The dynamics of institutional and organisational

Int. J. Automotive Technology and Management, Vol. X, No. Y, xxxx

The dynamics of institutional and organisational change in emergent industries: the case of electric vehicles Miguel Vazquez* IEFE, Bocconi University, Milan, Italy and Florence School of Regulation, European University Institute, Florence, Italy Email: [email protected] *Corresponding author

Michelle Hallack Florence School of Regulation, European University Institute, Florence, Italy and Faculty of Economics, Fluminense Federal University, Niteroi, Brazil Email: [email protected]

Yannick Perez RITM, Armand Peugeot Chair (CentraleSupélec-ESSEC Business School), Université Paris-Sud and LGI Centrale Supelec, LGI 3 rue Joliot Curie 91190 Gif sur Yvette, Paris, France Email: [email protected] Abstract: We consider the electric vehicle industry as a complex system within which firms choose among competing organisational architectures and regulatory institutions emerge from the interaction between firms’ choices and rule-makers’ beliefs. The main drivers to change regulatory institutions are the ‘evaluative criteria’ applied to outcomes. Evaluative criteria are rule-makers’ simplified models against which outcomes are evaluated. We look at the emergence of dominant organisational structures, and point at the importance of the institutional design in such process. In particular, we analyse the interaction between policy choices: we consider policy makers that have two main dimensions upon which to act: they may facilitate cooperative strategies, or they may implement demand-side measures. Copyright © 20XX Inderscience Enterprises Ltd.

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M. Vazquez et al. Keywords: co-evolution; institutional evolution; path-dependence; electric vehicles. Reference to this paper should be made as follows: Vazquez, M., Hallack, M. and Perez, Y. (xxxx) ‘The dynamics of institutional and organisational change in emergent industries: the case of electric vehicles’, Int. J. Automotive Technology and Management, Vol. X, No. Y, pp.xxx–xxx. Biographical notes: Miguel Vazquez is Visiting Scholar at the George Washington University. He is also research Fellow at IEFE, Bocconi, Italy and Research Associate to CERI, FGV – Brazil. Since 2013, he is the Market Designer Advisor for the Florence School of Regulation. He holds a PhD in Industrial Engineering from the Universidad Pontificia Comillas, Madrid and an Industrial Engineering degree from the Universidad Politécnica de Madrid. Michelle Hallack is currently a Professor of Economics at the Fluminense Federal University of Rio de Janeiro (Brazil). Since 2013, she is the Advisor of the Florence School of Regulation for Energy Policy. She holds a PhD from the University of Paris Sud XI of Economics, she also holds an MRes from Federal University of Rio de Janeiro, an European Master Diploma (EMIN) and a Diploma in Economics Sciences of the State University of Campinas. Yannick Perez obtained his Master’s and PhD in Economics at the University La Sorbonne in France. He became an Assistant Professor at the University de Cergy in 2000–2003 and Tenured Associate Professor of Economics at the University Paris-Sud 11 since 2003. At University Paris-Sud, he is the Academic Coordinator of the European Master Erasmus Mundus in Economics and Management of Network Industries. Since October 2008 he is a Chief Economic Advisor of the Loyola de Palacio Chair on European Energy Policy. Since 2009, he is a member of the Scientific Committee of the European Energy Market Conference: Leuven (2009), Comillas (2010), Zagreb (2011), Stockholm (2013) and Krakow (2014). Since September 2011, he is also an Associated Professor of Economics in Supélec, France. Since February 2012, he joined the Armand Peugeot Research Chair on Electromobility as an Associated Researcher. This paper is a revised and expanded version of a paper entitled ‘Coevolution of institutions and technology: the case of electric vehicles’, presented at [name, location and date of conference].

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Introduction

One of the questions that the evolution of the electric vehicle industry poses is which kind of dominant organisational design will be adopted. As in many other industries in an ‘emergent phase’, several organisational structures compete for survival. This kind of phenomenon is not new in the industrial economics literature, and has been identified by several theories of firm growth as one of the most important drivers for industry dynamics (Penrose, 1995; Nelson and Winter, 1982; Teece et al., 1997). In this paper, we will consider the application of this problem to the electric vehicle industry, which is an industry where no dominant organisational design exists yet. This means that firms must choose among competing architectures. We will cast that problem

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The dynamics of institutional and organisational change

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in terms of the modularity vs. integrality choice. After deciding their design, they act as innovators in a fluid industry. On the other hand, the level of detail with which institutions are represented in evolutionary studies is still moderate. To complement the existing literature, we consider that regulatory institutions emerge from the interaction between firms’ organisational choices and rule-makers’ beliefs. To that end, we represent industry as a complex adaptive system, where the interaction among all players is the system from which rules emerge (those rules, in contrast, will likely be relatively simple). In order to understand the main elements that define the previous process, we use the institutional analysis and development (IAD) framework, (Ostrom, 2009). In the IAD, the main drivers to change rules (in our case, regulatory institutions) are the ‘evaluative criteria’ applied to outcomes. We connect the idea of evaluative criteria to rule-makers’ beliefs in order to define how regulation change. Differently put, the main driver for regulatory change will be the evaluative criteria, i.e., rule-maker’s simplified model against which outcomes are evaluated. In this paper, we investigate whether institutions, and the adaptation process of such institutions, affect the choice of organisational structure in emergent industries. It is organised as follows. After this introduction, we set the theoretical context of our study in Section 2. In Section 3, we apply the previous context to the electric vehicle industry. In Section 3.3, we add the institutional dimension to the problem, and in Section 4, we propose a methodology to study the co-evolution of institutions and technology in the electric vehicle industry. Last section collects our final remarks.

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The theoretical context

In this paper, we are concerned with the evolution of rules. We use the definition of rules provided in Crawford and Ostrom (1995): rules are prescriptions of what players involved ‘must’ do, ‘must not’ do, or ‘may’ do, and the associated sanctions in case rules are not followed. One particular case of those rules is the regulatory framework. Our aim will be the analysis of the fundamental elements of the dynamic process defining changes in the regulation of the electricity sector. In this regard, we consider that rules are not, in general, the result of a static, rational decision-making process, but they are emergent properties of the complex interaction between rule-makers and industries.1 From this paper’s point of view, a key point of our representation is considering bounded rationality in the process of making rules. In our analysis, rule-makers do not decide using deductive, rational reasoning but they use instead inductive reasoning, (Arthur, 1994). Specifically, we represent that rule-makers, in a context of significant complexity, understand reality through simplified models that are then used to perform deductions. Such deductions may be interpreted as beliefs. Rule-makers also obtain feedback from the complex environment, which allows them to modify decisions according to their beliefs (their simplified models). This representation can be understood in the context of Simon (1959): rule-makers follow ‘satisficing’ routines, i.e., they will only change routines in case outcomes are no longer satisfactory. Furthermore, it is relevant to understand the environment from which rule-makers receive feedback. Differently put, rules shape technology, but also technology shapes rules. Consequently, we will consider that rules emerge from the interaction of industry

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choices and policy-makers’ beliefs. Hence, we need to describe in some detail industry choices. In order to understand the joint dynamics of rules and industry decisions, we consider two basic situations: situations aimed at conflict resolution and situations concerned with the coordination of resources. On the one hand, a large part of the study of governance is concerned with the idea of efficient coordination, where governance is introduced in order to align incentives and deal with conflict. That can be represented, in game-theoretic terms, by ‘commons’ dilemmas’, (Hardin, 1968), or equivalently, by ‘prisoners’ dilemmas’, as pointed out by as pointed out by Ostrom et al. (1994). Nonetheless, as highlighted in Langlois and Robertson (2002), another critical function to be performed is the coordination of resources, not only of incentives. To perform those functions, players (business institutions, frequently firms) create a set of productive routines, which constitutes their capabilities. From that point of view, the kind of game that describes the previous decision-making process is the ‘coordination game’: games where there is no better solution, but if players do not act jointly, both of them lose. These two types of problems will be termed ‘conflict’ situations (commons’ dilemmas) and ‘coordination’ situations (coordination games). These two kinds of situations do not evolve independently. One of the main insights provided in Künneke (2008) is to identify the relevance that two sets of multi-level group of activities are coherent: the first classification concerns institutional levels as identified by Williamson (1998). The other classification concerns technology practice, and it is defined by Künneke (2008) building on Dosi (1982). The two sets of levels should be coherent. In order to build the theoretical context of this paper, we may identify the first kind of problem (‘conflict’ situations) with the ones primarily studied in the context of institutional economics. The second kind of problem (‘coordination’ situations) is studied in depth within the literature on technological practice. The purpose of this paper is to combine both streams of literature in order to understand the joint evolution of institutions and technology. To that end, we will look at a framework where all those kinds of games can be analysed jointly. Moreover, both the institutional and the technological dimension can be described by different levels of analysis, (Künneke, 2008), so we will need to understand the interaction between the different levels. Consequently, we will use the framework defined by Ostrom (2009). Table 1

Relationship between action situations and institutional and technological levels

Situation type Operational-level situations Collective-choice situations Constitutional-level situations Metaconstitutional-level situations

Institutional level

Technological level

Resource allocation

Operation management

Governance

Routines

Institutional environment

Technological trajectory

Embeddedness

Technological paradigm

Source: Own elaboration, based on Ostrom (2009), Williamson (1998) and Künneke (2008).

In the first column of Table 1, we represent the different levels of action situations, as defined by Ostrom (2009). The basic idea behind an action situation is very close to the definition of transaction in Williamson (1998). Together with the rules of the game, which can be thought of as the structure of the action situation, they form an action arena.


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This general framework can describe both the institutional levels developed in Williamson (1998) and the technological levels developed in Künneke (2008). The correspondence is represented in Table 1. One of the most important insights provided by Ostrom (2009) is that, even if the decision-making process of the four situation levels is nested (e.g., decisions at the operational level are framed by decisions at the collective-choice level), level-shifting strategies are crucial to understand the evolution of institutions. In this paper, we will include in this interpretation the importance of level-shifting to understand the evolution of technologies. From this paper point of view, a player will be choosing level-shifting strategies when she begins to consider the change of any of the constraints in the operational level. Our setting, hence, can be described as follows: we consider players, who will be equipment manufacturers (as described in the next section), deciding at the operational level. Such action arena will be defined by an institutional level [as defined by Williamson (1998)] and a technological level [as defined by Künneke (2008)]. That operational level is structured by the decisions taken at the collective-choice level (governance, for the institutional dimension; and routines, for the technological dimension). More importantly, players can engage in level-shifting strategies (either to change the institutional dimension or the technological dimension). The purpose of our study is to show that the level-shifting strategies at both levels must be taken into account in order to understand the evolution of technology and institutions in the electric vehicle industry. In particular, our problem can be described as follows. In the nested view of the multi-level representation of industry dynamics, one normally defines the rules for the industry (its regulatory institutions) according to the current technological trajectory. After that, firms make their organisational choices. We are interested in situations where there is uncertainty in the technological trajectory (what may be called an emergent industry). In that situation, regulatory institutions are defined according to rule-makers beliefs. The observation of resulting outcomes should update such regulatory institutions. The description of the decision-making process associated with the choice of architecture may be motivated from the idea of modularity. Modularity can be thought of as “a very general set of principles to manage complexity”, (Langlois, 2002). The concept of modularity has traditionally been a building block of the description of complex systems in behavioural science (Simon, 1962). This section has two main objectives. The first one is to understand the drivers for the choice of architecture and its relevance in terms of technological change including the institutional dimension, as showed in Section 2. The second objective is to show the application of this analytical framework to the electric vehicle industry.

2.1 Choice of architecture, institutions and level-shifting strategies Let us begin the reasoning by considering the extreme case of perfect modularisation with no need for organisations. The reasons used to justify not observing such perfect modularisation – the unlimited use of anonymous markets – are varied. Alchian and Demsetz (1972) pointed at externalities associated with production as a motivation for non-modularisation. The argument condensed the complementary views of residual rights to choose (Grossman and Hart, 1986), and of rights to residual income (Foss and Foss, 1995).

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An alternative explanation, which is relevant from this paper’s point of view, is that ownership is not only a way to protect oneself from unexpected counterpart’s behaviour, e.g., (Williamson, 1985), but also a driver of structural change (Langlois, 1992a). In particular, vertical integration helps to coordinate efforts to innovate. In order to state the problem precisely, we may use as building block the concept of (economic) capabilities introduced by Richardson (1972): ‘knowledge, experience and skills’. Using this concept, he argued that firms tend to specialise in activities that are characterised by the need for similar capabilities. We may interpret this as modularisation. One important insight of this view is that firms need to coordinate complementary activities, and that such coordination cannot be always done through an incentive (price) system. Moreover, the difficulties of coordination of complementary activities associated with production is even more difficult within a context of innovation, where the required activities are significantly uncertain. On the other hand, modularity is an important dimension in the description of product design. Baldwin and Clark (2000) have stressed the value of modularity in the design of artefacts. The strategy of modularity in product design is close to the one in organisations. The rationale behind it is separating a complex system into discrete simpler pieces, with standard interfaces within a standard architecture. Differently put, a system of tasks (the ones required to produce) may be divided into several ‘modules’, within which interdependency is high but dependency with other modules is limited. The design of organisations is not independent of the design of products. An extreme view of this is developed in Sanchez and Mahoney (1996), who argue that organisational design follows from product design, i.e., modular products are best organised through modular organisations. This is frequently called the mirroring hypothesis, introduced in Henderson and Clark (1990). As pointed out by Bargigli (2005), modularity faces greater challenges when complexity is significant. This is especially important when, in the context of innovation, one applies the concept of modularity to knowledge creation and organisation. Hence, integral architectures would prevail when technology is highly complex. An alternative standpoint is provided in Miraglia (2014). This work proposes that modularity and integrality are two orthogonal dimensions and hence some architectures can be modular and integral at the same time. Our analysis is focused on the fact that firms, when there is a new technology, need to explore alternative architectures in order to choose the one that fits their interests. That is, new technologies create the possibility of designing new products, which in turn may motivate the implementation of new organisations. This problem is not new in the economics of organisations. What has been less explored is the role that institutions play in that process. The typical context for the study of choice of architecture involves the decision of one firm, or of several interrelated firms (Langlois, 1992b). Our contribution builds on the idea of explicitly considering that firms are organisations that decide within an institutional environment, as defined by Williamson (1998). In this sense, rules (including regulatory institutions) affect the choice of architecture. From the point of view of Williamson (1985), rules help in enhancing conflict resolution, and hence modular architectures (the ones relying in relatively more transactions) will benefit from rules that improves conflict resolution.


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In that sense, we consider that firms and rule makers may engage in level-shifting strategies, as defined by Ostrom (2009). In that sense, firms (which are organisations deciding at the collective-choice level) may decide to change the institutional environment if they identify problems with the rules-in-use (note that this typically involves rule makers in the process of institutional change). From this point of view, regulation is not a static set of rules, but it emerges from the complex interaction between firms’ choices and regulatory decision-making. In order to apply the IAD framework, we begin by identifying nested levels in the context of electric vehicles. In this sense, we will apply the ideas of Table 1 to the specific case of electric vehicles.

2.1.1 Interaction #1: constitutional level The first kind of interaction we consider is associated with what was called the constitutional level (both institutional environment and technological trajectory). In it, two types of actors make decisions. On the one hand, rule-makers decide on the regulatory framework. This includes, among other decisions, the design of the way in which electric vehicles can participate in electricity markets, or the design and implementation of support mechanisms for electro-mobility. In addition, it includes possible dedicated financial lines. On the other hand, firms make decisions regarding technological choices. R&D activities and the development of capabilities related to electric vehicle technologies are made at this stage. This involves whether to engage in the development of new batteries technology or whether to specialise in one specific model (as Tesla’s high-end vehicles). Moreover, firms also decide at this level the architecture of the firm (level of outsourcing of both production and innovation process).

2.1.2 Interaction # 2: collective-choice level The second situation that we consider in this paper takes place at the collective-choice level. At this level, rule-makers decide on two basic dimensions: they manage implemented support mechanisms, and they set regulated prices for network services in the electricity market. These decisions can be seen as the role as counterpart for market players that was defined at the constitutional level. From the vehicle’s point of view, these actions are relevant since they affect the profits to be obtained by the use of the car, which is ultimately represented by the total cost of ownership, see Kempton et al. (2014). On the other hand, firms make their contracting decisions. This implies manufacturers selling cars to customers, but also purchasing the required input for manufacture. At the lower level, the operational level, firms and rule-makers decide how to implement decisions made at the upper level. That typically implies monitoring and regulated contract management for rule-makers, and production and consumption management for firms. This is represented in Figure 1.

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Figure 1

Application of the traditional nested levels to the analysis of electric vehicle industries (see online version for colours) Car makers’ decisions

• • • •

Strategies to develop new technologies Level of technological specialization Architecture ...

Rule makers’ decisions

• • • •

Design of electricity market rules Dedicated financial lines Support mechanisms ...

Firms’ decisions


Firms’ decisions Rule makers’ decisions

Figure 2

• Decide purchase agreements • Sales strategies • ... • Manage support mechanisms • Set regulated electricity prices • ...

• Production management • Contract management

Representation of the change dynamics: level shifting strategies in energy systems (see online version for colours) Firms’ decisions

• • • •

Strategies to develop new technologies Level of technological specialization Architecture ...

Constitutional level


Level‐shifting strategy: The change in the rules is endogenous (not associated with some external policy maker)

• • • •

Design of electricity market rules Dedicated financial lines Support mechanisms ...

Firms’ decisions

• Decide purchase agreements • Sales strategies • ...

Collective‐Choice level Rule makers’ decisions

• Manage support mechanisms • Set regulated electricity prices • ...

The logic for our modelling approach is to consider that, after the technological trajectory and the regulatory framework are decided, firms need to coordinate activities to implement the expected strategies. In that view, they may face barriers to develop some


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of those strategies. Those barriers are sometimes associated with the regulatory framework. The previous logic establishes a link between regulatory frameworks and firms’ decisions. However, rule-makers are not capable of foreseeing all aspects of the firms’ decision-making process. Consequently, they also observe outcomes to deduce whether the regulatory framework is appropriate. Hence, both firms and rule-makers are observing outcomes, and they both apply evaluative criteria to understand them. Regardless the criteria that they used, if they deduce from the observation of outcomes that regulatory frameworks or technological trajectories are not adequate, they are able to engage in a process to change the upper level decisions. These decisions were termed level-shifting strategies in the previous subsection. Note that this means that we do not consider some external policy-making process. The next step of our reasoning is to describe in detail architecture choices in the electric vehicle industry and their relationship to innovation processes. Finally, we will discuss the role played by institutions with regard to the choice of architecture.

2.2 Organisations in the automobile industry The main element of our description of the industry is the competition among electric vehicle firms in an emergent industry. In that view, one of its main characteristics is that electric vehicles are at the intersection of two important industries: the automobile industry and the electric industry. We will begin our reasoning considering the traditional business of automobile industries to highlight changes brought by electric vehicles. To that end, we consider two basic dimensions that determine organisational choices. The first dimension has to do with governance. As shown above, governance mechanisms are important objects of study within the new institutional economics. Governance mechanisms are chosen to minimise transaction costs, but the range of available mechanisms might be considerably wide. Ménard (2004) shows properties of hybrid organisations, meaning choices between hierarchies and markets. In the application to the automobile industry, traditionally, original equipment manufacturers produce based on a network of external suppliers (which can be seen as a significantly modular organisation). The modes of governance chosen for each transaction varies (see Bensaou, 1999) for a traditional review of carmaker-supplier relationships. The basic characteristic of this literature is considering the effects derived from the fact that suppliers control a proprietary technology and hence carmakers are captive customers. Under these hypotheses, the problem is one of asset specificity and the design of mechanisms to deal with the associated conflict. The second dimension of the problem is related to a different set of relationships: managing learning processes (Aggeri et al., 2009). In this context, one need to define which parts of the production chain should be produced in-house. This effect can be summarised by the work in MacDuffie (2013). The main idea behind this study is to question the benefits of outsourcing. A specific example put forward in MacDuffie (2013) is fiat, which lose specific skills in producing components as a consequence of engineering outsourcing. Losing these skills eventually meant poor capabilities in managing the interdependencies among components. Zirpoli and Becker (2011) show the limits of outsourcing in the automobile industry. They follow the basic logic of the mirroring hypothesis, (Henderson and Clark, 1990): first, if the product is not modular, modular organisations lose the ability to deal with

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interdependencies among components; second, the accumulation of knowledge is limited, understanding the accumulation process in the sense of Dosi et al. (1988). The basic element brought by the development of electric and hybrid vehicles is structural uncertainty, in the sense of Knight (1921). As a consequence, no dominant design has emerged yet. As shown in Chen and Perez (2015), traditional equipment manufacturers are essentially characterised by their reliance on the outsourcing of the supplier of many of the value chain required to produce electric cars. PSA, BMW, among others, are examples of this strategy. Another alternative is to create internally the competencies required for the innovation (see Chen and Perez, 2015). The paradigmatic example of this strategy is Tesla Motors.

2.3 The institutional dimension Institutions may help not only to manage conflicts but also to coordinate activities (resources). The idea is that a larger amount of firms will choose a modular design when the institutional setting is favourable. For instance, a strong policy to develop the electric vehicle industry where governments facilitate discussions and agreements between the parties involved makes the existence of this ‘market for capabilities’ more efficient. Consider the case of France, where this kind of policy exists and had produced the creation of a multi-player coordination setting called VEDECOM2. VEDECOM stands for a pre-competitive research institute including French car manufacturers, tiers-suppliers, and public agencies among other electromobility stakeholders.3 In that situation, engaging in a strategy of developing external capabilities will be less costly than in other country (say Brazil or the USA), where such kind of policy does not exist. Conversely the US government grants Tesla Motors Company with a direct financial help of 5 billion $ to develop it research program about electrical battery. It seems coherent according to our framework that French and US institutions action has taken this two different directions. The puzzle is now to define in which way the correlation works from industry strategy and institution action.

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The modelling approach: dynamics of technological and institutional evolution

The methodological framework of this paper builds on the history-friendly models introduced in Malerba et al. (1999). We will setup a relatively simple agent-based model in order to investigate the effects of policy-makers in the choice of organisational structure in the electric vehicle industry.

3.1 Oligopolistic market representation 3.1.1 Demand dynamics The first step of our modelling approach is the description of demand for electric vehicles. One of the main drivers for the industry dynamics is the fact that there is product differentiation: not all vehicles have the same characteristics and not all consumers value them equally.

The dynamics of institutional and organisational change Figure 3

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Representation of the model’s steps (see online version for colours) Cooperation environment Firms decisions: Cooperate or integrate (including R&D budget)

Policy makers react to results Probability of innovation (Consequence of the creation of knowledge) Oligopoly (Schumpeterian competition) – Production of EV

In general, we denote x = (x1, …, xN) the vector of product characteristics, being N the number of possible characteristics of the product. That is, a certain type of electric vehicle will be defined by N characteristics. For instance, the first dimension may be the price, the second one may be the distance that it is capable of doing before charging, etc. We will also assume that there is a specific demand for each product characteristic, along the lines of Lancaster (1966). In that view, we model an aggregated demand for each type of vehicle as an aggregate demand function defined by a ‘merit order’. That is, let us denote Mj the utility of the electric vehicle type j, its ‘merit’. We consider, in general, j = 1, …, J types of vehicles. Thus, its merit will be defined by a utility function for each product characteristic:

M dj

co x1j

x1min

c1d

xNj

xNmin

cNd

where co is a scale parameter, ximin are minimum requirements for each characteristic so if it is not met the utility is zero (we assume them equal for all vehicles types), and cid are the elasticities associated to each market segment d = 1, …, D. The strategy we pursue in this paper is to assume that M dj can be used as a proxy of the total amount of type j vehicles bought by the market segment d. As shown in Malerba et al. (1999), if j is the only type of vehicle sold in the market that fulfils the thresholds, each segment will buy M dj items. However, if there are competing types, variables such as advertising and market share (bandwagon effects) will play a role. To model that effect, we define the probability of a particular type of vehicle to be sold in the market. We consider that the probability of a vehicle being sold depends on, besides its merit, seller’s previous sales and advertising expenditures. Hence, we define the probability of a vehicle j being sold, φj as: φdj

0

M dj

m

Sj

S0

s

Aj

A0

where Sj and Aj are previous sales and advertising investments, respectively, of vehicle j. S0 and A0 are minimum values to ensure that vehicles with no prior sales or advertising can access the market. 0 is a constant to ensure the probability is between zero and one,

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and m, s, measure the relative importance of each term. Therefore, market segment d will buy M dj vehicles of type j, with probability φdj .

3.1.2 Firms’ market decisions The basic problem for firms in the short run is to make pricing decisions. Firms in our model will produce only one type of vehicle, and we assume that all firms produce different types of vehicles. We simplify the problem and assume that they set a constant mark-up μ over their production cost kj, so the vehicle price is pj = kj(1 + μ). Production costs will evolve over time as consequence of technological progress, so the model represents the evolution of costs as a result of firms’ learning. Besides, the first 1 . That is, if the car is cheaper, component of the vehicle characteristics will be x1j pj it will sell more units, all other characteristics being equal.

3.1.3 Market clearing The sequence for the determination of vehicle sales is the following: as a result of the innovation process, at each period t, the j vehicle characteristics, {x1j , ..., xNj }, are set; with that, we check the subset j of vehicles that fulfil the minimum requirements of all characteristics we calculate the amount of cars to be sold to each segment M dj and the probability of selling those vehicles φdj we compute each firm’s profits as L j

d

φdj , M dj , k j , μ.

3.2 Technological evolution and architecture design This section differentiates innovation processes that take place within a range of architectures. The logic for this was developed in Section 2.2, when we described the outsourcing decision in the traditional automobile industry. In this sense, we do not consider different firms’ strategies as the ones studied in Chanaron and Teske (2007) – being firms more or less reluctant to develop electric vehicles. We internalise those effects in the learning process.

3.2.1 Innovation under integrated organisations We will define technological evolution by the probability of an innovation to occur. As in our definition of demand behaviour, we assume that the probability of innovation refers to each product characteristic. Thus, we will denote γij the probability of an innovation in the ith dimension of electric vehicle of type j. That probability will be defined by three components: investment in R&D programs; time during which the firm has been developing the technology; and distance to the technological limits. So let us define: γij

0

Ri j

r

Ci j

c

Ti T0

t

Li

xi

l


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where Ti and Li – xi are the time of technology development and distance to technological limits. T0 is a minimum value to ensure that vehicles with no prior technology development have a positive probability of innovation. 0 is a constant to ensure the probability is between zero and one. Therefore, vehicles of type j innovate with probability γij . From the standpoint of the reasoning developed in Section 2.2, integral architectures can control more efficiently interdependencies among the modules that make up the vehicles. This means that they will tend to favour steepest changes in technology, but probably with lower probability.

3.2.2 Innovation under modular organisations The main element of our description is the probability of establishing successful cooperation: γij

0

1 γ

r

coop

c

conflict

The probability defines incremental improvements along the cost dimension. Successful cooperation will be governed by its own evolutionary process, which in turn is characterised by its significant complexity and technological uncertainty. There are several characteristics that may preclude cooperation, e.g., when the intensity of competition in the oligopolistic market is large enough, collaboration networks will not survive; besides, when the industry is close to saturation, cooperation is less probable (as slight changes result in large profits). Consequently, firms face the trade-off that conflict may appear. Such conflicts cannot be dealt with by contracts, because the uncertain nature of the innovation process precludes contract specification. We model such risk with the probability γconflict. The dynamics of cooperation can be summarised as follows: at each point in time, one firm decides whether to engage in a collaborative strategy or not. The decision depends on the comparison between the increased innovation probability after the cooperation and the probability of a conflict. In this sense, modular architectures tend to favour incremental innovation, probably with higher probabilities.

3.3 Modelling institutional effects Frequently, models of technology dynamics assume that, after clearing the short-term market, the next players to decide are again firms, which decide on R&D expenditures and possibly other longer-term decisions, e.g., advertising expenditures. In this paper, we add a step where rule makers may change the institutional setting, so that they affect the most adapted firm organisation. In other words, the change the rules of the game, so the best firm organisation may change and they will need to adapt. In particular, we consider that the institutional setting may facilitate the establishment of continued relationships between different experts. To that end, policy-makers need to create the appropriate institutions.

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At the beginning of the simulation, policy-makers establish a relatively low level of cooperation-enhancing institutions. After firms decide and evolve, policy-makers observe results. If the amount of electric vehicles sold is below their threshold, they decide to act. As described in Kempton et al. (2014), different public policies can be chosen to foster the development of electromobility. Some countries like Norway has chosen to have a demand side policy that include VAT tax exemption, access to bus lines and free highway toll. Others like USA or France goes for purchase policies in favour of rather small EVs by using a lump-sum help of 6,300€ in addition of the aforementioned coordination of research activities in VEDECOM. Lastly, some countries like Brazil or Germany take no action to develop electromobility. In these cases, some degree of path dependency toward local thermal cars tradition (ethanol choice in Brazil and strong German OEMs positions) can explain the relative underinvestment. Consequently, we model the effects of institutions through two elementary options: demand-side measures, which are represented by an increase number of vehicles sold (we assume that all vehicles benefit from this measure) setting-up cooperation enhancing institutions, which we model by a reduced prospect of conflict. Hence, we simplify rule makers’ behaviour in order to focus the analysis on two possible ways of action. On the one hand, if they decide to apply demand-side measures, the effects as perceived by market players is an increase in sales, and hence in the budget available to invest for the next period. Consequently, as increased profits increase the budget that can be devoted to R&D activities, demand-side measures will help innovation processes that are internal to the firm. On the other hand, cooperation-enhancing institutions facilitate innovation processes that are external to the firm. In this sense, the choice of rule makers may affect innovation processes in the industry.

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The simulation

We pursue a two-step strategy. We begin by testing whether our model is able to reproduce stylised features of the co-evolution between rules and architecture choices. The number of agents in all simulations is 12. We then conduct an experiment aimed at studying how rules evolve under the system description developed in this paper.

4.1 Model calibration The first step of our modelling approach is the calibration of the model: we test whether the model is able to reproduce simple expected features of the situation that we are modelling. From the reasoning above, one of the basic features of this paper’s model is that modular architectures will be more frequent when policy-makers favour cooperation. The rationale behind this is that, when the perception of conflict is low, modular architectures are more probable. As policy-makers can affect the perception of conflict, their actions can increase the number of modular architectures chosen by market players.


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In order to show this, we begin by showing that the perception of conflict affects the choice of architecture. As shown in Section 3.2.2, γconflict represents the probability that conflict arises after establishing a cooperative initiative. We begin by considering that this parameter is exogenous and fixed, in the sense that cannot be affected neither by market players nor by rule-makers. In this context, the decision might be viewed as a traditional case of modular-vs.-integral architectures. As shown before, if the prospect of conflict is high, modular architectures are less attractive (especially in terms of outsourcing innovation processes). This stylised features is shown in Figure 4: the choice of modular architectures is affected by the estimated probability of conflict. Figure 4

Number of modular designs as a function of probability of conflict (see online version for colours)

10 9 8 7 6 5 4 3 2 1 0 1

4

7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Conflict Parameter = 1

Conflict Parameter = 5

Conflict Parameter = 9

Note: The X-axis represents the number of periods since the starting point of the simulation (cumulated time).

Figure 4 shows in the vertical axis, the number of firms that choose modular designs (measured as the number of cooperative initiatives observed each period). In the horizontal axis we represent each period of the simulation. The three curves represent the market players’ behaviour under three values for the exogenous parameter ‘expectation of conflict’, γconflict (note that it is not a probability). We observe that the larger the conflict parameter, the larger the trend to choose integral architectures. As shown in Figure 4, the green curve represents the higher probability of choosing cooperation when conflict is less expected. On the contrary, the yellow curve shows the preference for integral architectures when conflict is expected. The second step in the model’s calibration is to test whether our model is able to represent policy adaptation and the corresponding increase of modular designs. Differently put, we aim to test whether policy adaptation affects decisions on architecture. In this case, rule-makers react in order to decrease the perception of conflict (represented by the parameter γconflict). That is, they act permanently trying to reduce conflict, but their ability to do so is limited. In particular, they are able to reduce the conflict parameter 30% each time cooperation is too low (less than 30% of market players) or the conflict parameter is too high (equivalent probability higher than 20%). According to our model, policy adaptation increases modular architectures as a result of decreased probability of conflict. Figure 5 shows our model’s results. The three curves represented in Figure 5 correspond to the same conflict parameters as in Figure 4. The

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green curve is the one corresponding to the lowest conflict parameter and the yellow one is the one corresponding to the largest conflict parameter. However, we observe in this case a significantly higher amount of collaborations in the industry (as measured by the number of collaborations represented in the vertical axis). Figure 5

Increased modular designs as a result of policy adaptation (see online version for colours) 14 12 10 8 6 4 2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

0

Conflict Param = 1

Conflict Param = 5

Conflict Param = 9


Our last test has to do with the control of rule-makers’ adaptation process. That is, according to the previous reasoning, rule makers intervene when the prospect of conflict is significant, so we need to consider how rule makers evaluate the prospect of conflict. In that sense, we investigate the effects of varying evaluations of ‘high conflict situations’. We model such situations as rule makers expecting to observe a certain level of modular designs. This would be a consequence, rule makers think, of a relatively favourable environment for collaboration among firms. For instance, if electricity distribution companies cooperate with equipment manufacturers, there is less need to develop solutions to charging problems. If the expected amount of modular designs is lower than expected, rule makers would act in order to reduce the probability of conflict (as in the case of VEDECOM). As the focus of this test is rule-makers adaptation, we do not assume a static, perfectly rational behaviour in this regard. Rather, we are interested in testing whether rule makers that are prone to adapt result in a lower prospect of conflict, as implied by the reasoning developed in this paper. We model this idea by defining a probability with which rule makers observe the industry with the aim of engaging in level shifting strategies, i.e., correcting maladapted rules. We called that probability ‘trigger level’, as it triggers the rule-makers adaptation. If they observe the industry, they will act according to the following rationale: if the observed amount of modular designs is lower than expected, rule makers will act in order to reduce the prospect of conflict. Note that this is not the optimal amount of modular design, but the rule-makers’ belief on the correct amount of modular designs.


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That is, low trigger levels mean that rule makers will adapt frequently: the probability of rule makers observing the industry is high. Figure 6 represents different evolutions of conflict parameter values (γconflict), depending on the level of response considered by rule makers. The parameter is represented in the vertical axis. Three curves are represented, each of them representing the percentage of market players that is expected to choose modular designs (expected by rule makers). Figure 6

Probability of conflict as the evaluation criteria change (see online version for colours) 10 9 8 7 6 5 4 3 2 1

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

0

Trigger level = 0

Trigger level = 0.5

Trigger level = 1


As expected, we observe that in the light blue curve, where the trigger is zero, rule makers act in each period, reducing the probability of conflict as much as possible and as quickly as possible. On the other hand, as by the dark blue curve, with high values of trigger level, the adaptation of rule makers is delayed.

4.2 An experiment We turn to the study of emergent properties. Our concern in this experiment is understanding the consequences, from a system perspective, of different rule-makers’ behaviours. From this paper’s point of view, we do not consider directly the effects of different sets of rules (a type of ‘regulatory scenarios’) but different forms of processing feedback obtained from the system. In this sense, we analyse system properties associated with rule-makers’ algorithmic rationality. In terms of the IAD, we study the consequences of different evaluative criteria. Question #1 What is the interaction between policy choices? As defined in Section 3.3, policy makers have two main dimensions upon which to act: they may facilitate cooperative strategies, or they may implement demand-side measures. In Figure 7, we analyse how policy makers act in regard to demand-side measures. The vertical axis represent the (cumulated) number of policy actions perceived as necessary by rule makers. That is, the number of periods where the sales volume was perceived too low and hence demand-side measures were estimated necessary.

18 Figure 7

M. Vazquez et al. Cumulated number of periods when demand-side measures are perceived as necessary (see online version for colours) 90 80 70 60 50 40 30 20 10

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

0

Trigger level = 0

Trigger Level = 0.5

Trigger level = 1


We observe that the dynamics are between the two dimensions of policy action (demand-side and cooperation-enhancing measures) are complementary: the later rule makers adapt to conflict situations, the larger the amount of funding they dedicate to the industry. As a secondary result, we may interpret that the higher the prospect of conflict, the lower the level of car sales. Question #2 How do rule makers’ beliefs affect the innovation process? We finally look at the differences in the associated technological dynamics. Figure 8 shows the dynamics of the performance dimension. For low trigger levels, we observe a smooth evolution whereas, for large trigger levels, we observe more volatile dynamics. That corresponds with the idea that technological development under integrated designs favours steepest changes with relatively lower probability to occur. Besides, Figure 9 shows a very similar pattern for the cost dimension. From this preliminary experiment, we find potential to explore the idea that evaluative criteria may in fact affect the technological path. Depending on policy makers’ expectations, the dynamics of the rules may be significantly different and hence they may affect technological dynamics. These results can be related to the ones developed in Dijk and Kemp (2010) and Dijk (2014).

The dynamics of institutional and organisational change Figure 8

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Evolution of vehicle performance (see online version for colours) 16 14 12 10 8 6 4 2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

0 Trigger Level = 0

Trigger Level = 0.5

Trigger Level = 1

Note: The X-axis represents the number of periods since the starting point of the simulation (cumulated time). Figure 9

Evolution of vehicle cost (see online version for colours) 51 50 49 48 47 46 45 44 43 42

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

41 Trigger Level = 0

Trigger Level = 0.5

Trigger Level = 1


5

Final remarks

In this paper, we have pointed at the importance of representing the interaction between technology and institutions. We propose to look at the electric vehicle industry as a complex, highly interconnected system. In this kind of model, one relevant question is what kind of result can be obtained. In any case, equilibrium is not the default situation and, if equilibria exist, they need to be justified. We propose a methodology that do not rely on equilibrium assumptions, but let equilibrium solutions arise as an outcome of the

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interactions in the industry. To that end, we define a methodology that look at the interaction between firms’ decision-making process and policy-makers’ design of institutions. Using this model, we seek insight in the understanding of organisational dynamics in an emergent industry. In that context, we look at the dynamics of the emergence of dominant organisational structures and point at the importance of the institutional design in such process. As showed by the experiment conducted in this paper, institutional dynamics are nor separated from technological ones and their response cannot be considered to happen after technological development. Moreover, institutional design is not an exogenous process from the viewpoint of technological development. We point out that one main driver for regulatory change is the evaluative criteria, i.e., rule-maker’s simplified model against which outcomes are evaluated. We have shown, in this context, that the emergence of a dominant organisational design may be crucially affected by those criteria, and that the design affects the path of technological evolution. Consequently, rule-makers’ beliefs might determine the technological path chosen by the electric vehicle industry.

Acknowledgements Y. Perez benefit from the support of the Chair ‘PSA Peugeot Citroën Automobile: Hybrid Technologies and Economy of Electromobility, so-called Armand Peugeot Chair’ led by CentraleSupélec and ESSEC Business School and sponsored by PSA Group.

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Notes 1 2 3

Arthur (2014) among others provides a general framework to define emergent properties. http://vedecom.fr/en/. VEDECOM was created as «Institute for the Energy Transition» by the French government. As a French public-private partnership, VEDECOM is dedicated to research and training on carbon-free, sustainable individual mobility. It is based on an unprecedented collaboration between industries of the automotive sector, infrastructure and services operators in the mobility eco-system, academic research institutions, and local communities.