Moore's Law and battery technology: no dice[END_REF] which states that many technologies have been doubling their performance every 18 months due to fundamental chemistry inherent to a battery's inner workings [START_REF] Schlachter | No Moore's Law for batteries[END_REF].

Certainly there must be a technology that can achieve a high energy density so that batteries can compete with gasoline engines. Technology forecasting is a method that uses historical data to predict future characteristics of useful technological machines, procedures, techniques, protocols, etc. The primary use of the method is to analyze the current state-of-the-art and determine, through a regression analysis, how the technology will perform in the near future.

The energy densities of various battery technologies are forecast and analyzed to determine if investing in a particular technology will become competitive or if a new technology should be invested. The following sections describe the methodology of forecasting and their interpretations.

Methodology

The technology life-cycle describes the commercial impact of a product through research and development through profitability. The "shape" of the curve describing a technology is often referred to as an S-curve because of its slow growth through its conception and introduction, its steady growth through product maturity, and its slowed growth at the end of its life-cycle at which point a newer technology assumes a similar pattern. This shape can be parameterized as [START_REF] Omar | The Automotive Body Manufacturing Systems and Processes[END_REF] where: f (t), performance of a metric over time t; f 0 , performance offset time for introduction; S, scaling factor for total growth; α, rate of growth; β, shifting parameter.

f (t) = f 0 + S exp (αt + β) 1 + exp (αt + β) (2)
Equation ( 2) is essentially a logistic regression with a scaling term S and an offset term f 0 . The parameters are estimated using a nonlinear regression routine that uses historical data over time for each technology metric of interest. The data used in the analysis was taken from [START_REF] Linden | Handbook of Batteries[END_REF] for various primary batteries including: Zn-Carbon, Mg/MnO2, Zn/HgO, Alkaline-MnO2, Li/MnO2, Li/SO2, Li/SOCI2, and Zn/Air. Details about the batteries, their characteristics, applications, and cost can be found in [START_REF] Linden | Handbook of Batteries[END_REF].

Results

The nonlinear regression model described in (2) was implemented and the results are shown in Table 3. The table shows the regression coefficients (appropriate units are assumed) and the predicted performance of the technology in the year 2013 and 2020.

Discussion

The linear regression indicates that each technology appears to be saturated with Zn/Air being the largest at ∼379 Wh/kg. Zn/Air does, however, suffer from environmental conditions and would most likely deteriorate during use in outdoor settings such as driving. Li/SOCl2 is the next highest with ∼323 Wh/kg, which is still quite high when compared to current lithium-ion secondary batteries that have an energy density of ∼130 Wh/kg [START_REF] Linden | Handbook of Batteries[END_REF]. Li/SOCl2 currently cost quite a bit more than conventional battery systems which may diminish its utility. However, the regression performed during technology forecasting does not incorporate questions of cost -it only describes the performance over time.

Conclusions

In this paper, the role of sustainability in the consumer perceived value has been analyzed. From the linear regression model we can hypothesize that sustainability has increased customer perceived value throughout the years. The regression ignores contingency variables such as fuel prices and economic crisis, but as a simple model it captures the effect of sustainability in relation to market share quite well. This result of the model could leads competitors to develop more sustainable vehicles by investing in sustainable energy systems and features. Investment in "greener" technology is justified by a higher market share over years. For what concerns technology investment justification and performance expectation, two possible solutions can be under-taken: either invest in manufacturing enhancements of this battery type or to begin research and development in a different technology. Further research should focus on the impact of contingency variables and how they affect consumer purchasing habits.

Table 1 .

 1 Regression Model Output

	Predictor	Centroid Estimate Standard t-Statistic	p-value
			Error
	Constant (β0 )	-	1.448700 0.095081	15.237 3.20E-16
	Year	2006.91	0.067261 0.027279	2.466 0.019221
	MSRP	20781.70 -0.000179 0.000085	-2.124 0.041512
	Weight	2864.37	0.004038 0.001299	3.108 0.003935
	MPG	34.465	-0.121992 0.089362	-1.365 0.181727
	Greenhouse	4.602	-0.528704 0.756184	-0.699 0.489494
	Year*MSRP	-	-0.000006 0.000005	-1.227 0.228865
	Year*Greenhouse	-	-0.097961 0.043220	-2.267 0.030311
	MSRP*MGP	-	0.000071 0.000017	4.115 0.000253
	MSRP*Greenhouse	-	0.000767 0.000187	4.095 0.000268
	Weight*Greenhouse	-	-0.002912 0.000954	-3.053 0.004540

Table 2 .

 2 Hypothetical scenario vehicle builds for Toyota and Honda

	Scenario Model Year MSRP Weight MPG Greenhouse Market Share
	T1	Toyota 2012 26900	3042	50	2.97	0.962
	T2	Toyota 2012 25500	3042	50	2.97	1.459
	T3	Toyota 2012 27900	3000	51	2.96	0.582
	T4	Toyota 2012 26900	3000	51	2.95	0.841
	T5	Toyota 2012 28000	2900	55	2.90	0.976
	H1	Honda 2012 24200	2853	44	3.37	0.181
	H2	Honda 2012 23000	2853	44	3.37	0.752
	H3	Honda 2012 25800	2800	48	3.50	0.349
	H4	Honda 2012 24200	2800	48	3.50	0.495
	H5	Honda 2012 27500	2750	50	3.60	0.990

Table 3 .

 3 Results of the regression analysis of forecasting various battery technologies

	Technology	f0	S	α	β f (2013) f (2020)
	Zn-Carbon	55.609	41.695 0.506 -1001.820	97.3	97.3
	Mg/MnO2	80.217	37.114 0.090	-177.560	116.9	117.1
	Zn/HgO	38.189 101.846 0.061	-120.145	136.1	137.4
	Alkaline-MnO2	62.235 102.268 0.473	-936.427	164.5	164.5
	Li/MnO2	145.335	87.388 0.551 -1093.350	232.7	232.7
	Li/SO2	127.233 161.985 0.316	-622.261	289.2	289.2
	Li/SOCI2	17.748 305.175 0.505	-996.407	322.2	322.9
	Zn/Air	77.890 301.797 0.179	-352.650	379.4	379.6


Paolo Pecorario
email: pwp5155@psu.edu

Eliciting a Mode of Transportation to Improve Product Life Cycle Performance

Keywords: utility theory, transportation, PLC, product life cycle

The purpose of this paper is to describe how to implement the utility theory to a supply chain problem. In this problem, two different modes of freight transportation are compared to improve life cycle performance of a mass product: trucks and trains. First, the problem is described, the criteria and attributes are introduced and the dataset for this problem is defined. Then, a description of the utility theory model is conducted, explaining the choice of the multiplicative model instead of the additive one. After that, the utility functions for each attribute are elicited, and a trade-off analysis is conducted in order to define indifference points and to calculate the scaling factors. Then, the aggregation function is formulated for this problem, the alternatives ranked, and the best mode of transportation elicited.

Introduction

The utility theory is applied to a supply chain problem: eliciting a mode of transportation to improve product life cycle performance of a particular kind of product (refrigerators) on a specific environment (European continent). The problem has been drawn according to [START_REF] Wu-Hsun | A modular design approach to improve product life cycle performance based on optimized closed-loop supply chains[END_REF]. However, [START_REF] Wu-Hsun | A modular design approach to improve product life cycle performance based on optimized closed-loop supply chains[END_REF] does not directly take into account how to ship the refrigerators: there is no information about the mode of transportation used. In order to improve this shortcoming, the aim of this paper is to elicit the best alternative to ship refrigerators. More specifically, two different modes of freight transportation are taken into account for this problem: trucks and trains. The first mode of transportation is represented by three different types of general freight truck, divided by mileage: less than 250 miles (truck type A), between 250 and 500 (truck type B), and over 500 miles (truck type C), according to [START_REF] Forkenbrock | External Costs of Truck and Rail Freight Transportation[END_REF]. Trucks are well known to be flexible, they require little time to be loaded, and they do not need railroad facilities: they can easily go wherever there is an asphalted road. Trucks' drawbacks are pollution emissions and higher cost per ton per mileage. On the other hand, trains are reasonably flexible, less expensive than trucks and produce much less emissions. The main drawback of trains is the journey time:

given that trains usually may carry a large quantity of goods, loading time is consequently longer compared to trucks. In this analysis, four different trains have been taken into account: Heavy Unit Train, Mixed Freight Train, Intermodal Train, and Double-stack Container Train [START_REF] Forkenbrock | External Costs of Truck and Rail Freight Transportation[END_REF].

Criteria and Attributes

In order to rank the alternatives, three criteria have been considered relevant for this problem: flexibility, cost, and emissions. For each criterion, different attributes have been used, as follows:

 Flexibility: ─ Maximum distance that the mean of transportation can cover (max dist.) ─ Maximum load that can be carried (max load) ─ The average speed of the truck or train (avg. speed) ─ The easiness to restore, repair, and replace truck or train (eas. to R/R)  Environmental Impact: ─ The amount of NO X gas emitted by the mode of transportation (NO X ) ─ The amount of CO 2 gas emitted by the mode of transportation (CO 2 ) ─ Environmental Risk: based on the overall environmental impact and footprint (env. risk)  Average Cost per Ton Mile (avg. cost p t-m) Some data has been drawn by [START_REF] Forkenbrock | External Costs of Truck and Rail Freight Transportation[END_REF], others (avg. speed, eas. to R/R and env. Risk) have been calculated by using some assumptions and making estimation: for this reason the data in input is affected by some variations, and it introduces a source of uncertainty in the model. For instance, data may slightly vary depending on the driving condition, the maintenance status of the engine, the type of road and several other factors. For this purpose, utility theory has been developed for this problem. The dataset for the problem is reported as follows:

Utility Function Elicitation

In this section, the utility function elicitation is performed. First, for each attribute, it is necessary to define a function and its parameters. In this way, for each attribute, it is possible to calculate the utility that corresponds to each level of the attributes itself. The utility range goes from zero to one. The utility function gives a value of zero to the worst level of each attribute, and a utility of one to the best level. The largest or the smallest number of each attribute is considered the best or worst case depending on the minimization or maximization of that particular attribute.

Mathematical Model of the Utility Function

In this section, we first describe the method used to determine the utility function for each attribute. Among the several different types of function that can be used, the exponential function is the most popular. This is due to the fact that it is sufficient to define only one parameter (RT) to describe the curve. In this case study, we use the exponential function, which is reported as follows:

( ) ( ) (1)
Where U(x) is the utility of consequence x; RT is the parameter that determines the curvature and it is called risk tolerance (it determines if the DM is risk adverse, risk neutral or risk seeking); EXP is the exponential function; A and B the scaling parameters. These two parameters depend on RT too, as follows:

( ) [ ( ) ( )]
(2)

[ ( ) ( )] (3)
In order to define the RT parameter, three points must be defined. Two points are the end points of the dataset, corresponding to the best and the worst case; the third point is chosen by the decision maker by using the certainty equivalent. This is the amount of payoff (in terms of utility) that the decision maker is willing to receive to be indifferent between that payoff and a given gamble [START_REF] Sargent | Macroeconomic Theory[END_REF]. The certainty equivalent is calculated as follows:

[ ] (4)
Where CE is the certainty equivalent; RT is the risk tolerance parameter (that we want to find); LN is the natural logarithm function; and EU the expected utility.

In order to estimate the correct value of RT for three given points (best case, worst case, and certainty equivalent), it is possible to use the following:

( ) [ ( )] [ ( )] (5)
Equation ( 5) states that the utility of CE must be equal to the expected utility of the lottery. The value of RT that verifies this equivalence should be used to describe the curve.

The Utility Function Elicitation for Each Attribute

In this section, the utility functions are elicited for each attribute. For any of them, the assessment inputs are chosen; the risk tolerance and the scaling factors are calculated; the function is drawn; and finally the utility for each alternative is reported.

The Multiplicative Aggregation Method

The choice of the multiplicative method is required whenever the attributes involved are correlated among each other. As soon as there is at least one significant correlation among two variables, a multiplicative model must be used. In our problem, there are some strong correlations (positive or negative) among the attributes, which are reported as follows:

 Average cost per ton-mile is negatively correlated (ρ= -.72) with the maximum load: the higher is the load, the lower is the average cost per ton-mile  The pollutant gases NO X and CO 2 are positively correlated (ρ= .85)  The environmental risk is highly correlated with the maximum load (ρ= .97) Since there are some correlations among the problem's attributes, the multiplicative model has been implemented. According to [START_REF] Thurston | Decision Theory for Design Economics[END_REF], the overall utility is given by the following:

( ) [∏ ( ( ) ) ] (6)
Where, u i is the utility of the attribute x i and it is defined by the utility function, k i is the scaling factor of the attribute x i , and it will be defined using a trade-off comparison analysis among the attributes, and K is a non-zero solution to the equation [START_REF] Thurston | Decision Theory for Design Economics[END_REF]:

∏ ( ) (7)
The utility function have been elicited for each attribute, and the value of u i (x i ) have been defined (by using values in Table 2). Then, the k i are defined, throughout a trade-off comparison among the attributes.

Scaling Factors Elicitation

In this section, the scaling factors for the multiplicative model are defined (k i ).

First, it is necessary to rank the attributes, and then, a value for the scaling factor of the most important criteria is assigned. The other scaling factors are defined by comparing them with an equivalent level of the most important criteria.

Criteria Ranking

The first step is to define the ranking of the each attribute. The criteria have been ranked as follows: The most important criterion is cost. The maximum distance and the maximum load are both equally important and they occupy the second position of the ranking. The easiness to replace the mean of transport is in the fourth position, followed by environmental risk, and by the two pollutant gases (NO x and CO 2 ). The last position belongs to average speed. As specified in the previous sections, The best alternative is the intermodal train, which scores pretty high in the three most important attributes: max distance, max load and average cost. In general, it is possible to notice that the overall utility score of the four trains is slightly higher than the three trucks. This is due to the fact that trains score higher than trucks on "maximum distance", "average cost", and "maximum load". Moreover, trains have a higher score for what concerns the environmental impact's attributes (e.g. NO X and CO 2 emissions are lower for trains than for trucks).

Conclusions

In this paper all the aspects of utility theory have been covered. First, the problem has been introduced, the attributes have been defined, and the utility functions for each attribute have been elicited. Then, the multiplicative aggregation model introduced, indifference points defined, scaling factors calculated, and the K elicited. Finally, the alternatives have been ranked based on the overall utility: the best alternative is the intermodal train and the worst is the general freight truck type A. In general, trains have a higher score compared to trucks. This is due to the fact that trains have a higher utility in the most important attributes, as average cost, max load, max distance, and gas emissions.

Table 1 .

 1 Attribute dataset ([START_REF] Forkenbrock | External Costs of Truck and Rail Freight Transportation[END_REF], except for avg speed, easinees to R/R and env. risk)

				Flexibility (max)			Environmental Impact (max)	Cost (min)
		Max Dist. (miles)	Max Load (tons)	Avg. Speed (mph)	Eas. to R/R	NOX (g/ mile)	CO2 (g/ mile)	Env. Risk	Avg cost p t-m (cents)
	Truck type A	350	7.26	23.68	8.5	2.390 34.88	1	21.17
	Truck type B	750	14.77	27.61	10	1.88	94.62	2	8.94
	Truck type C	1250	15.61	30.41	8	16.12	134	2.50	7.69
	Heavy Unit train	1000	10500	9.62	1	0.257	22	10	1.19
	Mixed Train	500	6300	7.81	3.5	0.322 18.60	7.5	1.20
	Intermodal Train	1750	3360	24.65	5.5	0.603	17	6	2.68
	Double stack	1750	6720	18.42	2	0.400 15.40	7	1.06

Table 2 .

 2 Assessment inputs and outputs for the attributes

		Max Dist. (miles)	Max Load (tons)		Avg. Speed (mph)	Eas. to R/R	NOX (g/ mile)	CO2 (g/ mile)	Env. Risk	Avg. cost p t-m (cents)
					Assessment Inputs
	Worse Payoff	350	7.26		7.81	1	16.12	134	10	21.17
	Certain Equiv.	650	1500		15	4	12	90	7.5	15
	Better Payoff	1750	10500		30.41	10	0.257	15.4	1	1.06
					Assessment Outputs
	Risk Tol.	465.03	2181.97	14.11	6.23	-6.89	-109.77	-4.36	-11.64
	A (2)	1.052	1.008		1.253	1.309	1.111	1.514	1.146	1.216
	B (3)	2.233	1.012		2.178	1.537	0.107	0.447	0.116	0.197
		Max Distance					Max Load
	1.20					1.20
	1.00					1.00
	0.80					0.80
	0.60					0.60
	0.40					0.20 0.40
	0.20					0.00
	0.00					0	2000 4000 6000 8000 10000 12000
	0	500	1000	1500	2000
			Distance					Max Load

All the functions have been built from a risk adverse prospection. In fig.

1

, the utility functions for the eight attributes are reported. It is possible to notice the risk aversion in the concavity of the curves, as follows: Fig. 1. Table

3

. Utiliy Functions

Table 3 .

 3 Attributes ranking

	Criteria	ki	Attribute	Rank
		1	Max Dist. (miles)	2
	Flexibility	2	Max Load (tons)	2
	(maximize)	3	Avg. Speed (mph)	7
		4	Eas. to R/R	3
	Environmental	5	NOX (g/ mile)	5
	Impact	6	CO2 (g/ mile)	6
	(minimize)	7	Env. Risk	4
	Cost (minimize)	8	Avg cost p t-m (cents)	1

Table 4 .

 4 Utility and ranking for each alternative

	Alternative	Utility	Ranking
	Truck type A	0.732657	7
	Truck type B	0.866891	5
	Truck type C	0.815110	6
	Heavy Unit train	0.874521	4
	Mixed Train	0.883914	3
	Intermodal Train	0.958810	1
	Double stack	0.941066	2

the average speed is considered to be so relevant for this problem. Consequently, k i 's are ranked as follows [START_REF] Keeney | An Illustrative Example of the Use of Multiattribute Utility Theory for Water Resource Planning[END_REF]:

(8)

Determination of the Indifference Points

The third step is to determine the scaling factors for all of the attributes. One method is determining the indifference points by comparing the best level of any sub-criterion (x i ) to an equivalent trade-off level of the most important criterion (x 8 ). According to (8), the most important scaling factor is average cost per ton mile, and its value has been assumed equal to the following:

(9)

The trade-offs for the other attributes have been assumed equal to the following:

The trade-offs lead to:

(10)

K elicitation

In order to calculate the value of K, it is necessary to plug-in the values of the k i 's that have been determined by the trade-off comparison, and substitute them in (7), where K is a non-zero solution of the equation ( 7). Among all the roots of the equation, the expert choice is to choose the largest one. The highest non-zero K value of the equation is K = -0.90974.

Aggregation Utility and Results

In this section, the utility function is finally calculated. Using (6), it is possible to plug in the data we have for each alternative, calculate the overall utility for each alternative, and rank them according to it. The result is the following:


Mario Rapaccini
email: mario.rapaccini@unifi.it

Isabella Porcelli
email: isabella.porcelli@unifi.it

How advances of ICT will impact on service systems and on the delivering of product-related services

Keywords: Service Systems, Augmented Reality, telemetry, product-related services, self-service, super-service

Using a multiple case study approach, this paper studies how the boundaries of service systems that provide product-related services are shifting towards self-service and/or super-service configurations, as a consequence of the introduction of modern ICT.

Introduction

Anticipating the age of Internet of Things [START_REF] Atzori | The internet of things: A survey[END_REF] and smart services [START_REF] Allmendinger | Four Strategies for the Age of Smart Services[END_REF] leading manufacturers such as Rolls-Royce, IBM and GE [START_REF] Davies | Moving base into high-value integrated solutions: A value stream approach[END_REF] recently adopted technologies such as telemetry, condition monitoring and other information and communication technologies (ICTs) that entail to the wider paradigm of e-maintenance [START_REF] Levrat | E-maintenance: review and conceptual framework[END_REF], to catch real-time data from their installed base of products [START_REF] Davies | Charting a Path Toward Integrated Solutions[END_REF][START_REF] Lehtonen | Enhancing field-service delivery: the role of information[END_REF]. Captured data can be leveraged to provide customers with more effective life-long support and other product-related services (PRS), such as remote control, optimization, routine maintenance, updates/upgrades, and achieve more competitive advantages [START_REF] Voss | Paradigms of manufacturing strategy re-visited[END_REF]. However, the widespread adoption of these technologies is expected to radically change the ways PRS are delivered, favoring super-service (i.e. providers perform autonomously -in isolation from the customer -tasks previously done by/with the customer) rather than selfservice (i.e. customers perform on her/his own tasks previously done by/with the provider) [START_REF] Campbell | From self-service to super-service: a resource mapping framework for co-creating value by shifting the boundary between provider and customer[END_REF]. Nonetheless the relevance of this subject [START_REF] Johnson | Supply chain management for servitised products: a multi-industry case study[END_REF], there is still paucity of scientific research addressing how the boundaries of service systems will be affected by technological innovation in the next years. This paper aims to fill this gap in the context of industrial goods/equipment. The paper is organized as follows: Section 2 provides a brief literature review on the topic of the study. Section 3 outlines the research methodology, while section 4 synthetized the empirical findings. Implications as well as limitations of the paper are discussed in the last section.

Service Systems

In service systems [START_REF] Roth | Insights into service operations management: a research agenda[END_REF] resources as different as people, information, money and technologies are mutually shared by different entities (i.e. customers, providers) that create (or co-create) value by accessing to these resources [START_REF] Maglio | The service system is the basic abstraction of service science[END_REF]. For instance, in the case of a field-repair, at least two entities interact: on one side, the customer (i.e. product owner/user) opens the door of her/his house/facility and lets the technician access to the broken product; on the other side, the technician provides know-how, efforts, tools and/or materials to fix the product. To determine the value created in the service system, both entities compare the achieved benefits to the sustained costs. The more benefits exceed costs, the more value is perceived in the service system. If value is perceived to be higher than expected, entities are satisfied. Since value is created in the service system as entities share resources and interact to each other, benefits and costs of both sides are strictly intertwined. In the provision of industrial services, costs are mostly related to the consumption of specific resources (e.g. materials, energy, labor time, etc.), but can occur also as a consequence of (mental/physical) efforts sustained by entities during the service provision [START_REF] De Ruyter | The Dynamics of the Service Delivery Process: A Value-Based Approach[END_REF]. For instance: driving/walking to the product site (physical effort), being embarrassed for not being able to provide the requested information to customer support (psychical discomfort); being disturbed by noise/smell/dirt/unpleasant service environments (sensorial discomforts).

On the other side, benefits from the provision of PRS to industrial equipment are manifold [START_REF] Mathieu | Product services: from a service supporting the product to a service supporting the client[END_REF] and include [START_REF] Smith | The three value proposition cycles of equipment-based service[END_REF]: a) ensure minimum disruption when the equipment fails; b) ensure maximum availability of the equipment; c) ensure the capabilities requested to achieve the best/expected outcomes by the use of the equipment. Irrespective of the classification/terminology used by each provider to promote and sell its offering, it emerges that a first type of PRS, namely recovery services, is focused on assuring product recovery with acceptable performances; a second group, namely availability services, is aimed to assure product availability, keeping low disruptions to the customer's processes; lastly, a third category, namely outcome services, are purposed to increase (empower) the capabilities (skills) of the product user so that she/he's not prevented from extracting/realizing the value potentially embedded in artefact. As a result, the inputs supplied by customers and providers into the service systems greatly differ across these classes (see Table 1).

To model in detail each step of value creation or co-creation, we adopted the Process-Chain Network (PCN) visual framework [START_REF] Sampson | Visualizing Service Operations[END_REF]. In PCN, service delivery processes pertain to the regions of direct (i.e. person to person) and surrogate (i.e. person to things) interactions. In the first people interact synchronously and directly (vis-à-vis) or by means of technology mediation (e.g. phone, live chat). In the second, instead, each entity creates value acting on inanimate resources, such as goods, information, etc., as these are provided by other entities. Surrogate interactions from both sides of a service system show that entities can create value in isolation, though using resources supplied by others. As already said, self-services depict situations in which customers are enabled to create value on their own. Conversely, super-service is the case in which providers are allowed to create value on behalf of their customers, acting on their inanimate resources. As technological advances enable new ways of providing PRS, the boundaries between direct and indirect/surrogate interactions, i.e. self-and super-services, may shift. In PCN, technologies that favor super-services and selfservice are called, respectively, relievingand enabling-technologies [START_REF] Sampson | Visualizing Service Operations[END_REF].

Before introducing technological innovation in a service system, its impact should be carefully assessed [START_REF] Lehtonen | Enhancing field-service delivery: the role of information[END_REF]: firstly, identifying existing/potential value expectations of customers; then, modeling the value creation process; finally, investigating how boundary can be shifted due to the adoption of technology. In section 4 we will apply these concepts to the provision of a typical field-repair intervention (i.e. recovery service).

Research Methodology

Since we needed to explore the rationales behind the scouting and the experimentation of new technologies in service systems (our unit of analysis), we adopted a multiple case study approach [START_REF] Voss | Case research in operations management[END_REF][START_REF] Meredith | Building operations management theory through case and field research[END_REF]. Purposive sampling strategy was used to be confident that the selected companies had characteristics pertinent to our aims. Finally, 6 companies carrying out pilot projects and/or introducing ICT in their service systems were selected, whose characteristics are presented in Table 2. For confidentiality reasons, their names have been disguised. Data were collected through in-depth interviews (overall, 15 respondents were interviewed) as well as recurring to secondary sources (e.g. service contracts, balance sheets, etc.). Data from interviews were coded and findings were used to model AS-IS and TO-BE delivery processes. Due to space constraints, only the case of a typical field-service for product repair is presented in the next section.

PRS types

Findings and Discussion

All the data gathered during the interviews were coded and classified in order to identify: a) the type of technology object of the scouting, b) the rationale for its introduction and c) the stage of technology introduction (Table 3). The comparison of similarities and differences among cases led to the identification of the following findings. Finding 1. Two kinds of technology, respectively telemetry and Augmented Reality (AR), emerged as cutting edge technologies that service companies are interested in scouting and/or still introducing. Telemetry includes a set of technologies, such as sensors, SCADA and control systems, secure communication networks, databases, storage systems, etc., that are used to collect data from the field. AR, instead, relates to any technology that, superimposing virtual signs to a real scene, helps to guide its users (e.g. the field technician, the product user) in performing a task through the visualization of the right information in the right place and in real time [START_REF] Porcelli | Technical and Organizational Issues about the Introduction of Augmented Reality in Maintenance and Technical Assistance Services[END_REF]. To recognize what is viewed, retrieve, transmit and anchor the information on the user's view, software (artificial) rather than human agents can be leveraged. The first is the case of Automatic AR systems (AAR), the latter of Mobile Collaborative AR systems (MCAR) [START_REF] Billinghurst | Mobile Collaborative Augmented Reality[END_REF]. Finding 2. Since the collection of field data is central to enable the shift towards service-oriented business models, any company has (or is planning to have) telemetry to offer PRS such as condition monitoring and remote support to their products. Conversely, with respect to the adoption of AR, most of the companies can be considered in pre-implementation stages, since they are trying to evaluate the real benefits and costs of its introduction through pilot projects, surveys and consultancies. The reasons for the delay in AR adoption with respect to telemetry are twofold. On one hand AR is an emerging technology that entered the market recently and that it is still in its development phase. On the other hand AR usually needs data and information to work (especially AAR) so companies that are evaluating its introduction/ introduced it, have already started/completed the introduction project of telemetry system. Finding 3. Rationales that lead companies to scout/introduce new technologies stem from the need to reduce costs, increase productivity or create higher customer value.

In the first cases, innovation is pursued to enable higher internal efficiency and to empower the workforce without sustaining remarkable costs. In the latter case, the trigger is mostly external as managers are willing to increase the value proposition of their integrated offerings. Finding 4. Any innovation that concerns the adoption of both telemetry and AR technology strongly affects the boundaries of service systems, since it enables the provision of new services, rather than new ways of providing the same service, in the form of super-and/or self-service. Due to space limitations, in the following we will only focus on a field-service (repair from faults) and describe how the above technologies are expected to impact on the delivery process. In Fig. 1, the different steps of a "repair from faults" process are presented using the PCN framework. In particular, the left side shows the AS-IS traditional process (i.e. without the adoption of telemetry and AR), while the right side shows the same process as changed by the introduction of such technologies. In the AS-IS scenario, once problem is detected, customer initiates the service process, asking the provider for assistance. Depending on the way through which the customer contacts the provider and on the type of their interactions, this task could be performed by the customer both interacting directly with provider (direct interaction, as in the case of live chats, phone help desks, etc.) or indirectly with its resources (surrogate interaction, as in the case of sending e-mail or posting customer support requests via a web site). Since discussing the ways through which, traditionally, customers may ask for assistance, is out of the scope of the paper, we represent this task with a box that crosses direct and surrogate interactions. Once assistance is requested, the provider takes on the responsibility of the process, defines the action plan, schedules and then executes the field intervention for repairing the product. Even in this case, the level of interaction with the customer can be different; for instance, in certain cases only surrogate interactions may happen (e.g. the provider has free access to the customer facility, thus no direct interactions are required with the customer personnel) while, conversely, in other cases direct and recursive interactions are unavoidable. In this situation, super-services arise the more the adoption of telemetry spreads. In fact, through telemetry definition of action plans, scheduling and execution of interventions could be performed, to a certain extent, without direct interactions with the customer. For example, in case restoration requires only to take remote control of product (e.g. fix software bugs, optimize/administer computing power/disk allocation, release updates, etc.) the boundary of super-services can greatly be extended. For instance, this is the case of 24-hr always-on tele-control and remote maintenance services of company B. With respect to the adoption of AR, its introduction affects more the responsibility of the "repair product" task, since this technology can assist the execution of the intervention. However, three situations emerged from the analysis of the cases. i) companies that are experimenting/introducing AR in their service systems state that the first application of this technology is focused on supporting their own technicians in performing technical tasks on the field. In this case (Fig. 1, right side, case 1), boundary does not shift, since the provider keeps the ownership of the "repair product" task. However, the adoption of AR is expected to lead to several improvements. ii) Some companies showed interest in understanding how and when AR systems could be used to enable value co-creation (see Fig. 1, right side, case 2); MCAR, in fact, can be leveraged to establish easier/more effective communications among remote specialists (service provider's personnel) that provide assistance to product users (customer's personnel). In case customer agrees to take under its responsibility some tasks related to fixing and corrective maintenance, and execute field interventions under the supervision/support of some remote experts, the system boundary shifts towards assisted self-service by means of the introduction of AR as an enabling technology. From this situation both the provider and the customer could benefit. The first, in fact, could reduce the number of experienced technicians that are deployed on the field, and consequently the costs sustained to provide services. The second, instead, whether favored by some incentives (e.g. lower costs of service contracts, lower response time) could enable situations in which repair tasks are performed by unskilled operators, since the missing know-hows could be remotely provided by specialists. iii) A third option concerns the case in which AAR systems automatically guide customers in performing product repair, with no needs to interact with the provider anymore (see Fig. 1, right side, case 3). This is clearly a case in which the customer takes on responsibility to perform a service task previously done by the provider, thus the self-service boundary can be extended. Most of informants report that neither technology nor customers/markets seem sufficiently mature to shift towards self-repair services. Anyway, they did not deny this transition could be implemented in a nearest future, in certain circumstances, such as to replace usermanuals or to provide services in countries where no technicians reside or can be easily sent.

Conclusions

Recent advances of ICTs, and the lowering of their introduction costs, are favoring new (more efficient/effective) ways to deliver PRS. Hence, the service departments of manufacturing companies are carrying out several pilot/implementation projects to assess the benefits of adopting these technologies against the related costs. To this aim, it is essential to understand how technological innovations within service systems may enable self-services and/or super-services. This paper represents a first attempt to generate, through a multiple case-study, insights about service systems' modification and to suggest how to study the impact of ICT in service delivery. This paper has managerial implications as well, since the findings from case-studies can be useful to inform service directors, CIOs, etc., about the impacts expected by investments in new technologies.

Fig. 1 .

 1 Fig. 1. PCN of field-service "repair from fault" process

Table 1 .

 1 Classification of PRS (source: adapted from[START_REF] Smith | The three value proposition cycles of equipment-based service[END_REF]).

Table 2 .

 2 Characteristics of selected companies.

		Product recovery	Product availability	Product outcome
	Examples	Installation, preventive	Inspection, control &	Hot line and help
		and corrective mainte-	troubleshooting, evalua-	desk, training, con-
		nance, provision of	tion of residual lives,	sultancy, technical
		spares and consuma-	definition of maintenance	documentation
		bles, updates and up-	programs
		grades
	Resources	Products (broken, dete-	Mostly information about	Mostly minds (e.g.
	supplied by	riorated, etc.)	product/process status,	knowledge required
	customers			functioning parameters,	to interact, under-
				alerts, etc.	stand, learn, etc.)
	Resources	Products (new, repaired,	Mostly technical skills,	Mostly minds (e.g.
	supplied by	refurbished, etc.), spare	methods and tools	knowledge required
	service pro-	parts,	consumables,		to interact, analyze,
	viders	technical skills and		teach, etc.)
		know how, methods and
		tools

Table 3 .

 3 Cross-case analysis.

	Com-	Technolo-	Innovation rationales	Current stage of introduction
	pany	gy
	A	Augment-	Improvement of diagnostic	Pre-implementation assessment through a
		ed Reality	skills of field technicians	pilot project
	B	Teleme-	Guarantee the business	Implementation of online telemetry as the
		try; Aug-	continuity 24/7; empower	enabler of condition monitoring services;
		mented	technicians	evaluation of AR technology acceptance
		Reality		by field-force through an internal survey
	C	Teleme-	Analyze data and determine	Implementation of telemetry (1300 ma-
		try; Aug-	optimal maintenance poli-	chines monitored); condition monitoring
		mented	cies; improve communica-	services under development; use of AR to
		Reality	tion between remote centers	support installation
			and field technicians
	D	Augment-	Verify the work progress	Use of AR to support installation/repair
		ed Reality	and support technicians in	and control progress of construction site
			troubleshooting tasks
	E	Telemetry Guarantee the business	Large use of telemetry for several ser-
			continuity 24/7; Analyze	vices (e.g. maintenance and control, re-
			data and determine optimal	porting) (approx. 1000 machines moni-
			maintenance policies; get	tored)
			prepared to propose up-
			grade to the customers
	F	Augment-	Lower costs of field inter-	Pre-implementation assessment through a
		ed Reality	vention; improve communi-	pilot project
			cation between remote
			centers and field technicians