Entry, Exit & Survival

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Entry, Exit & Survival

An Analysis Of The Industry Life Cycle of the

European Wind Turbine Industry

Harm-Jan Stuut

s2442273

Student MSc BA Strategic Innovation Management Supervisor: Florian Noseleit Co-assessor: Pedro De Faria

3-1-2014

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Summary

Increasing environmental concern during the 20th century has moved the research focus from conventional electricity sources to renewable sources. Hansen & Hansen (2007). This article describes the development of the European wind industry from the ILC theory perspective. Peltoniemi (2011) organized empirical ILC works in five themes that dominate the literature and relates to changes in industry structure and innovation, and to the determinants of survival. The five themes that Peltoniemi (2011) describes are entry and exit rates, changes in nature of innovation, survival, entry timing and pre-entry experience. The wind turbine industry is still in the transition phase of the ILC based on the growth in firm numbers. The nature of innovation has a large focus on product innovation because there are many undiscovered areas but process innovations do occur because of the growing competition. According to Lumpkin & Dess (2001) the most successful start-ups are those launched in the growth stages of an industry's life cycle. This seems applicable for the wind turbine industry as well. Pre-entry experience and entry timing seem to influence the survival pattern. Survival in the wind turbine industry is the highest among the diversifying entrants that entered in the transitional stage and firms with pre-entry experience have a high survival rate and especially diversifying firms that build a new production facility. But the most applied entry mode among surviving firms are new firms with new plants. Despite the conflicting findings with statements of Dunne, Roberts & Samuelson (1988) that young firms have the highest failure rates and that diversifying entrants tends to have higher failure rates than new-firm entrants the wind turbine industry is in line with the ILC theory.

Introduction

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manufacture wind turbines was a carpenter called Riisager (Kamp, 2002; Neukirch 2010) who commercialized the breakthrough design Gedser wind turbine by Johannes Juul. At the beginning of the wind turbine industry countries like Denmark, Sweden, Germany the United States had joint research program where different institutions focused on different parts and technologies (Kamp, 2002; Neukirch, 2010). Hence, from the birth of the industry there is a European market.

Wind turbine design is based on many technical options. According to Kamp (2002) the most important technical choices are axis position, position of the rotor, number of blades, blade material, rotor control, generator type, turbine size. The choices that have been made by wind turbine

manufacturers have been based on experience with the technical options and are linked to learning processes to a large extent, (Kamp, 2002). More than 90% of the turbines produced worldwide nowadays are three-bladed, horizontal axis turbines (Kamp, 2002; EWEA, 2009; Hansen & Hansen, 2007).

Kamp (2002) and Gipe (1995) noticed that the development of wind turbines has been heavily subsidized by national governments because of the high expectations of wind energy but also by the European Union. The governments provided R&D subsidies to research institutes and appointed firms for specific R&D projects in order to lower the threshold for investment on the one hand. On the other hand, subsidies are provided to buyers in order to promote the wind turbine in order to create a pull force from the market. Kamp (2002) found that despite higher government subsidies on wind turbines, the Dutch wind turbine industry was less successful than the Danish manufacturers. It seems the main problem in the transition to sustainable energy are the uncertainties in the diffusion and development of the technology (Anderson & Tushman, 2001). This can be applied at the wind turbine industry and paves the way for an analysis of the development of the wind turbine industry. A first attempt to understanding the industry and its development characteristics is analyzing the life cycle of the industry. The industry life cycle (ILC) theory describes the emergence of patterns in the development of industries (Porter, 1980; Dunne, Robert & Samuelsson, 1988; Klepper, 1996; Klepper, 1997). According to Peltoniemi (2011) the theory aims to explain the patterns in changes in technological development and industry structure as the industry ages. To explain the pattern of development in the European wind turbine industry, the following research question will be answered in this paper.

How did the European wind industry develop from the industry life cycle theory perspective? To answer the research question the following sub questions will be used:

 How did entry and exit activity evolve over time?

 What are the changes in the nature of innovation?

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o What impact does pre-entry experience have on survival?

The paper is structured in the following way. At first, the literature about industry life cycle (ILC) theory and the contribution to ILC theory Peltoniemi (2011). He organized empirical industry life cycle works in five themes that dominate the literature. The five themes proposed by Peltoniemi (2011) are the backbone for this research. The themes are entry and exit rates, changes in nature of innovation and survival which can be separated in entry timing and pre-entry experience. In the methodology section of this research can be found how the data collection was conducted. After that, there will be explained how each of the five themes of Peltoniemi (2011) is measured. Followed by the results and conclusion by the answering of the research question, the discussion and possibilities for further research.

Literature

This paragraph will discuss the literature involved within the field of ILC theory. The five themes of Peltoniemi will be discussed and an additional insight on the wind turbine industry by Neukirch (2010) on the pioneering stage of the wind turbine industry.

Industry Life Cycle Theory

Young industries are characterized by turbulence due to frequent entries and exits (Malerba & Orsenigo, 1996). Beesley & Hamilton (1984) found that the turbulence created by the rate of entries and exits of firms within an industry is important to determine the competitive situation of an industry. Castrogiovanni (2002) found that dynamism is greater in new industries than in established industries and concluded that dynamism of industries decreases over the course of time. Hauschild, Zu

Knyphausen-Aufseß & Rahmel (2011, p.245) define industry dynamics as the frequency, the magnitude, and the irregularity of changes of customer preferences, of changes in the competitive situation and of technological changes during a certain time span and within the boundaries of an industry. Abernathy (1978) and Abernathy & Utterback (1978) tried to theorize changes in

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numbers through technological developments consisting of decreasing product variety and emerging scale economies (Peltoniemi, 2011).

Literature provides industry life cycle models with multiple stages (Abernathy & Utterback, 1978; Klepper, 1996; Gort & Klepper ,1982; Klepper & Graddy; 1990; Agarwal & Gort, 1996 and show that as a new industry evolves from birth to maturity, price falls, quantity rises and the number of ﬁrms initially rises and then falls. Which shows similarities with the patterns of Klepper (1996). Klepper (1996) mentions two patterns concerning the nature of industry evolution in technologically progressive industries. First, at the beginning of the industry, the number of entrants may rise over time or it may attain a peak at the start of the industry and then decline over time, but in both cases the number of entrants eventually becomes small (Nelson, 1994; Utterback &Suarez, 1993). Which is also described in the transition phase later on. The outcome of the competition is highly uncertain both in terms of which alternative will be the winner (Nelson, 1994) and in terms of industry leadership. Innovators compete as much against market skepticism as against rivals (Utterback, 1994). The population of innovators changes substantially over time, because most innovative entrants are occasional innovators, and only some become persistent innovators (Malerba & Orsenigo, 1996). The growth stage usually is characterized by high levels of heterogeneity between firms, such as

unstandardized products, high product variation, and market share instability (Mazzucato & Semmler, 1999). The number of producers grows initially and then reaches a peak, after which it declines steadily despite continued growth in industry output. The second pattern of Klepper (1996) is

characterized by market growth (Utterback, 1994), fewer new entrants (Utterback & Suarez, 1993) and a shakeout of firms. Eventually the rate of change declines and the leadership of the industry stabilizes Klepper (1996).

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Because of the growth in demand and new users the number of firms increases, then drops as entry decreases and exits increase (Abernathy & Utterback, 1978). Klepper (1997) states that innovation shifts from a product focus toward a process focus because efficient operations become essential to survival but product innovation has to be within the confines of the dominant design. Market growth slows towards the end of the transition phase, leading to a shakeout over time (Abernathy &

Utterback, 1978).The mature phase sets in when industry growth stalls or slows dramatically indicating that only the most efficient and best positioned firms survive (Afuah & Utterback, 1997). Market shares stabilize as the established firms occupy well specified competitive positions and barriers to entry increase (McGahan, Argyres et al. 2004). The nature of innovation shifts towards incremental in both product and process, with a focus on productivity improvement (Klepper 1997; McGahan, Argyres et al. 2004). Customer preferences are stable and finally detailed regarding the product architecture (McGahan, Argyres et al. 2004). The industry enters the final stages of its life cycle when opportunities for further cost reduction become exhausted and demand stagnates. The decline phase sets in and organizational mortality therefore increases, causing a net decline in the number of firms in the industry (Argyres & Bigelow, 2007).

A context specific approach is made by Neukirch (2010). He describes four periods in the pioneering phase of the wind turbine industry from a Danish perspective which shows similarities with the first pattern of Klepper (1996) and the fluid phase and the maturity phase partly. The pioneering phase, according to Neukirch (2010) lasts from 1975 until 1991. During the period of technical innovation (Period 1: 1975-1978 ) production-ready wind turbine concepts are developed for the first time. The next period is one of market stabilization (Period 2: 1979-1982). At this time most producers to small craft couldn’t meet the increasing demand. Subsequently, there was among the manufacturers a generational change. A shift from self-builders to large manufacturers took place. In the testing period (Period 3: 1983-1987) is analyzed what the conditions where under which Danish technology was successfully introduced on a large scale. It was still questionable whether the wind has been recognized internationally as a proven technology in 1987. The problem started when the export market collapsed after the expiry of the Tax Credits and Danish producers, who had expanded their production massively, now plunged into a serious crisis. There was the danger of the collapse of the entire industry. The fourth period is the period of international stabilization (Period 4: 1988-1991) where technology had attained a good degree of reliability, but also the fundamental problems of legislative and administrative nature have been mastered.

Five themes by Peltoniemi

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quantitatively as an industry ages, but there is relatively little research examining whether and how entering and exiting firms are qualitatively different at different stages of industry evolution. Peltoniemi (2011) organized empirical ILC works in five themes that dominate the literature. He relates to changes in industry structure and innovation, and to the determinants of survival. The five themes are entry and exit rates, changes in nature of innovation, survival, entry timing and pre-entry experience. They are discussed hereafter per theme.

Entry, exit and M&A

Klepper (1997) provides evidence on entry, exit, firm survival, innovation and firm structure in new industries and assessed whether industries proceed through regular cycles as they age. According to Mamede (2009) changes in the competitive structure of an industry can be captured by the

frequency of entries and exits of firms and Jauch & Kraft (1986), Nadkarni & Narayanan, (2007) state that the collective actions of competitors can be measured by the rate of entries, exits, restructurings and merger; in other words: frequency of competitor change. Klepper (1997) provides evidence on entry, exit, firm survival, innovation and firm structure in new industries and assessed

whether industries proceed through regular cycles as they age. Entry and exits rates are industry specific, can involve both technological change and scale economies, and are a causal factor in all competitive industries that the ILC is based on and therefore is a useful to understand why the ILC occurs as it does. Firms that are not producing the dominant designs either change over or are about to exit (Klepper, 1997). An acquisition implies both an entry (the acquirer) and an exit (the acquiree).

Changes In Nature Of Innovation

Garg, Walters & Priem (2003) assume that through technological changes, new products emerge, which in turn might change customer preferences, and provoke reactions from competitors. Audretsch (1991) examined a large sample of manufacturing firms and found that new firm survival rates were positively related to their innovative activity. Peltoniemi (2011) summarizes in two themes that emerge in the literature on changes in nature of innovation. Firstly, a product innovation focus

followed by a shift towards process innovation (Agarwal et al., 2002) and periods of where radical and incremental innovation alternate. The shift from product to process innovation takes place once the product shows sufficient performance and producers may concentrate on cost innovation rather than quality innovation (Peltoniemi, 2011).

The Survival Pattern: Entry Timing & Pre-entry Experience

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Entry timing

Does entry timing matter? Klepper (2002) states that timing of entry is random because firms enter the industry when they have developed the capabilities for an industry. But others think differently. According to Dunne, Roberts & Samuelson (1988), small and young firms have the highest failure rates but it doesn’t take the ILC stage in account. Eisenhardt & Schoonhoven (1996) and Sandberg & Hofer (1987) found that empirical support for a higher success rate among growth industry

enterprises where early entrants have higher survival rates than later entrants. Klepper & Simons (2000) argue that company size may be more important than age with regard to explaining survival. According to Lumpkin & Dess (2001) entrepreneurship scholars have consistently argued that the most successful start-ups are those launched in the growth stages of an industry's life cycle and have the advantage of early entrants is explained by cumulative learning, economies of scale in production, and cost spreading of R&D. This might help explain the findings of Suarez & Utterback (1995) because they found that firm survival was strongly influenced by technological change and that the probability of firm survival was greater for firms entering the industry before the emergence of a dominant design than for firms that entered after the emergence of the dominant design. According to Klepper & Simons (1996) the emergence of a dominant design wasn’t the cause of shakeouts

technological industries. They argue that change was a consequence of a broader evolutionary process in which early entrants became leaders in product and process innovation and eventually dominated their industries (Klepper & Simons, 1996, p. 87). The first mover is expected to enhance its market share and can reduce unit cost or introducing a better product. Above all the firm can gain competitive advantage. However, being first can involve high cost and uncertainty (Hoppe, 2000). Agarwal & Gort (2001) question the idea of early-mover advantage because it may be disappearing these days because early entrants do not enjoy the same type of information advantages that they had in the early 1900s. In an era of intense technological activity, entrants enjoy a higher rate of survival, because incumbents suffer from technological obsolescence (Agarwal, 1996). Survival rates mean that the maturing of an industry does not necessarily create a barrier to entry, but it may create a barrier to survival

(Audretsch, 1995). Olleros (1986) argues that pioneers may suffer from ‘burnout’ due to the costs associated with technology-creation and market-creation, an extended payback period and rapid obsolescence of first-generation technology, and the risk of choosing a product design that fails to become dominant.

Pre-entry experience

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chances”(p.2947). Klepper (2002) defined experienced firms that diversify into a new industry,

experienced entrepreneurs, spinoffs and inexperienced firms. Dunne, Roberts & Samuelson (1988) test entry and exit patterns among different industries and examine the relative importance of different types of entrants, the persistence of industry entry and exit patterns over time, the correlation between industry entry and exit rates. Dunne, Roberts & Samuelson (1988) separate entrants into new firms, existing firms that diversify into an industry by opening new production facilities, and existing firms that enter by altering the mix of outputs they produce in their existing plants. Dunne, Roberts & Samuelson (1988) found that new firms and diversifying firms that enter through changes in the mix of products they manufacture in their existing plants are of equal importance in terms of market shares. The group of diversifying entrants tends to have higher failure rates than new-firm entrants (Dunne, Roberts & Samuelson, 1988). They also found that on average, the firms that survive tend to be much larger in subsequent years than the new-firm entrants that survive. Dunne, Roberts & Samuelson (1988) focused on three aspects of the entry and exit process. These aspects where the relative importance of different types of entrants, the correlation of entry and exit patterns across industries and over time, and the post-entry size and exit patterns of the entrants.

Methodology

In this section is discussed how the data collection was conducted and how the five themes will be measured.

Data Collection

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As a starting point for the search of existing wind turbine manufacturers the member lists of the European Wind Energy Association (EWEA), Danish Wind Industry Association (DWIA) and Dutch Wind Energy Association (NWEA) were checked on manufacturers. The EWEA and NWEA did not reply the request for historical data. Only existing members were retrieved from the public site. Dutch Statistics (CBS) and Dutch Chamber of Commerce (Kamer van Koophandel) work with SBI codes to code industries but the data doesn't contain the 5 digit SBI code for wind turbine manufacturers and the record goes back till 1993. Via the World Wind Energy Association (WWEA) the website of The Wind Power1 was found. This professional website contains a database of 146 manufacturers. During the checking of individual manufacturers, a German wind turbine database2 was found with 296 manufacturers and additional information like year of foundation, year of exit and contact information. Starting point for the search for manufacturers from the birth of the industry are the papers of Kamp (2002), Neukirch (2010), Lindley (1980), the book of Gipe (1995), Frey (2010) and from the Danish website Winds of Change3 of a former blade manufacturer in the early stages of the industry.

For all firms listed, there has been checked within former sources and the internet whether companies are, or were, actually wind turbine manufacturers. The year of entry and year of exit, if there had occurred M&A, entry mode have been checked. Enernex (2003) defined two general classes of wind turbines namely utility scale and residential scale. 300 kW To 3 MW turbines for utility scale is used to generate bulk energy the electricity market. The other class of residential scale corresponds with small-scale turbines. However, there is no distinction made between turbine output in this research. The EWEA defines all turbine manufacturers as manufacturers so there is no distinction between classes.

All variables are searched via web search, company web sites, wind (online) magazines Wind Power Monthly4 and Renewable Energy World5, latter mentioned papers and book, and the Journal of Industrial Aerodynamics. If present, multiple sources where added to increase the validity of the dataset. If data was not to find for existing companies an email was sent or the contact form on the company website was filled out with the request for additional information. Two companies replied by email. For new, non-differentiated European manufacturers, year of foundation is the moment of entry in the market. There might be a lag between foundation, prototyping and actual market entrance but this is hard to determine. For differentiating European firms the actual year of entry on the market is used. For manufacturers from outside Europe, the year of entry on the European market is used.

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The database consist of 389 firms. 244 Firms of this set are, or used to be, wind turbine manufacturers and are, or where, active on the European market. But from fourteen of these manufacturers, data on year of entry and/or exit is missing. Two double entries are found and are deleted. Therefore, there is a remaining population of 228 wind turbine manufacturers on the European market in the time span from 1976 until 2013.

Entry, Exit and M&A

Agarwal & Gort (1996) state in their article that entry and exit rates depend on the state of development in the market from birth to majority and this is broadly consistent with empirical

regularities observed in multiple industries. Klepper & Graddy (1990) show that the annual number of firms tends to first rise rapidly, then declines slowly over time. Afuah & Utterback (1997) and Klepper (2002) graphically displays number of firms, entrants and exits. It is expected that changes in the number of firms in an industry occur at times of transition in an industry’s life cycle. Hence: the annual number of manufacturers will follow the ILC curve.

According to Malerba & Orsenigo (1996), entry often occurs through acquisition. Hauschild, Zu Knyphausen-Aufseß & Rahmel (2011) support that and propose that other forms of entries and exits (f.e. insolvencies) are relatively minor compared to the number of M&A. Hauschild, Zu Knyphausen-Aufseß & Rahmel (2011) notice that the number of mergers and acquisitions is an appropriate item to measure the frequency of changes in an industry’s competitive situation. But they recognize that only a number of M&A would be meaningless. The number of M&A can be weighted to the total number of manufacturers. The frequency of changes in the wind turbine industry’s competitive situation occurs through merger and acquisitions. Hence, the average annual sum of mergers and acquisitions divided by the total number of competitors can measure the frequency of changes in the competitive situation.

Changes In Nature Of Innovation

Changes in the nature of innovation are used to explain changes in industry structure (Peltoniemi, 2011). Cohen & Klepper (1996) state that if industries mature, firms tend to stress more incremental and process innovations. According to Utterback (1994) the selection of the dominant design results in a change in the nature of technical change from radical product innovation to process innovation and incremental product innovation. But Peltoniemi (2011) states that it is not known how the shift from product to process innovation is determined and proposes to look into phenomenon at the micro level through qualitative and survey studies that examine, how consumers make sense of technologies, how willingness to pay emerges, and how firms decide the level of ambition in their R&D efforts. But for this research it is too comprehensive to analyze all technical developments in the industry. An

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Survival Pattern

The main interest on survival should be on entry timing, pre-entry experience and innovativeness (Peltoniemi, 2011).

Entry timing

Klepper (2002) states that entry timing is random because firms enter the industry when they have developed the capabilities for an industry. According to others entry timing matters (Lumpkin & Dess, 2001;Eisenhardt & Schoonhoven, 1990; Sandberg & Hofer, 1987; Peltoniemi, 2011) but it should be noticed that this derived from an ex post analysis. To research the entry timing, year of entry and the age are linked to results of the entry and exit analysis on the ILC curve to check whether early entrants are better off than late entrants. The year of entry between surviving and non-surviving firms will be compared. According to Lumpkin & Dess (2001) the most successful start-ups are those launched in the growth stages of an industry's life cycle. Dunne, Roberts & Roberts (1988) reveal that small and young firms have the highest failure rates. The entry timing of the top 10 most successful companies will be compared. Suarez & Utterback (1995) found that firm survival was strongly influenced by technological change and that the probability of firm survival was greater for firms entering the industry before the emergence of a dominant design so it can be expected that the survival rate among firms that entered in period 1 and 2 is higher than firms that entered after the emergence of the dominant design.

Pre-entry experience

For pre-entry experience Klepper (2002) defined experienced firms that diversify into a new industry, experienced entrepreneurs, spinoffs and inexperienced firms. Deciding whether a firm is a spin-off and information on the careers of entrepreneurs is hard to find. Therefore, the variables of Dunne, Roberts & Samuelson (1988) are used. They separate entrants into new firms the construction of a new plant , existing firms that diversify into an industry by opening new production facilities (DF/NP) and existing firms that enter by altering the mix of outputs they produce in their existing plants (DF/PM). These variables where findable in the data collection. Dunne, Roberts & Samuelson (1988) found that new firms and diversifying firms that enter through changes in the mix of outputs they manufacture in their existing plants are of roughly equal initial importance in terms of market shares. Dunne, Roberts & Samuelson (1988) state that their data was particularly suited for their research (p.514).

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Results

The results of the research are presented below per theme.

Entry, Exit and M&A

In Figure 1 the gross entry, exit and M&A rates are displayed. Average entry (including entries due to M&A) over the period from 1976 until 2013 is six firms per year. Outliers are the years 1982 and 2007 with twelve new firms and 2008 with fifteen new firms and 1988 and 1990 with both one new entry. Average number of exit (including exits due to M&A) is over the period 1976 until 2013 is 2.84 firms per year. Shake outs occurred in 1982 (6 firms on 54), 1997(8 firms on 126) and period between 1988 and 1991. 25 Firms on a total of 98 entrants left the industry in those four years’ time.

41 Manufacturers left the industry by M&A. In the population, 32 firms are registered that where acquired by other industry members. Only Hazelwind was acquired by an outsider but the firm went out of business in the year of acquisition. Firms that are acquired, exit the industry because they are part of another firm since that moment. Nine firms were involved in merging activities. Three mergers among industry members and two among an industry member and firms outside the industry. Mergers imply an entry and two exits. This yields for NEG and Micon, merged into NEG-Micon, Bouma and Newinco, merged into Nedwind, and Holec and Windmaster, merged to HMZ Nederland. Volund merged with ASEA and the Danish ministry of Energy to Danish Wind Technology (DWT). ASEA and the Danish ministry of Energy were not active in the industry before. Volund left and DWT entered the industry which is a status quo for the accumulated number of firms. The same status quo yields for ATB Riva Calzoni. ATB was not in the industry before the merger with Riva Calzoni. A total 37 M&A activities in 38 years on a total of 228 competitors took place in the industry. The average annual sum (.97 per year) of mergers and acquisitions divided by the total number of

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years makes a rate of .01

Figure 1: Entry, exit and M&A

In Figure 2 the cumulative entry, exit and total firms is displayed. There is an increase of the

population up to 1987 and a decline until 1991. From 1992 on, the population has grown an average of 3 firms per year. If the total firm curve is compared to the ILC theory it can be found that the industry is still growing. The pioneering phase of Neukirch (2010) develops as following. The end of period 1 contains a growth from zero to 13 firms (16 entries, 3 exits). At the end of period 2 there are 40 firms (38 new entries, 11 exits), at the end of period 3 there are 64 firms (35 new entries, 11 exits) and at the end of period 4 there are 48 firms left (9 new entries, 25 exits). 50 Out of the 98 firms exited the industry in the pioneering phase.

Figure 2: Entry, exit and total firm development

0 2 4 6 8 10 12 14 16 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Num ber o f firm s Time in years

Entry, exit and M&A

Entry Exit M&A 0 20 40 60 80 100 120 140 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Num ber o f firm s Time in years

Entry, exit and total firm development

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Changes In Nature Of Innovation

The EWEA publicized in 2009 an extensive report on the wind industry with an overview of developments in technology over time and also provides areas for future development. Figure 3 displays the main evolution of designs over time and shows that technological development is

cumulative. From stall regulated, fixed speed and with geared transmission to the present, pitch

regulated, variable speed and with direct drive transmissions appearing, along with continued use of gearbox transmissions.

Figure 3: Technology trends (EWEA, 2009)

The EWEA (2009) states that there is still room for developments in fundamental meteorology, aerodynamics and materials science which can be a base for new innovative pathways. According to the report, R&D focus will be on supporting the new drive towards offshore technology. Offshore wind energy provides opportunities for the development of a whole new branch of technology within the wind turbine industry which will also involve companies from the established offshore energy field (EWEA, 2009). And there are still untried concepts for turbine design which may be worth serious consideration. This implies less incremental product innovation.

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(2002) on wind turbine design it can be concluded that horizontal axis position and three bladed turbines can be seen as the dominant design in the industry. Within that dominant design, there are ongoing product innovations which are shown in Figure 1by shifting technology trends. Because of the ongoing major technological changes and areas to discover it can be concluded that the nature of innovation is still in product innovation but process innovations occur more often last decade (Hansen & Hansen, 2007, p.81).

Survival Pattern: Entry Timing

There are 120 active firms in the industry. The average age of the survivors is 11.96 years. The average age of the 108 non-surviving firms is 7,7.

Figure 4: Entry timing by survivors and non-survivors

Figure 4 shows that most survivors entered between 2002 and 2013. Most firms that dropped out have their roots in the early years of the industry. The age distribution of firms is presented in Table 1 where the age represents their actual age and the period of entry. For non-surviving firms the age represents the age the firm has become before exiting the industry. For the survivors, 65 firms (54%) are nine years or younger. A Germany manufacturer is the oldest company with 35 years. Among the seven oldest firm is Danish manufacturer Vestas the largest firm, market leader in 2012 (Cleantech, 2012) and ranked in the top three of largest manufacturers in the world (Windpowermonthly, 2013). Enercon entered in 1984 and is ranked fifth (Cleantech, 2012). 77 Firms (71% ) of the sample didn’t become ten years old. Most survivors entered between 2004 and 2013.

Age Year of entry

(of survivors) Survivors Non-survivors 0 up to 4 2009-2013 24 42 5 up to 9 2004-2008 41 35 10 up to 14 1999-2003 18 18 15 up to 19 1994-1998 10 5 20 up to 24 1989-1993 8 4 25 up to 29 1984-1988 12 2 ≥30 1976-1983 7 2 0 5 10 15 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Num ber o f co m pa nies

Entry timing by survivors and non-survivors

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Table 1: Age distribution survivors

As addition on table 1, figure 5 presents when non-surviving firms entered the industry and what age they have become before exiting the wind turbine industry. 50 Of the108 non-surviving firms entered during the pioneering phase.

Figure 5: Age and year entry of non-surviving firms

Survival Pattern: Pre-entry Experience

In Table 2, the entry modes are presented in cross tabulation form and broken down in survivors and non-survivors. For each period in time an overview is shown. The first figure between the parenthesis display percentage of firms that became survivor or non-survivor for each entry method and the second figure is the percentage of the entry mode in the group of survivors and non-survivors. Overall, non-surviving firms that entered with the new firm entry method have higher failure rates. Firms that enter by diversifying give the highest rate of survival. Among the non-survivors, 48,1% of the firms chose the NF/NP mode of entry, 36,1% the DF/PM mode and 15,7% the DF/NP mode. Among survivors is the entry mode quite balanced with 35,8% of the firms with NF/NP mode, 36,7% with the DF/PM mode and 27,5% with the DF/NP mode.

No entrants of the first two years survived no matter what entry method they have chosen. From the period of 1978 until 1983 seven (15%) firms are still active whom used both new entry and

diversifying by altering the product mix. Relatively the highest rate of failure can be found by DF/NP where none of the six firms survived. Almost 50% of all entrants in this period that chose NF/NP didn't survive. The period between 1984-1988 shows that the number of firms that tried to enter the industry declines. Most entrants try the NF/NP method where 45% of that group survived. In the crisis period of 1989-1993 19 firms entered the industry. NF/NP and DF/NP seemed to be the most fertile entry method in this period with more survivors than non-survivors. No DF/PM firms were able to survive. The next period shows a similar entry method pattern in absolute numbers but in this period

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there are more surviving entrants with an almost balanced entry method for the survivors. The period between 1999 and 2003 is the first period where there are more surviving entrants than non-survivors. NF/NP and DF/PM show high survival rates. DF/NP is balanced. Between 2004-2008 the rate of survival is the same but the number of entries doubled. But during this period DF/NP has a high survival rate. In the last period only one firm left the industry and 24 entered. Most firms (12) entered by DF/PM , eight entered by DF/NP and four with a new firm and new plant.

Status vs. Entry method cross tabulation total

Method of entry Total

NF / NP DF / PM DF / NP

Status Non-survivor 52 (54,7%, 48,1%) 39(47%, 36,1%) 17(34%, 15,7%) 108

Survivor 43(45,3%, 35,8%) 44(53%, 36,7%) 33(66%, 27,5%) 120

Total 95 83 50 228

Status vs. Entry method cross tabulation (1976-1977)

Status Non-survivor 6 2 0 8

Survivor 0 0 0 0

Total 6 2 0 8

Survivor 3 4 0 7

Total 28 19 6 53

Survivor 10 1 1 12

Total 22 6 1 29

Survivor 5 0 3 8

Total 8 6 5 19

Survivor 3 4 3 10

Total 6 10 5 21

Survivor 9 6 3 18

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Survivor 12 12 17 41

Total 14 15 21 50

Survivor 4 12 8 24

Total 4 13 8 25

Table 2: Survivors and non-survivors versus method of entry (per period)

Table 3 displays the entry modes in cross tabulation form, broken down in survivors and non-survivors but here there is a separation between the fluid en transitional stage. The survival rate in the fluid stage is low in total and at each entry method. In the transitional stage most entries are on NF/NP but DF/NP has the highest survival rate, followed by NF/NP and DF/PM.

Status vs. Entry method cross tabulation (Fluid stage 1976 - 1983)

Survivor 3 4 0 7

Total 34 21 6 61

Status vs. Entry method cross tab. (Transitional stage 1984-2013)

Method of entry Total NF / NP DF / PM DF / NP Status Non-survivor 21 22 11 54 Survivor 43 35 35 113 Total 64 57 46 167

Table 3: Survivors and non-survivors versus method of entry (per ILC stage)

21 Firms from outside Europe entered the European market. Seven by opening a new production facility and 14 by altering their mix of outputs. Two out of the 21 non-European firm didn’t survive. They both applied a DF/PM entry method. These diversifying firms from outside Europe have wind turbine industry specific experience but apply it on a new market.

Discussion and Conclusion

The research question of this paper was “How did the European wind industry develop from the ILC theory perspective?” This question was broken down in the following sub questions:

 How did entry and exit activity evolve over time?

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 What is the survival pattern among the wind turbine manufacturers? o What impact does entry timing have on survival?

o What impact does pre-entry experience have on survival? Hereafter, all questions will be answered and discussed.

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which is also noticed in the 2009 EWEA report. After the crisis the growth continued steadily except for the year 1997 where eight firms left the industry wherefrom five with other reasons than M&A. Abernathy & Utterback (1978) stated that market growth slows towards the end of the transition phase, leading to a shakeout over time and the maturity phase sets in when industry growth stalls or slows dramatically indicating that only the most efficient and best positioned firms survive, leading to a shakeout over time (Afuah & Utterback, 1997). Based on the former statements and the findings of this study, that the industry is still growing due to growth of the industry in firm numbers, it can be concluded that the wind turbine industry is still the transition phase.

Malerba & Orsenigo (1996) stated that, entry often occurred through acquisition. In this sample, only one acquisition is recorded for a firm outside the industry that entered by acquiring a wind turbine manufacturer. Hauschild, Zu Knyphausen-Aufseß & Rahmel (2011) supported Malerba & Orsenigo (1996) and even proposed that other forms of entries and exits like insolvencies are relatively minor compared to the number of M&A. But this is not the case in the wind turbine industry. From the 108 exits, 41 where by M&A. The wind turbine industry is not in line with statements of Malerba & Orsenigo (1996) and Hauschild, Zu Knyphausen-Aufseß & Rahmel (2011). This can be explained by Yin & Shanley (2008) because they argue that alliances are more likely than M&As in industries in which technological uncertainty is high.

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According to Lumpkin & Dess (2001) most successful start-ups are those launched in the growth stages of an industry's life cycle. This holds for the wind turbine industry as well. But it might be obvious because the industry is still growing within the transition phase the industry is in. The findings and the Cleantech (2012) market share overview show that two firms from the pioneering phase in Europe have become global leaders. This might hold that Vestas and Enercon benefitted from their early-mover advantages. Vestas is the only firm that was active in the fluid phase and before the emergence of the early dominant design. Lumpkin & Dess (2001) argue that the advantage of early entrants is explained by cumulative learning, economies of scale in production, and cost spreading of R&D. Klepper (2002) stated that entry timing is random because firms enter the industry when they have developed the capabilities for an industry. This doesn’t seem to hold in this context considering most firms left in the pioneering phase of the industry. That most firms left in the pioneering phase and fluid stage can be explained different factors like the crisis mentioned by Neukirch (2010) and because incumbents suffer from technological obsolescence by not adopting the dominant design so entrants that produce conform the dominant design enjoy a higher rate of survival (Agarwal, 1996). Anyhow, the survival rate after the pioneering phase (and crisis) is higher. Figure 4 presents that most survivors entered between 2004 and 2013. But there is still a large possibility of exiting because 77% of the non-surviving firms didn’t pass the ten years. The most non-survivors had their roots in the pioneering phase. Agarwal & Gort (2001) questioned the idea of early-mover advantage because it may be disappearing because early entrants do not enjoy the same type of information advantages. From the top ten largest companies of 2011 only Vestas has its roots in the fluid phase and together with Enercon they have their roots in the pioneering phase. The companies from the top ten outside Europe entered later but have built knowledge and experience on their domestic markets.

Summarizing, most survivors entered after the pioneering phase so in order to survive entry timing matters but the most successful firms in Europe entered early which might imply that they have benefited from early-mover advantages. But Agarwal (1996) stated that in an era of intense technological activity, entrants enjoy a higher rate of survival, because incumbents suffer from technological obsolescence.

There is almost no difference in firm numbers between entry mode among survivors on the grand total but the failure and success rates are different. The exit rate is lowest for the diversifying-firm, new-plant entrants which is supported by Dunne, Roberts & Samuelson (1988). But Dunne, Roberts & Samuelson (1988) revealed that small and young firms have the highest failure rates. That conflicts with the findings of this research and with the findings of Eisenhardt & Schoonhoven (1990), Sandberg & Hofer (1987) and Lumpkin & Dess (2001). But, Klepper & Simons (2000) argued that company size may be more important than age with regard to explaining survival use it as an

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Theoretical and Managerial Implications

Helfat & Lieberman (2002) found that pre-entry experience can cause a higher probability of survival. The results of this study on the wind turbine industry support that and contribute because this industry is heavily subsidized and adds the insight that diversifying firms that build a new production facility have a higher survival rate in the transitional stage of an subsidized industry. This contribution can used for diversification decision for managers in subsidized industries. Furthermore, this research shed a light on the current stage of the life cycle the wind turbine industry is in. Afuah & Utterback (1997) provide strategical options for the transitional stage. The focus should be on skills for product

differentiation, focus on marketing and in preparation for the maturity stage, firms should advertise to establish brand recognition. Findings in this is research might have implications in other industries that are involved in sustainable energy development with the same uncertainties in the diffusion, subsidy structure and development of technology.

Limitations and Future Research

The non-cooperation of official institutions for collecting data about wind turbine manufacturers can be seen as a limitation. Small firms might be missed in the search because they had a minor

contribution on the industry and were not mentioned in papers or books. The market entry date might be blurred because the sources didn’t provide clear information launching the first product on the market and the year foundation. Next, Dunne, Roberts & Samuelson (1988) stress the importance of post entry characteristics but this research doesn’t take characteristics like firm size and market shares in account as a variable of survival. Roberts & Samuelson (1988) concluded that diversifying firms that enter through new-plant construction exhibit the most stable market shares, while diversifying product-mix entrants register the most dramatic increase in the average size of survivors. This could implies a lack of industry specific variables, like firms size and success in the sense of market share and financial measures and are a limitation for this study. This limitation can serve as an opportunity for future research in this industry context. The same yields for the absence of the impact of alliances and networks (Yin & Shanley, 2008) and might shed new light on survival and success of firms in the industry. Future research could aim on the innovative efforts and the technological changes of

survivors and incumbents to give a better view on survival. This is supported by the statement of Utterback & Suarez (1993) because they argue that the dynamics are a reflection of underlying driving forces based on technological change that determine the form and level of competition, the

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involved in sustainable energy development with uncertainties in the diffusion and development of the technology. Like solar energy, hybrid energy sources or in any other industry where the performance of new sustainable technologies is initially less than that of the existing technologies (Anderson & Tushman, 2001). This study didn’t make a distinction between two general classes in wind turbine output namely utility scale and residential scale (Enernex, 2003). The AWEA (2009) recognizes a growth market in small residential scale wind turbines which makes it an interesting topic for future research. Ackermann & Söder (2000) noticed that due to the fast market development and the evolution of the wind turbine technology, co-innovation in other areas, that are complementary to wind turbine technology, is needed. Bayus & Agarwal (2007) highlight that it is crucial to study what firms do after they enter a new industry to more completely understand their ultimate performance.

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Windpower Monthly (2013) Windpower Monthlys roundup 2013