Falling Flat: Failed Technologies and Investment under Uncertainty

Falling Flat:
Failed Technologies and Investment under Uncertainty
Jamie P. Eggers
3024 Steinberg Hall - Dietrich Hall
Wharton School
Philadelphia, PA 19104
tel: (215) 746-3111
e-mail: [email protected]
JOB MARKET PAPER
Working Paper (September 2007)
This research was supported by the Mack Center for Technological Innovation. The author wishes to
thank participants at the CCC, ACAC, and Academy of Management conferences and the Wharton PhD
Seminar Series, as well as Tim Folta, Sarah Kaplan, Dan Levinthal, Will Mitchell, Tom Murtha, Laura
Poppo, Nicolaj Siggelkow, Christophe Van den Bulte, and Sid Winter for helpful comments and
suggestions on earlier drafts of this paper. A condensed version of this paper appears in the Academy of
Management Best Paper Proceedings, 2007. Wharton undergraduates Erin Lee and Sharon Volotzky
provided invaluable research assistance.
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Falling Flat:
Failed Technologies and Investment under Uncertainty
Abstract:
The quest for first mover advantages can spur firms to enter new technological markets quickly. But these
new markets often begin with a significant amount of technological uncertainty about how the market will
evolve. This implicit assumption of this uncertainty is that some early technological investments will
result in failure. However, the management literature has very little to say about the potential firm-level
consequences of making investments in technologies that do not go on to revolutionize a new market (i.e.
“failed technologies”). This paper investigates the complexities of pursuing first-mover advantages under
conditions of technological uncertainty. It does so by evaluating the relationships between investments in
a failed technology and the decisions to exit and to pursue future opportunities in the same market, and
the product-level consequences of these decisions. The research setting is the flat panel display market
between 1965 and 2005, which initially featured multiple competing and uncertain technologies. The
research demonstrates that failed investments during the uncertain period of the market’s development
constrain the future choices of firms by promoting exit and encouraging firms to pursue niche strategies
or strategies other than technological leadership. These choices, however, are driven more by the
organization’s response to the failed investment and less by rational decision-making, as potential rational
explanations are not supported empirically and fieldwork offers behavioral explanations. The firms
initially pursuing the failed technology but choosing to remain in the market actually create superior
products in the dominant technology years later, suggesting that firms making more conservative
decisions may have abandoned a potentially successful – though difficult – opportunity. The implications
for our understanding of first mover advantages, technological path dependency and real options are
addressed.
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INTRODUCTION
The quest for first mover advantages can spur firms to enter new technological markets quickly
(Lieberman & Montgomery, 1988). These new technological markets are inherently uncertain at their
inception (Christensen, Suárez, & Utterback, 1998; Mitchell, 1989), but firms seeking first-mover advantages
may be required to enter this uncertain market early in order to build necessary capabilities and knowledge
that will allow them to bring products to market sooner. The reason for this is that both types of
technological first-mover advantages identified in the literature – learning curve advantages, where firms
gain technological superiority through more extensive experience with a new technology than rivals (Amit,
1986; Argote & Epple, 1990; Arrow, 1962), or pre-emptive patenting, which enables firms to secure control
of technological knowledge areas (Gilbert & Newberry, 1982) – require long experience with the emerging
technology. Thus, the desire to establish first-mover advantages can lead many firms to make speculative
investments in an inherently uncertain technological market.
This technological uncertainty presents important challenges to firms and managers, as there is a
significant potential for one or more of the firm’s technological investments to result in failure. In the typical
evolution of new markets, a period of technological uncertainty with multiple competing technologies is
followed by standardization as the industry coalesces around a specific technological platform (Anderson &
Tushman, 1990; Schilling, 1998). The implicit assumption is that one technology wins and others lose. Thus
securing of a first-mover advantage may be contingent upon making the right choices in this early period and
supporting the winning technology (Christensen et al., 1998). While there is ample work on the adoption of
successful new technologies in existing work on radical technical change (Tripsas, 1997; Tushman &
Anderson, 1986), technological shakeout (Jovanovic & MacDonald, 1994; Klepper, 1996; Utterback &
Suárez, 1993), and technological evolution (Dosi, 1982), we know practically nothing about the firm-level
consequences of supporting the wrong technology initially.
This paper investigates what happens when firms get it wrong in a new market, and whether the
options these firms face are as bleak as common sense might suggest. The shakeout literature suggests that
firms investing in failed technologies simply leave the market completely (Jovanovic & MacDonald, 1994;
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Klepper, 1996), but it is not clear that this should always be the case. Larger firms are more likely to be early
entrants into the new market than smaller firms, as success in the new market is of specific strategic
importance to them (Mitchell, 1989; Thomas, 1996). Thus, exit does not make sense as the only possible
outcome of early failed investments. This gap in our understanding of whether unsuccessful technological
investments by firms have any long-lasting effects for organizations presents a real difficulty in
understanding the ability of firms to create first-mover advantages, and may contribute to the mixed and
conflicting results about the relationship between market entry order and performance (Makadok, 1998;
Markides & Geroksi, 2005).
This is not to suggest that existing strategy literature makes no prescriptions about how organizations
and managers should deal with the potential for technological investments to fail. Real options theory
suggests that the optimal strategy to cope with uncertainty involves investing in multiple technologies and
then focusing on the dominant technology as it emerges (McGrath, 1997), similar to the way a venture
capitalist might invest in multiple speculative companies in a single market. There are, however, potential
problems with this logic. Investing in multiple technologies is costly in terms of financial, managerial, and
research resources, which limits the viability of this strategy for all companies (Chesbrough, 2003).
Additionally, as Adner and Levinthal (2004) point out, the potential value of real options theory as a decision
heuristic requires the ability to abandon failed early-stage investments. Given the observed path dependency
within research and development activities of organizations (Helfat, 1994a, 1994b), this seems like a
challenging proposition in the face of innovation failure. An understanding of whether investment in a failed
technology has an impact on an organization’s ability to focus on the emerging dominant technology would
provide insight into whether organizations use real options as conceptualized, or whether there are spillover
effects – negative or positive – from contemporaneous “option-like” investments with different outcomes.
To understand whether the consequences of failed investments truly are bleak, this paper addresses
both on what firms do when they suffer a significant, failed investment – including both exit and response to
future opportunities – and on what the product-level consequences are of that initial failure. The empirical
data are drawn from the flat panel display market between 1965 and 2005. In the early stages of the market’s
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development (1965-1982), a number of competing technologies existed, primary among them liquid crystal
(LCD) and plasma displays. Engineers and managers expressed uncertainty about which technology would
develop into profitable products. In the early 1980s, a series of technological innovations (and concomitant
commercial opportunities) propelled LCD to its status as the dominant technology. At this point, it became
clear that firms originally supporting plasma had backed the wrong technology and faced a series of
important strategic choices about how to respond. Two other historical factors make this context an
interesting one in which to study these phenomena. First, the technological competition did not play out in
the marketplace but in the R&D labs as firms raced to bring these products to commercialization. Firms had
knowledge of which technology was the winner before making sizable and irreversible investments that
might prevent adaptation. While this setting is lower-profile than situations of market competition for new
technologies, it is a much more common situation faced by firms as they experiment with potential
technological solutions to business problems. Second, despite the “triumph” of LCD technology, research on
plasma and other new display technologies continued over time, allowing firms to remain in the market
without adopting LCDs. This continued research on plasma displays brought about a resurgence in this
technology that has eventually led to plasma being a popular choice in one market segment for flat panel
televisions (though it is not used in many other flat panel applications). This resurgence offers an intriguing
opportunity to test the degree to which organizational outcomes are affected by prior experiences.
The theoretical perspectives set forth below are based largely on implicit assumptions from existing
literature, as few scholars have directly addressed the consequences of failure. I surface those implications as
specific hypotheses and then test them empirically. In a sense, I am looking at the mirror image of the
literature on technological success in order to understand technological failure. The logic presented is
familiar therefore, even if the exact hypotheses are novel. The empirical findings are less expected and
present an interesting picture of the decisions made by firms over time. In general, the models demonstrate
that firms initially pursuing the failed technology make conservative strategic decisions within the same
market – both by exiting and (for those that choose to remain) by limiting the aggressiveness of their
response to future technological opportunities. This limiting of exposure to future risk takes the form of
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increased hurdles for risky investments and the promotion of risk-averse decision-makers, among other
mechanisms. However, those firms that do choose to remain and do develop products in the dominant
technology actually perform well (in terms of outside expert ratings of their products). Thus, these early
technological decisions play a role in understanding the establishment of dominant positions within the
market over time, but that the decisions that lead firms to those positions may not have been based
completely on rational responses to the opportunities available to firms. These early failed investments
appear to have path dependant effects on the choices that managers perceive as being available to the
organization as the market evolves. By looking at the implications of failure, this research more broadly
addresses the potential learning we are ignoring by focusing only on successes.
THEORETICAL DEVELOPMENT
Despite the theoretical importance of failed technologies to the literatures discussed earlier, there is
very little research on technological failures. In both the economics research on competition under network
externalities (Arthur, 1989; Schilling, 1998) and case studies of VHS versus Betamax (Cusumano,
Mylonadis, & Rosenbloom, 1992; Rosenbloom & Cusumano, 1987), the research addresses only address the
antecedents of success or failure in very specific market-competition contexts. The “losing” technologies
considered in the research on radical technical change (Tripsas, 1997; Utterback & Kim, 1986), the evolution
of technological paradigms (Dosi, 1982), and technology-driven shakeouts (Jovanovic & MacDonald, 1994;
Klepper, 1996; Utterback & Suárez, 1993) cannot be considered failures, as they generally were older, onceprofitable technologies that are replaced by newer technologies. A discussion of failed technologies would
require either competing technologies with a clear winner or loser, or a challenger to an existing technology
that failed to oust the incumbent (such as Laser Discs from Pioneer in the 1980s, who failed to unseat VHS).
Work on first-mover advantages (Lieberman & Montgomery, 1988; Mitchell, 1989) discusses the possibility
of technological uncertainty existing before the establishment of the dominant technology, and that this
uncertainty may be a reason that firms are reluctant to enter the new market immediately (Wernerfelt &
Karnani, 1987). This literature, however, has generally defined a “first mover” as the early firms to enter
successfully into a significant new market (Kerin, Varadarajan, & Peterson, 1992; Robinson, Kalyanaram, &
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Urban, 1994) – thus ignoring the possibility of failed technologies and investments, and the potential
consequences of pursuit of those failed technologies on the ability of the early movers to obtain first-mover
advantage in the emerging market.
Other work has focused on failure more broadly, but largely this literature focuses on the antecedents
of organizational failure. Work in population ecology (e.g. Hannan & Freeman, 1989) demonstrates how
organizational mortality is related to inertia (age) and competition (density dependence). The product
introductions literature demonstrates how careful management of the firm’s product portfolio in relation to
the competitive environment affects survival chances (Cottrell & Nault, 2004; Sorenson, 2000). The message
of my research, however, is that we need a better understanding of the consequences of prior failures on
future actions. One group of studies investigating the consequences of failure looks at how firms learn about
the viability of uncertain strategic actions by studying the actions and failures of other firms in the market
(Miner, Kim, Holzinger, & Haunschild, 1999). Hoetker and Agarwal (2007) show that firms may learn
vicariously through the dissemination and reuse of knowledge created by firms that later failed. The studies
discussed above do not address learning by the firm that failed, but instead study vicarious learning
processes. Arino and de la Torre (1998) look at the ways in which two firms in a joint venture or alliance
relationship learn from failures and breakdowns in their relationship, and how that learning encourages the
firms to go back to the negotiating table to prevent future failures. However, none of these papers addresses
the question of the organizational consequences of a significant failure experience, regardless of context.
Organizational Failure and Market Exit
While no existing literature directly addresses the organization-level consequences of failed
investments, the shakeout literature is the most closely related literature and offers the clearest implicit
hypothesis. As discussed earlier, this literature looks at the replacement of an existing technology with a new
one, but the findings suggest that firms supporting the losing technology will exit the market once it is
revealed that their technology will not be dominant in the future (Jovanovic & MacDonald, 1994; Suárez &
Utterback, 1995). The empirical research on shakeouts generally does not measure technology investments,
looking instead at population-level data on entry and exit. One study does measure technological
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commitments and supports the theoretical causes of shakeouts (Tegarden, Hatfield, & Echols, 1999). The
shakeout literature also makes two other relevant assumptions – that exit occurs only after the emergence of
the dominant technology, and that firms can only support one technology or the other. The former
assumption I will test empirically. The latter assumption I will relax by focusing on the degree of the firm’s
research activity in the failed technology, controlling for the firm’s activity in the successful technology,
consistent with the real options perspective (McGrath, 1997). Overall, I present the following hypothesis to
directly align with the findings of the shakeout literature.
H1: The greater a firm’s research activity in a “failed” technology in the early, uncertain period,
the greater the likelihood of abandoning the market once the dominant technology is identified.
Response to Future Technological Opportunities
Many firms that support the wrong technology initially may not leave the market after the dominant
technology emerges. Many firms that choose to enter before the establishment of a clear dominant
technology may be large, de alio entrants from closely related industries with a significant desire to succeed
in the new market (Mitchell, 1989). Such entry is likely to be in hopes of achieving a first mover advantage.
Whatever choices the firms make in the early, uncertain period of the industry, the ability of these firms to
respond aggressively to the emergence of future opportunities within the new market space is crucial to their
ability to establish this first-mover advantage. In some situations studied in the first-mover advantage
literature, the introduction of the first product in a new market requires nothing more than marketing
innovation (packaged goods sizes, cereal flavors, etc. (Kerin, Kalyanaram, & Howard, 1996; Thomas, 1995,
1996)). In new markets requiring more technical knowledge, however, there is a lag between the time a firm
chooses to pursue a new opportunity and the introduction of the first product. This lag period is where the
two sources of technological first-mover advantage identified in the literature – pre-emptive patenting
(Gilbert & Newberry, 1982) and learning curve benefits (Argote & Epple, 1990; Arrow, 1962) – are
established. Thus, understanding how aggressively firms respond to the emergence of these technological
opportunities gauges the degree to which a firm is making the necessary investments that may lead to a firstmover advantage. I will present three different views on how firms may be expected to respond to the
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emergence of these opportunities over time based on their technological decisions in the period before the
emergence of the dominant technology.
It is possible that firms that initially support a failed technology may be quick to pursue future
opportunities within the market. Organizations and managers may have consistent risk tolerance profiles, so
firms that made one decision to pursue a new technological opportunity vigorously are more likely to pursue
additional opportunities in the same manner (March & Shapira, 1987). To the extent that risk preferences
would be changed by prior losses or failures, Prospect Theory indicates that managers may become more
likely to accept risky bargains after experiencing failures (Kahneman & Tversky, 1979). Managers that have
already made one commitment to an emerging market may face strong external pressure to succeed in this
new market at all costs (Mitchell, 1989), and may be inclined to escalate their commitment to the market
even if that means investing new money in new technologies (Brockner, 1992; Staw, 1981). This suggests
that managers who have already made one decision to pursue a risky technology will be even more likely to
pursue future risky opportunities within the same market, and that this more aggressive response will be an
increasing function of the degree of initial research in the technology that later fails.
H2a: The greater a firm’s research activity in a “failed” technology in the early, uncertain period,
the more aggressive the firm will be in pursuing new technological opportunities within the same
market.
Conversely, firms initially pursuing the wrong technology may be slow in responding to new
technological opportunities. The simplest rationale is that these firms may be rationally responding to entry
barriers already being erected by the earliest movers into the new market space who initially supported the
winning technology (Lieberman & Montgomery, 1988)1. Additionally, Helfat (1994a; 1994b) presents an
evolutionary perspective of R&D activities, suggesting that the activities in the past are the best predictors of
activity in the future. Thus, firms are reluctant or unable to make significant changes to that portfolio due to
the need to acquire new skills. Work in psychology and organizational behavior suggests that managers that
receive negative feedback may be very reluctant to pursue other risky opportunities (Cannon & Edmondson,
2001; Hodgkinson & Wright, 2002; Ilgen & Davis, 2000). Senior management decision-makers who allowed
1
This and other related rationales for underinvestment will be discussed later when I unpack potential mechanisms for
my findings.
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money to be invested in a new technological path but later received negative feedback on the viability of that
investment may be very reluctant to support yet another risky technological endeavor within the same market
space. After initial work by Staw and colleagues (Staw & Fox, 1977; Staw & Ross, 1978), Staw, Sandelands
and Dutton (1981) suggest that, in the face of external threat, organizations are likely to “rigidly” pursue
established routines, and not engage in rash behavior. This behavior of ignoring the activities of other
organizations and focusing on existing processes is most likely to occur when firms face a threat to
profitability (George, Chattopadhyay, Sitkin, & Barden, 2006). This perspective presents the idea of senior
managers who, having already allocated resources to a failed technological effort, are less likely to allocate
further resources to what they may view as risky and speculative research projects. This reluctance will lead
to less aggressive pursuit of future opportunities in the same market space.
H2b: The greater a firm’s research activity in a “failed” technology in the early, uncertain period,
the less aggressive the firm will be in pursuing new technological opportunities within the same
market.
While these two hypotheses – one advocating inertia and one momentum – are conflicting on the
surface, the underlying theories are not necessarily conflicting. Instead, the application of each theory in this
context relies on different assumptions about how decisions are made within the organizations. The theories
supporting momentum and acceleration of activity generally assume that the decision maker controlling how
much to invest in the future opportunities is the same person, and has the same level of control and
autonomy, as the decision maker for the initial (failed) investment. This is certainly true for heterogeneous
risk tolerance and Prospect Theory, and is likely true for escalation of commitment. Thus, for any given
individual, an early failure experience may lead to a future acceleration of activity. This perspective presents
a picture of managers out to validate the initial decision to pursue the new technological market in the first
place, whatever the organizational and financial cost. For the inertial theories, it is less clear whether they
would operate at the organizational or individual level. Rational responses to entry barriers, path dependency
in research and development, and threat-rigidity have largely been looked at as organizational concepts, but
could easily apply to the behavior of individual managers, as well. This image could be of a more senior
decision maker intervening in the process (or even a different decision maker altogether), tempering the
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enthusiasm and expectations of the manager that might advocate accelerating activity. Thus, an empirical
finding in support of acceleration – Hypothesis 2a – would likely indicate that the same manager had
authority during both decision periods, while an empirical finding in support of inertia – Hypothesis 2b –
would require further investigation into the causes of that inertia. If I find no support for either hypothesis, it
may be because neither one is actually going on or because both are going on within the sample (not
necessarily at the same firm), and the effects are cancelling each other out. I will address this issue later in
the results section.
While a failed technology has (by definition) lost out on the opportunity to become the dominant
technology, it may still have a specific value for a niche market. While managers initially investing in this
technology may be disappointed at this outcome they likely would choose to pursue whatever rents are
available based on the knowledge, capabilities, and assets generated by the initial investment in this
technology. This may be a rational response based on sunk costs, or it may be a manifestation of escalation
of commitment (Staw, 1981). Given this perspective, it seems likely that firms initially pursuing a failed
technology may still wish to (and be able to) derive some return from that initial investment, even if the
market opportunity is much smaller.
H3: The greater a firm’s research activity in a “failed” technology in the early, uncertain period,
the more aggressive the firm will be in pursuing the same technology for a specific niche.
Long-Term Product-Level Outcomes
There are also reasons to believe that these failed early investments may have consequences not just
for decisions like exit or response to new opportunities, but for outcome measures of performance. While the
first-mover advantage literature has primarily focused on market share and profitability as performance
measures (Kerin et al., 1992), these outcome measures are highly dependent on the quality of the firm’s
commercialization capabilities and complementary assets. These assets and capabilities would not logically
be affected by the early technological decisions of the firm. Therefore, considering outcome measures such
as market share and profitability may obscure the effect of failed technological investments. A dependent
variable more closely related to the outcome of the research and product development process would be more
appropriate. In this research, I focus on the quality or value of a given product, which is a common measure
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used in the product development literature to evaluate the success or failure of research and development
processes (Harter, Krishnan, & Slaughter, 2000; Sethi, 2000).
The primary rationale for pursuing any given new technology faster than rivals is to build effective
barriers to entry (Teece, 1986; Wernerfelt & Karnani, 1987). For firms initially pursuing the wrong
technology but determined to remain in the market, they may be forced to play catch-up with the firms
already gaining experience in the dominant technology. Capabilities are not necessarily openly available in
the marketplace, and instead firms must build them up over time (Dierickx & Cool, 1989), putting late
adopters of a specific technology at a competitive disadvantage. In this situation, the ability to move “down
the learning curve” (Amit, 1986; Argote & Epple, 1990; Arrow, 1962; Yelle, 1979) in terms of both R&D
and manufacturing would constitute a first-mover advantage that may be expected to result in inferior
products from later adopters (Arthur, 1989; Lieberman & Montgomery, 1988). Levin (2000) demonstrates
that the learning curve process related to the quality of the output product (instead of just production cost) is
a function of cumulative experience time with the specific technology, providing early entrants a significant
product-level advantage. This leads to the logical conclusion that these early investments in failed
technologies may relegate even those firms determined to remain within the industry to producing inferior
products to those early movers with established advantages.
H4: The greater a firm’s research activity in a “failed” technology in the early, uncertain period, the
lower the firm’s ability to produce products in the dominant technology that the market values highly.
DATA AND ANALYSIS
Research Setting
My empirical tests of these hypotheses are set in the flat panel display market. According to Display
Source, one of the leading providers of data on displays, the 2006 flat panel display market (panels only, not
finished products) was around $85.5 billion worldwide, up 14% from 2004. Korean manufacturers LG
Electronics and Samsung are the two leading branded suppliers, while Taiwanese companies AU Optronics
and Chi Mei Optronics were two of the leading OEM providers. Despite the current dominance of Korean
and Taiwanese manufacturers, however, the market has long roots in both the U.S. and Japanese markets.
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In the 1960s, a series of technological discoveries in several U.S. research universities indicated the
possibility of creating large flat panel screens to replace the existing cathode ray tube (CRT) televisions and
monitors that were prevalent at that time. In this early stage of the market’s development (1965 through the
early 1980s), there were a number of competing technologies, most notably liquid crystal (LCD) and gas
plasma displays, but also including some more speculative investments in and experimentation with
electroluminescent, electrochromic, field emission, and other displays. At this time (long before there were
any viable commercial applications to this research), there was a great deal of uncertainty about which
technologies would be able to develop into profitable products. From the late 1960s to the early 1980s,
multiple firms in the U.S. and abroad made significant research and development investments hoping to
achieve the breakthrough discoveries that would make one of these technologies viable and establish it as the
dominant technological trajectory2. These firms came from a variety of backgrounds – television
manufacturers, computer companies, watch companies, and makers of light emitting diodes (LEDs, the
simple technology largely replaced by flat panels). Each firm had different visions of what markets the flat
panel technologies would be able to serve in the future – TV manufacturers were fixated on wall-hanging
televisions, computer companies on laptop computers, industrial equipment companies on advanced readout
displays in smaller spaces, etc.
In the early 1980s, important technological advances led to the emergence of LCD technology as
dominant technology. This dominance of LCD was based on technological superiority for the early target
markets for flat panel technologies (watches, laptops, computer monitors) and not on marketing or consumer
acceptance. Long-time market insiders point to Seiko-Epson’s appearance at the January 1983 Society for
Information Display (SID) conference with a working 1” color LCD television prototype as one of the most
important events in the early evolution of the market. While researchers in other firms had known that Seiko
was working on such a device, the presentation of the working prototype was an eye-opening experience for
many observers. One market insider who was at the event called the product “spectacular” and another
2
Before 1983, more than 3,500 flat panel display patents were applied for across all technologies. By comparison,
IBM’s total patenting output (across all product types) for 1992 was less than 700 patents.
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remembered telling his superiors that, “this could really be the key for the future of flat panels.”3 The crowds
at Seiko-Epson’s booth and the discussions among the various attendees at the conference spurred R&D
managers from firms that already had LCD investments to ask senior management for additional funding to
pursue LCD technology, and led managers from firms pursuing different technologies to question their
commitment to these other technologies and consider reorienting around LCD technology. The result was
that more than 20 other companies exhibited active-matrix LCD screens at SID events between 1984 and
1991 (Howard, 1992). This growing interest in LCD technology in this period can clearly be seen from
studying the patent records in the market during this period, as depicted in Figure 1. During this period, the
aggregate patenting in LCD matched and then quickly passed that in plasma, the primary rival technology
during the early period of the market’s evolution. As this paper focuses on the role of investment decisions in
the early, uncertain period, this period is defined as starting with the first patents in the flat panel display
market (1965) and ending just before the emergence of LCD as the dominant technology (pre-1983), though
moving the end of the early period as early as 1981 or as late as 1985 does not materially change the results.
Based only on the patent data in the graph, it might appear that the turning point for the emergence of the
dominant technology was closer to 1981, and I have tested models with that starting point that have
demonstrated not significant changes in the overall results.
-- INSERT FIGURE 1 ABOUT HERE -This setting is an especially appropriate one to test the hypotheses outlined above for four specific
reasons. First, this setting features multiple technologies and real uncertainty. Firms expended research
resources on many potential flat panel technologies, including LCD, plasma, EL (electroluminescence), FED
(field emitting device), EC (electrochromic), and OLED (organic light-emitting diode), though the first two
are the only ones that received significant investment in the early (pre-1983) period. This technological
uncertainty combined with some uncertainty about the consumer preferences that would drive the market
(since the dominant target market – laptop computers – did not actually exist initially) meant that firms faced
a truly uncertain technological landscape against which to make their resource allocation decisions. A
3
As discussed in more detail below, all quotes and historical details used in this paper come from primary interviews
conducted by the author with experts on the evolution of the flat panel display industry in the U.S. and Japan.
12
number of leading consumer electronics and computer firms made significant investments in plasma and
little investment in LCD initially, including IBM, Sony, Fujitsu and NCR. Each viewed this technology as a
possible technology for both computers (desktops and the nascent laptop market) and larger screens
(televisions, wall displays, etc.). This early uncertainty produced real heterogeneity in the investment
decisions of these firms, and suggests that the firms supporting one technology were generally similar to
those supporting the other in ways that may not be measurable in the data.
Second, despite this early uncertainty, this setting features clear technological winners and losers.
The evidence of the early dominance of LCD is discussed above. All of managers that I spoke with
confirmed that, despite the resurgence of plasma displays in the late 1990s and 2000s, they viewed these
early plasma investments as failures, and that this early research produced practically no commercially viable
products. One manager stated that “we realized pretty early that [plasma] just didn’t make much sense any
more,” while another manager called his firm’s early plasma investments “worthless.” The majority of firms
abandoned significant research in plasma displays for most of the mid- to late-1980s, with only Tsutae
Shinoda (Fujitsu) and Larry Webber (Plasmaco, a startup that purchased assets from IBM) continuing to
pursue large-format, full-color plasma technology. Thus, the later resurgence of plasma displays (1990s and
2000s) can be seen as a separate event from the early research in plasma. Even so, the recent reemergence of
plasma is high-profile, but still only a small fraction of the overall flat panel market. Display Search, a
leading flat panel display market research firm, projects that the 2008 market shares (units) for televisions
will be 47% for LCD, 46% for CRT, and 6% for plasma – and this in the television market where plasma is
strongest. Display Search also noted that virtually every laptop screen was LCD, more than half of all cell
phones sold had LCD screens (and those without did not have full screens), and about 80% of new desktop
computer sales came with LCD monitors (with CRT monitors controlling the remainder). Thus, it seems
quite reasonable to suggest that LCD established and maintained its position as the dominant technology, at
least from 1983 through 2007.
Third, this setting presents a situation with non-obvious outcomes. If the early period of
technological uncertainty was very short and/or firms generally did not make any significant technology-
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specific investments during this period, then the expected effects of a failure experience would be negligible.
Conversely, if the winning technology was not clearly established until after a significant period of research
and capital investment, then it might be impossible for firms to recover. In this situation, there was a
significant period of uncertainty (1965-1982) with investment in both research and rudimentary
manufacturing in multiple technologies by a number of firms, but the dominant technology emerged before
the significant ramp-up period in patenting and large-scale manufacturing.
Finally, this context presents certain practical data availability features that are appealing. Patenting
data in each of the various technologies provides the opportunity to distinguish between activity in each
technology. Obviously, program-specific research & development spending would be a more ideal measure
for this research (Henderson & Cockburn, 1994), but such data are rarely available (due to the large number
of diversified firms in the market). The production of LCD displays (for televisions, laptops, and monitors)
provides a long-term measure of the outcomes of these early research decisions. There are other important
features of this setting that provide some ability to untangle the mechanisms at play, but these will be
addressed later.
Data Sources
To measure the technological activity of firms in each flat panel technology I rely on patent data
drawn from the Derwent Innovations Index, covering every flat panel display patent granted between 1965
and 2005, and every firm listed on those patents (more than 6,000 firms and 500,000 patents). There are three
specific reasons why Derwent offers specific advantages that would not be available with the USPTO
database. First, given the global nature of the market with players from EU (Philips & Thomson), US (IBM,
Planar, NCR, Texas Instruments), Japan (Sharp, Sony, Matsushita, NEC), Korea (LG, Samsung), Taiwan
(AU Optronics, Chi Mei Optronics), drawing data from the USPTO alone would not provide an adequate
picture of the market over time – Derwent is a superior source of international patent data. Second, Derwent
groups patents into patent families, where identical patents filed in multiple offices around the world are
actually consolidated into a single patent family to prevent double counting (my dataset has more than
500,000 patents organized into about 250,000 patent families). All analyses done in this paper are based on
14
patent families and not on individual patents. Finally, Derwent has custom-created detailed technological
categories beyond what any government patent office utilizes, which allows me to distinguish between
patents designed for different technological purposes (LCD, plasma, EL, FED, EC, OLED, etc.). For any
patents applicable to multiple technologies, I have assigned these patents to a “general flat panel
technologies” area (these patents generally focus on component parts such as glass and power supply.
USPTO data would not allow the fine-grained analysis necessary, as all flat panel technologies would be
grouped into “Television”, “Computer” and “Watch” categories without the necessary information on the
underlying technological architecture. For each model discussed below, I use a subset of this overall patent
family database limited by time windows and focusing on for-profit firms (excluding government
organizations, universities, individuals, etc.). Additionally, all references to dates for patents represent the
earliest application date for any patent in that patent family.
I supplement this patent database with two other sources of data. First, to assess long-term product
outcomes, I collected data on product value ratings for LCD product between 1987 and 2005. These ratings
are drawn from a number of sources (Consumer Reports, CNet.com, PC World, PC Week, Government
Computing News, and LCDTVBuyingGuide.com). I provide more detail on these data in discussing the
model to evaluate H4 below. Second, I conducted a number of detailed interviews with senior executives and
senior research managers with long histories in the flat panel display market, both in the U.S. and Japan.
Despite the fact that events that are the primary focus of this research took place more than 25 years ago, I
have conducted 15 interviews (averaging about 90 minutes each) with 18 individuals with personal
experience from at least 12 companies and secondary knowledge of at least 10 more (through personal
friends and colleagues). These interviews, as well as previous qualitative work (Matsui, West, & Bowen,
1997; Murtha, Lenway, & Hart, 2001), have helped provide important context and first-hand knowledge of
the decisions that firms made and why they made those decisions. All quotes and references to “experts” in
this paper were drawn from those interviews.
Variables, Analysis and Models
15
Since this research covers empirical models with three different samples and dependent variables (as
discussed in more detail below), I will present the detailed methods for the first analysis, and then discuss
what is different for each of the other analyses. These are not intended to be complete models of exit, R&D
resource allocation decision-making, or product value, but are instead intended to examine and demonstrate
the relationship (if any) between the theoretical independent variable of interest and the outcome variable. I
have attempted to control for as many relevant alternate explanations as possible, and feel confident that any
unmeasured variables that may affect the outcomes have no theoretical or logical reason to be associated
with the independent variable.
Model: Organizational Failure and Market Exit
Sample: I use a subsample of my overall flat panel patent database to construct my sample to assess
H1 (that firms with greater early research in the failed technology will be more likely to exit the market after
the emergence of the dominant technology). The sample contains every firm with at least three flat panel
patents over the course of the firm’s history (to reduce the potential effects of misclassifications), and runs
from 1965 to 2000. In all, this includes 1,422 firms, 363 of whom exit the market at some point. The overall
sample (firm-year observations) contains a total of 14,574 observations, based on roughly 10 annual
observations for each firm in the sample.
Dependent Variable: I measure exit from the flat panel display market by tracking the organization’s
patenting activity. As many of the firms in this market do not actually manufacture end-use products, and
much of the history of this market takes place during periods with little or no manufacturing, it is not
practical to measure exit by the cessation of manufacturing operations directly. However, to the extent that
firms are only likely to continue to patent in flat panel technologies if they remain actively involved in the
business, this measure provides an adequate estimation of organizational exit. I consider the firm as entering
the flat panel display market in the year of their first flat panel patent (in any technology) and exiting in the
year of their final flat panel patent. Given that the sample only runs through 2000 and I have patent data
through 2005, this means that firms coded as exiting have been inactive in patenting for at least five years.
16
Independent Variable: To measure the firm’s research activity in a failed technology, I use a count of
the firm’s patents in plasma technology between 1965 and 1982 (PRE-1983 PLASMA PATENTS), logged
for overdispersion. To account for the effect in different time periods (before versus immediately after the
emergence of LCD as the dominant technology), I interact this variable with dummies for UNCERTAINTY
PERIOD (1965-1982) and LCD EMERGENCE PERIOD (1983-1989), with the interaction with the omitted
third period as the baseline effect.
Controls: My control variables for this model address three primary concerns: that the firm’s
research activity before 1983 is endogenous and firm-specific, that firms may have differing levels of
available resources, and that firms may face differential selection conditions that impact exit. To account for
the firm’s overall pre-1983 activity in the flat panel market, I include similar counts of the firm’s activity in
LCD (PRE-1983 LCD PATENTS) and all other flat panel display technologies (PRE-1983 OTHER FPD
PATENTS), and I also interact these with the period dummies. Given that the order of entry into the new
market is likely endogenous (Lieberman & Montgomery, 1998), and that certain types of firms may be most
likely to under before the cessation of technological uncertainty (Mitchell, 1989), I include a dummy (LATE
(1983-2000) ENTRANT) noting whether the firm was a late (1983 or later) entrant into the flat panel display
market. Prior research has suggested that firms entering immediately before the establishment of a dominant
design may have higher rates of survival (Christensen et al., 1998), so I include a dummy (WINDOW) noting
whether the firm entered the flat panel market in the 5 years previous to the establishment of LCD as the
dominant technology (1978 to 1982). Since larger, diversified firms may have more resources available to
fund speculative flat panel research than startups focused on the flat panel market, I include a dummy for
firms having zero patents in any other field before its first flat panel patent (DE NOVO ENTRANT).
Because the selection environment and financial pressures facing firms may be different in different regions
of the world (based on economic growth and size, availability of financing, etc.), I measure the percent of the
firm’s patents that were filed in (a) the U.S. (AMERICAN FOCUS), (b) Japan (JAPANESE FOCUS), (c)
Western Europe (WESTERN EUROPEAN FOCUS), and (d) the rest of the world (omitted category – the
four variables sum to 1 for all firms). I include the AGE of the firm’s research operations in flat panel
17
products (the time since the firm entered the flat panel market), and its square. Prior research has strongly
suggested a liability of newness or of “middle age” for organizations (Freeman, Carroll, & Hannan, 1983;
Levinthal, 1991). Finally, as mentioned earlier, I include dummies for different time periods within the
model (UNCERTAINTY PERIOD and LCD EMERGENCE PERIOD). The interactions for these assess the
timing of the impact of investment in plasma, but the main effects address issues of time-varying selection
environment based on global macroeconomic conditions and the evolution of the flat panel market.
Model: Given the coarse nature of my exit measures (annual measures for events that may take place
at any time during that year), a time-aggregated discrete time model makes the most sense (Allison, 1982).
Thus, I use a logit model with dummies for the three time periods considered (UNCERTAINTY PERIOD
and LCD EMERGENCE PERIOD, with the third period as the omitted category). Using dummies for each
year (as opposed to larger time periods), or using a Cox proportional hazard model, in no way changes the
primary findings of this analysis.
Response to Future Technological Opportunities
Sample: To assess the competing hypotheses H2a and H2b (that firms with greater early research
activity in the failed technology will be either more or less aggressive, respectively, in responding to future
opportunities) and H3 (that these firms will be more aggressive in pursuing an opportunity based on the
originally failed technology), I assess the response to two different technological opportunities within the
evolution of the market – the emergence of LCD as the dominant technology (which should be the primary
focus of any firm seeking first-mover advantages) and the re-emergence of plasma during the 1990s. The
LCD opportunity is used to assess just H2a and H2b (whether early investment in a failed technology makes
firms more or less aggressive in responding to future opportunities), while the plasma opportunity is used to
assess these hypotheses as well as H3 (that these firms will be more aggressive in pursuing the return of the
same technology). For each opportunity, I have a separate multi-year panel model. The LCD panel runs from
1983 to 1992 (though the results are robust to moving the end of the panel one or two years in either
direction), and has 5,086 observations on 734 firms (each with at least three flat panel patents over the firm’s
history). The plasma panel runs from 1989 to 2000 – the primary “rediscovery” and ramp-up period for
18
plasma displays (again, the results are robust to movements of the beginning and end points of this panel).
The plasma panel, which is later in the market and includes many more late entrants, has 10,048 observations
on 1,308 organizations.
Dependent Variable: For each panel (LCD and plasma), the dependent variable is an annual count of
the number of patents the firm had in that specific technology. To the extent that the number of patents the
firm files for in a specific technology over time represents the degree of that firm’s involvement and activity
in the development of that technology within the market, this outcome measure provides an adequate
estimation of the organization’s response to these two distinct opportunities within the market.
Independent Variable: I use the same variable (PRE-1983 PLASMA PATENTS) for these models.
Control Variables: When selecting controls, my primary concerns were for the endogeneity of entry,
external resource constraints, path dependency, and differing strategic decisions of firms. As with the exit
model, there are concerns about the endogeneity of the entry decision, so I include the PRE-1983 LCD
PATENTS, PRE-1983 OTHER FPD PATENTS, and LATE (1983-2000) ENTRANT controls discussed
earlier. Given the time- and location-specific nature of resource availability and technological evolution, I
include the AMERICAN FOCUS, JAPANESE FOCUS, and WESTERN EUROPEAN FOCUS variables
discussed earlier, as well as year fixed-effects. I would also expect that there would be important factors
related to the organization’s size and available internal resources that may constraint choices. Unfortunately,
given the construction of my sample (including a large number of small and/or non-U.S. firms), I have been
unable to collect organizational size and profitability measures for the vast majority of the firms in my
sample. Given the fact that many of the firms that choose poorly and later fail based on those poor initial
choices may never have the chance to go public or achieve international notoriety, limiting my sample to
only those firms that are public and whose information is available during this period (at a time when very
few non-U.S. firms had ADRs providing easy access to financial information) could dramatically skew my
sample and my results. Therefore, I have attempted to control for these considerations within the limitations
of the data as best I can. The most important control that I use for this is a lagged dependent variable – the
number of patents the firm had in either LCD (LCD PATENTS (t-1) in panel 1) or plasma (PLASMA
19
PATENTS (t-1) in panel 2) in the previous year. With this control included, the results of the models can be
interpreted as the effect of the variable on the organization’s deviation from the overall expected trend, based
on their previous level of activity. This also allows for the possibility that smaller firms may have more
modest levels of patenting activity at the beginning of and throughout the panel, and keeps the effect of the
independent variable focused on the deviation from expectations. I also include the DE NOVO ENTRANT
dummy introduced earlier, which is also a rough proxy for organizational size.
Since alliance activity was an important part of the development of flat panel technologies and
allows firms to share the cost of development (theoretically freeing more resources for additional research), I
include a dummy (ALLIANCE OR PARTNER) noting whether the firm had a shared patent (assigned to
more than one firm in the sample) or a joint venture patent in the previous year. This measure of alliance
activity is actually preferable to traditional measures, as (a) it captures co-development activity that may or
may not be formally documented, and (b) it is specific to the flat panel market.There is also the possibility
that firms may choose to allocate more or fewer resources to a specific technology at any given time based
on their allocations to other flat panel technologies. In the LCD panel, I control for this with the count of the
plasma patents the firm applied for in the focal year (COMPETING PATENTS) – a measure of whether the
firm remained committed to plasma technology even after the emergence of LCD technology. In the plasma
panel, it is clear that the organizations decisions in this period will depend (in part) on what decisions the
firm made in the previous period. Thus, I include LCD PATENTS (1983-1988) and PLASMA PATENTS
(1983-1988) to address the extent to which path dependency and/or the aggressiveness of the firm’s response
to the dominant technology drives later behavior. The final control included in each model is a traditional
sample selection control (SAMPLE SELECTION), estimating the likelihood that the firm in question
actually appears in my panel based on the firm’s research activity in the previous period(s). See Appendix 1
for a more in-depth discussion of the sample selection models and the rationale for including this variable.
Model: As my dependent variable is a count variable and is highly over-dispersed, I use a negative
binomial model for the LCD panel. In my plasma panel, I have a large number (91%) of zero observations,
as many firms chose to “skip” the reemergence of plasma. Additionally, the decision of whether to pursue
20
plasma technology at all in this period and the decision of how much focus to place on plasma are related but
may have different relationships with my variables. The negative experience of investing in a failed
technology initially may discourage managers from pursuing that same technology again, but to the extent
that managers do make the decision to pursue plasma, the usefulness of the initial technological knowledge
and investment may encourage a more aggressive response to this opportunity. Therefore, this model is
constructed as a zero-inflated negative binomial model. In each case, it might make theoretical sense to
include firm-level random effects to address concerns about firm-level heterogeneity that may not have been
completely addressed with my controls. However, it is impossible to include both a lagged dependent
variable and random effects in a panel model (Baltagi, 2001; Greene, 1990/2003), due to concerns about the
fact that consecutive panel years for the same firm would have the dependent variable on the left-hand side in
one model and the right-hand side in the next, while still having the same endogenously created random
effect on the right-hand side in both models. Given the endogenous nature of many of the variables in my
panel, this would be a concern for practically any model with random effects in this format, but specifically
would apply to the lagged dependent variable. Models with random effects (with or without the lagged
dependent variable) generally did not converge, and I feel that the controlling for trend and path dependency
within the investment decisions of the firms in my sample makes the inclusion of the lagged dependent
variable the most important control.
Long-Term Product-Level Outcomes
Sample: To assess H4 (that firms with greater early research activity in the failed technology will
produce inferior products in the dominant technology later), I utilize independent expert ratings of LCD
products (televisions, monitors, and laptop screens) from a variety of manufacturers between 1987 and 2005.
My sample is based on the product ratings that I recorded (more on this below) and that I was able to assign
to a specific manufacturer (as some product brands, such as Apple and Dell, sourced from multiple suppliers
at the same time). In all, I collected 837 ratings of 694 products from 55 firms during the 19 year period.
Dependent Variable: This variable is based on the actual product ratings that I recorded from a
variety of sources (Consumer Reports, CNet.com, PC World, PC Week, Government Computing News, and
21
LCDTVBuyingGuide.com). As each source uses its own scale, I have normalized each rating so that the final
variable is expressed as the number of standard deviations above or below the mean for that source across the
entire panel. In cases where I have multiple ratings on the same product, I have averaged the ratings to ensure
only one rating per product. While every source’s exact rating criteria is unique, the following excerpt from
PC World’s website is indicative of the overall criteria used: “The PC World Rating is the overall rating for
a product, and results from the combined scores of four major component characteristics: features/
specifications, performance, design/usability, and price.” Thus, the ratings are not intended to capture such
strategic decisions as high quality/high cost versus low quality/low cost, but are meant to be comparable
across market segments as measures of product “value”. Product value ratings are an appropriate measure of
innovation outcomes because other measures of product performance (such as sales, profitability or market
share) are largely driven by important downstream complementary assets, such as brand name, marketing
capabilities, channel control, etc. (Nerkar & Roberts, 2004; Teece, 1986). It is not clear whether or how the
experience of investing in a failed technology in the early research stages would impact these important
complementary assets.
Independent Variable: I use the same variable (PRE-1983 PLASMA PATENTS) for these models.
Control Variables: There are four basic types of controls that I include: entry endogeneity controls,
complementary asset controls, sharing and learning controls, and selection bias controls. First, as there are
still concerns about the endogeneity of entry order and early research activity, I include the same controls
discussed earlier (PRE-1983 LCD PATENTS, PRE-1983 OTHER FPD PATENTS, and LATE (1983-2000)
ENTRANT). Second, some firms might have had important complementary experience that may be useful in
the flat panel display market. Noting that the majority of de alio entrants to this market came from closely
related product markets (televisions and computers), I include the DE NOVO ENTRANT dummy. To also
address this issue, I control for overall firm size through the (inflation-adjusted) net sales reported by the firm
for the year prior to the new product introduction (SALES), reported in billions of $US. Third, previous
research in the flat panel display market has indicated that the degree of knowledge sharing occurring in the
evolution of this market had a direct and positive relationship with firm performance (Spencer, 2000). I
22
control for this with the ALLIANCE dummy discussed earlier (though this version notes whether the firm
ever had an LCD alliance or partnership), and with the JAPANESE FOCUS variable, as Spencer (2000)
noted that Japanese firms were more willing to share information than their foreign competitors. Conversely,
sourcing or OEM (Original Equipment Manufacturer) relationships generally imply less information sharing
than internal development relationships, so for each product I have an OEM DUMMY noting whether that
product was produced and marketed by different companies. Fourth, I have significant concerns about
sample selection issues with this particular model. Specifically, my concern is that my earlier market exit
hypotheses suggested that those firms originally pursuing a failed technology would be more likely to exit
the market. Therefore, I would expect that this exit hazard would most strongly apply to the weaker firms in
that group, meaning that (by 1987) the only firms with significant early experience in a failed technology
would be “high type” firms that may be expected to produce superior products. To control for this, I include
a CUMULATIVE SELECTION HAZARD variable that is derived from the market exit model discussed
earlier. For more information on this control, see Appendix 1. Finally, I include year fixed effects to address
the expectation that product ratings may increase over time (as overall levels of product quality go up), and
firm random effects to address other remaining concerns about firm-level heterogeneity. With an exogenous
dependent variable such as product ratings, there are few concerns about the autocorrelation issues that
affected the earlier models.
Model: Since I have multiple observations for many firms (one for each product) and the data clearly
have a nested character, it makes sense to use a mixed multi-level model to assess the data. I use a
hierarchical model with random firm effects, and I also include the potential that the firm-level random effect
will be different depending upon the product category of the product (television, PC monitor, and laptop
screen). Given the heterogeneous backgrounds of the firms in my sample and their differing levels of product
development experience in each category, I allow this effect to vary across product categories.
Long-Term Product-Level Outcomes
Given the four different samples used in this paper, it is difficult to present useful summary statistics
of the data. A full disclosure would require four different full correlation tables, which are available from the
23
author upon request. Table 1, however, does present some key statistics in order to give a general flavor for
the data within the sample. There are a total of 1,422 firms in the overall sample, of which 233 entered before
1983. For those 233 firms, the average firm had 3.6 plasma patents before 1983 and 3.4 LCD patents before
1983, though the correlation between those two is only 0.22. This suggests that most firms had only a modest
level of activity in either technology (though the high standard deviations and maximums suggest that some
firms had significant levels of activity), and that there was little correlation between any given firm’s activity
in one technology and its activity in the other. The broader statistics show that, for the overall sample
approximately 60% of the observations come from firms that entered after 1982, and that 62% of
observations come from de novo entrants. There is also a strong focus on patenting in Japan in the sample
(JAPAN FOCUS has a mean of 0.50, versus 0.17 for both US FOCUS and EUROPE FOCUS), further
highlighting the need to use an international patent data source such as Derwent for this analysis.
-- INSERT TABLE 1 ABOUT HERE -RESULTS
Table 2 presents the results of the market exit analysis. First considering the control variables, the
full model suggests that, as expected, non-diversified firms without sufficient access to outside capital are
more likely to leave the market. Additionally, both late (1983 or later) and “window” entrants (1977 to 1982)
to the flat panel display market are more likely to exit than firms that entered during the early period of this
market. This finding generally supports Mitchell’s (1989) suggestion that the firms with the most at stake in
the new market are likely to be the earliest entrants. The regional variables suggest that Japanese firms are
less likely to exit, while U.S. (marginally significant) and European (highly significant) firms are more likely
to exit. This matches conventional wisdom about the market, where U.S. and European firms were largely
dominated by stronger Japanese and Asian competitors. The coefficient on FIRM AGE is positive and
marginally significant, indicating that older firms may be more likely to exit the market.
-- INSERT TABLE 2 ABOUT HERE -While early research activity in flat panel technologies other than LCD and plasma appears to have
no significant impact on exit, the findings on early LCD activity are as expected. The greater a firm’s
24
patenting in the emerging dominant technology during the early period of uncertainty, the less likely that
firm is to exit (across all periods, as indicated by the significant main effect and insignificant interactions).
These firms had developed extremely useful knowledge during the early period that provided a strong
incentive to continue to invest and remain in the market after 1983. The findings on plasma – the failed
technology – are somewhat different from expectations. H1 expected that the interaction between early
plasma patenting and the LCD EMERGENCE PERIOD dummy – the immediate post-uncertainty period –
would be positive, it is actually the earlier interaction that is positive and significant (p < 0.05). Thus, firms
are more likely to exit the greater their investment in a failed technology, but they are more likely to exit
before it has become clear to everyone that LCD is the dominant technology.
This finding contrasts directly with previous work on the emergence of dominant designs, where
firms began to exit after the clear winner was established (Klepper, 1996; Suárez & Utterback, 1995).
However, in the situations discussed in earlier research, the two competing technologies either were or were
assumed to be (for modeling purposes) equally technically viable, and the “winner” was determined by
consumer preferences or random events (Arthur, 1989). Thus, firms would receive no useful information on
the potential success or failure of their product until after both products had been launched in the
marketplace, and exit would be delayed until that point. In the case of flat panel displays, however, the
success of LCD and the failure of plasma were predominantly determined by technical considerations about
the number of technical concerns that would need to be addressed to bring either product to market in a
profitable format. There was initial uncertainty about the potential viability of these products, and so firms
chose to invest in one or both. Over time, researchers felt more and more comfortable that plasma technology
was more than a decade away from being able to support profitable, full-color displays, while LCD
technology was much closer. This difference in context and evolution is mirrored in the exit results – in this
setting, where firms were receiving potentially useful information on the shortcomings of plasma technology
even before the overall acceleration in interest in LCD, research activity in plasma technology encouraged
firms to exit earlier than in the dominant design models. While these findings suggest that investing in a
failed technology may be hazardous, they do not support H1.
25
Table 3 presents the results of the model assessing response to the establishment of LCD as the
dominant technology in the market between 1983 and 1992. For the most part, the control variables perform
as expected. The data exhibit strong path-dependency, as both the early LCD patenting variable and the
lagged dependent variable are positive and highly significant. Interestingly, however, the coefficient on the
contemporaneous investment in plasma (COMPETING PATENTS) is positive and highly significant. This
presents a picture of firms that are increasing or decreasing their level of activity in the entire flat panel
display market at the same time, regardless of technology. As expected, the results show that diversified
firms (with, presumably, greater resources available) were much more aggressive in their patenting activity
during this period, as the coefficient on the DENOVO ENTRANT control is negative and significant. Firms
with flat panel alliances also may have had more resources available for research, and that resulted in more
aggressive response to the emergence of LCD technology. The data also suggest that firms from developing
countries were more aggressive during this period than those from Japan, the U.S., or Western Europe. This
time period includes the entry and rise of the three primary Korean competitors in the market – Samsung,
LG, and Hyundai – all three of which remain important players to this day. In general, however, late entrant
(1983 or later) firms were marginally (p < 0.10) less aggressive in responding to the emergence of LCD. This
suggests that late entrants without some sort of national cost advantage may have been particularly
handicapped in this context. The independent variable supports H2b (p < 0.01), that increased early
investment in a failed technology limits that firm’s aggressiveness in pursuing new opportunities, at least in
the case of the emergence of the dominant technology. Thus, this model provides strong support for H2b and
no support for H2a.
-- INSERT TABLE 3 ABOUT HERE -Table 4 reports the results of the zero-inflated negative binomial regressions on the reemergence of
plasma technology in the 1990s. The “level” column reflects the coefficients predicting the level of annual
patenting for all non-zero observations, while the “zero-inflation” column reflects the likelihood that the
value will be non-zero in any given year. Thus, positive coefficients in either column indicate a higher level
of the dependent variable (either in degree or in terms of likelihood of being non-zero). Again, we see some
26
evidence of recent path dependency, as higher values of both the lagged dependent variable and the level of
plasma patenting between 1983 and 1988 are less likely to result in zeroes and more likely to result in higher
values. Firms with flat panel alliances are also less likely to have zeroes, but are no more likely to produce
higher patenting outputs. De novo entrants (with fewer resources available from outside businesses) are
likely to produce lower levels of plasma patenting, but are no less likely to patent at all, suggesting that a
number of de novo entrant firms may have attempted to carve out at least a small niche in plasma technology.
The regional variables also suggest that developing world companies were much more aggressive in their
pursuit of plasma technology than those from Japan, the U.S., or Western Europe.
-- INSERT TABLE 4 ABOUT HERE -The results for the independent variable of interest – PRE-1983 PLASMA PATENTS – are the most
interesting findings from this model. The positive sign (p < 0.01) in the “level” column indicates that firms
with greater pre-1983 research activity in plasma display technology were more aggressive in their response
to the reemergence of plasma technology as a viable alternative in the 1990s. However, the positive sign (p <
0.05) in the “zero-inflation” column tells the opposite story. The more a firm patented in plasma before 1983,
the less likely the firm was to patent in plasma at all in the later period. Thus, the early proponents of plasma
display technology were more likely to avoid plasma technology completely later, but those that did choose
to pursue it were aggressive in doing so. As mentioned when introducing this model earlier, it would make
sense that firms initially pursuing plasma technology may feel inclined to avoid reinvesting in that
technology completely, in a “once bitten, twice shy” reaction. Given the coefficient on the PRE-1983
PLASMA PATENTS variable in the zero-inflation regression, this perspective seems to be supported.
However, there are also signs of technological path dependence within the group of firms that choose to
pursue plasma in the 1990s, as the firms with greater early activity in plasma were more aggressive in their
activity in plasma later (on the condition that they pursued plasma at all). Given the large number of zeroes
in the sample (more than 90%), it seems clear that the negative reaction to the early plasma experience
dominated the technological path dependency of continued interest in plasma, suggesting that this model
provides the strongest support for H2b (that firms with greater activity in the failed technology will be less
27
aggressive in responding to future opportunities), no support for H2a (that these firms will be more
aggressive), and only minimal support for H3 (that firms will be more aggressive to respond to new
opportunities in the same technology).
Table 5 reports the results for the product value models. As expected, de novo entrant firms
produced inferior products, potentially because they lacked the experience of other firms in the end-product
markets (televisions and computers). Supporting the knowledge sharing arguments, the results also suggest
that Japanese firms produced (marginally) more valuable products. Most importantly, the CUMULATIVE
SELECTION HAZARD variable is positive and significant, supporting the notion that these firms that have
survived a history of “high risk” behavior produce better products, most likely because they were the
strongest firms initially. However, counter to H4, the coefficient on the independent variable is positive and
significant – the more a firm patented in plasma technology before the establishment of LCD as the dominant
technology, the higher the value of the LCD products they create and market later. Obviously, these results
provide no support for H4.
-- INSERT TABLE 5 ABOUT HERE -This finding – that the greater a firm’s research activity in plasma (the failed technology) in the early
(pre-1983) period, the higher the “value” ratings for their products in LCD (the dominant technology) years
or even decades later – is one of the more interesting outcomes in this research. Potential explanations for
this counter-intuitive result are worth considering. Firms making the wrong investment initially may develop
the requisite amount of organizational momentum towards the overall market (Miller & Friesen, 1980, 1982),
without necessarily being trapped by any first mover disadvantages. Firms that adopt the dominant
technology after experimenting with the failed technology bring some understanding of the technical
complexities of the market, without having become committed to any specific implementation of the
dominant technology. These early investments made by firms focused on the dominant technology from the
beginning can restrict a firm’s ability to properly adapt to the technological discoveries that have transformed
the dominant technology from just another competing concept to the winning idea (Markides & Geroksi,
2005). These firms have the ability to learn from the successes and failures of other firms in the market
28
(Miner & Mezias, 1996; Nathan & Kovoor-Misra, 2002), and may have enough knowledge of the overall
market (due to their decision to pursue a different technology in the market) to make the knowledge they
might gain from the activities of others more useful (Cohen & Levinthal, 1990, 1994). We certainly
understand the concept of first-mover disadvantages (Lieberman & Montgomery, 1998), and this appears to
be an example of just such a situation. Further work to understand the nature of this advantage for this
peculiar sub-class of early entrants would certainly be beneficial to our understanding of first-mover
advantages.
Looking at the Three Models Together – Sources of Inertia
Individually, each of these four models presents interesting results about the consequences of
research choices made during the early period of technological uncertainty. Additionally, these four
regressions together indicate important considerations for firms entering an uncertain technological market in
the hope of securing a first-mover advantage, and offer potential insight into the decision-making processes
in these firms during this period. The findings across the first three models – the exit model (Table 2), the
LCD panel (Table 3) and the plasma panel (Table 4) – all demonstrate the inertial behavior of the firms that
initially supported the failed technology. One interesting question is whether this inertia is based on rational
decision-making process (in which case the outcomes are largely driven by the luck or foresight exhibited by
firms in their initial selections), or whether there is another cause that may be more organizational in nature.
There are two plausible rational explanations for this inertial behavior exhibited by these firms that initially
pursued plasma technology – a lack of available resources and effective entry barriers erected by early
movers. However, neither of these two seems consistent with the data or the details of the empirical setting.
In the case of the flat panel display market, there were few (if any) profitable LCD products on the
market before the early 1990s, and thus the firms making the earliest investments in LCD would not have
had additional capital available to fund further investment in flat panel technologies based on their revenues
from existing products. Instead, all firms needed to rely on speculative investment by the larger organization
and/or outside sources of capital to fund their investments. Firms also did not appear to find themselves
splitting their flat panel R&D budget between multiple technologies, as we would expect that the coefficient
29
on the concurrent plasma investment in Table 3 would be negative and significant if that were the case (as
firms would be committing less to LCD as they committed more to plasma). Instead, the coefficient is
positive and significant, suggesting that firms actually increased their research activity in the entire flat panel
display market at the same time. Thus, to the extent that limited resources are to blame for the inertia
exhibited here, those resources were limited by decisions from investors (internal or external), and not from a
lack of capital on hand. Conversations with executives and research managers from various firms within the
market generally confirmed this perspective, as none of the people with whom I spoke suggested resource
constraints as a plausible reason for a firm’s lack of response during this period.
In reference to potential entry barriers, there are two such technological entry barriers discussed in
the literature – intellectual property protection and learning curve effects. The establishment of proprietary
intellectual property might prevent firms from aggressively pursuing the emerging opportunity (Gilbert &
Newberry, 1982; Reinganum, 1983). In the case of the flat panel display market, this seems highly unlikely,
as there were comparatively few flat panel display patents in LCD technology before 1983, and certainly not
enough to create effective patent thickets to deter later entry (Shapiro, 2001). Prior to 1993 there were fewer
than 1,000 LCD patents applied for by all players in the market, as compared to the more than 17,000 applied
for in the following 10 years. Additionally, the majority of the pre-1983 patents were in monochrome (black
and white) technologies, which might produce useful knowledge but that market insiders pointed out would
not be useful from a protective IP perspective.
The second potential entry barrier is related to the technological difficulty of switching from the old
to the new technology and catching up with the early movers in the market. This is a learning curve effect
and may result in the production of inferior products long-term (Arthur, 1989; Lieberman & Montgomery,
1988). There are multiple reasons to doubt the effect of this potential explanation. First, the inclusion of
lagged dependent variables in the panel models means that the results focus on the year-to-year changes in
research activity for these firms, and firms beginning the period at significantly higher or lower than average
levels of LCD research activity (for whatever reason) are not evaluated just upon those initial values. To the
extent that being technologically behind the learning curve puts firms that initially backed a failed
30
technology at a lower point, but has less of an effect on the incremental increases in investment over time,
this is accounted for in the model. Second, the LCD panel model (Table 3) controls for the firm’s early (pre1983) LCD research activity. This controls for the presence or absence of potentially requisite technological
capabilities at the start of the panel, which may have a significant impact on how quickly the firm is able to
locate and assimilate new knowledge (Cohen & Levinthal, 1990). Third, potential entry barriers based on the
technological difficulty of switching from one technology (plasma) to another (LCD) do not appear to be an
issue, as evidenced by the plasma opportunity model (Table 4) demonstrating the higher levels of initial
activity in a failed technology leads reduces the likelihood of pursuing even a technological opportunity
based on the same technology. Thus, it appears that the cause of this inertia is organizational and not based
on technological difficulty. Many insiders pointed out the relative ease of buying or acquiring knowledge
during the early period of this market for those that did not already have it in-house, either through alliances,
joint ventures, or licensing, or through the hiring of employees and other more “nefarious” means. These
insiders pointed to the rapid rise of Korean firms like Hyundai, Samsung, and LG, even though they had little
or no technological background.
Two specific elements of the results suggest an organizational mechanism for this inertia. First, the
early activity in the failed technology seems to limit the choice to pursue additional opportunities even in the
same technology. The “level” regression in Table 4 suggests that firms may have had a technological
rationale to leverage the knowledge created during the initial uncertain period, but the “zero inflation”
column suggests that there may be organizational motivations that limit the pursuit of those opportunities – a
“once bitten, twice shy” phenomenon. Second, the product-value model reported in Table 5 demonstrate that
firms initially pursuing plasma technology but later introducing LCD products do not go on to produce
inferior products. This suggests that these firms are not doomed from the start, and that any supposition by
managers that their firm would be unable to catch up to pioneers of the dominant technology appear to be
unfounded. Managers may have believed this to be a calculated and rational decision, but theses findings
suggest that this might not have been a clear rational response.
31
Finally, the interviews that I have conducted strongly support the idea that risk-averse managerial
decision-making – driven by the experience of having invested in a failed technology – was driving the
inertial behavior. I will offer three specific examples. First, one R&D manager discussed the increased
internal “legitimacy hurdles” that he faced in validating LCD technology to a group of executives that had
already seen their firm’s plasma investment fail completely. His group’s requests for funding for LCD
research were subjected to the additional question of, “Why is this investment not like last time?” This firm
was able to navigate this hurdle and reorient its flat panel operations, but many other firms may not have
been as successful in overcoming this hurdle. Other firms, like Polaroid (who retreated back to its camera
and film business) and AT&T/Bell Labs (who retreated to its core telephone service operations), chose to
leave the market completely in the wake of a significant failed investment, in part because senior
management was unwilling to allocate the necessary resources to make the project work. Second, a manager
in a different firm related how the parent organization was willing to support the firm’s desire to switch from
plasma to LCD as a basis for its flat panel operations, but on the condition that the new head of the division
be an executive from the legal and finance group that was very reluctant to make significant risky
investments. The firm responded relatively slowly to the emergence of LCD, consistently lagging behind
other firms in terms of research and manufacturing knowledge, and eventually had to create a joint venture
with a competing firm in order to have better access to required knowledge and assets. Finally, multiple firms
discussed how the initial failure in plasma technology made managers question the firm’s ability to discern
potentially successful technologies from unsuccessful ones. These firms may have been aware of progress
made elsewhere in LCD technology, but were significantly less certain about whether these advances
actually destined LCD to be the dominant technology.
This suggests that managerial risk aversion may be the real source of the inertia exhibited by firms
that initially pursued a failed technology. It seems that senior managers may lose faith in the people
managing the flat panel investment research operations, and become less likely to follow their advice, either
directly by removing their autonomy or indirectly by fabricating additional hurdles to investment that limit
32
choices. These findings point out the importance for researchers in understanding how and where firms
placed their “bets” in the early uncertain period, and not simply whether they entered at all.
DISCUSSION
This paper set out for a deeper look at the organizational consequences of investing in a “failed”
technology. Firms seeking a technological first mover advantage by entering an uncertain market before the
clear establishment of a dominant technology face the difficult decision of how and where to allocate a
limited pool of R&D resources amongst the various opportunities. The results of this research demonstrate
that making the wrong initial choices can limit or eliminate any opportunity for first-mover advantage. The
significance of investing in a failed technology can lead firms to exit the market even before the dominant
technology is even clearly established (thus preventing the firm from ever responding to the dominant
technology), and prevent firms remaining in the market from properly responding to future opportunities in
the same market (including the emergence of the dominant technology and the resurgence of the failed
technology). However, for organizations with the financial and technical resources to mount a switch to the
dominant technology, the firm is in a position to create products in the dominant technology that the market
will value even more than those from firms that always backed the dominant technology.
As discussed earlier, perhaps the most important implications of this research are related to our
understanding of the creation of first-mover advantages in new market situations. Overall, the models
suggest that early entry in the hopes of positioning the firm to respond aggressively to the emergence of
future opportunities appears to be compromised to the extent that the firm invests research time and money in
the technology that winds up being a “failure”. These firms are more likely to exit, are less aggressive to
pursue the emerging dominant technology should they actually remain in the market, and are less likely to
pursue a future technological opportunity, even if it is based on the original failed technology. The findings
on exit suggest that firms initially supporting the failed technology may actually have been forced to leave
(or decided to leave) even before having the opportunity to respond to the emergence of the dominant
technology. This is counter to a real options logic that would suggest that there should be no relationship
between a firm’s research in a technology that later fails and its pursuit of the dominant technology. Instead,
33
a path dependency argument suggesting that there are indeed spillover effects from one investment that affect
the decision to pursue another investment appears to be validated. To the extent the presence of first-mover
advantages appears to vary greatly across industries and time (Agarwal & Gort, 2001), one reason may be
differing levels of early technological uncertainty. In situations where this uncertainty is very high (such as
the setting discussed here), researchers may have found little or no evidence of first-mover advantages
because too many firms were restricted from later action by their early unsuccessful choices. To the extent
that there is technological certainty even in the early stages of the market, first-mover advantages may be
more easily secured by early entrants.
These findings also suggest that studies evaluating the first-mover advantages obtained by early
entrants (in terms of market share, profitability, or some other measure) may actually be underestimating the
size of that advantage for those that obtain the advantage. While the empirical methods prior researchers
have used accept the fact that some firms may have greater first-mover advantages than others, these results
suggest that some early entrants – those that initially choose poorly – may have no first-mover advantage at
all, and those firms that choose properly (through luck or foresight or both) may have a much larger
advantage than earlier estimated. This finding adds an important layer of complexity to our understanding of
the process by which early entry may or may not result in first mover advantages.
One consideration is whether the differences in aggressiveness of response to the emerging dominant
technology demonstrated here between firms that originally backed a failed technology and those that
focused on the dominant technology from the beginning is significant. Using the coefficients from the LCD
panel regression in Table 3, I have graphed the predicted responses of two similar and hypothetical firms.
Each firm entered the market before 1982 and had similar characteristics (regional focus, being de alio
entrants, etc.). Both also applied for 10 LCD patents in the five years before 1983, including three each in
1982. However, one firm also had applied for 10 patents in plasma technology, as well. The results of this
exercise show that the predicted difference between the LCD patenting activities of the two firms in 1983 is
only about 1.5 patents. With the effect of the lagged dependent variable included however, Figure 2 shows
34
that the predicted difference quickly accelerates to almost 16 patents in 1992 alone, with a large and
exponentially increasing cumulative advantage for the firm without a prior investment in plasma technology.
-- INSERT FIGURE 2 ABOUT HERE -In addition to the contributions to the first-mover advantage literature offered by this research, the
findings here related to the origination of organizational inertia and path dependency also offer suggestions
about the role played by managers and critical decision-making in emerging technology situations. If, as
suggested by Adner and Levinthal (2004) and supported by this research, a key element to using real options
logic in R&D spending allocation is the ability to know when to stop supporting a project and limit the
negative spillovers from a failed project to a successful one, then managers play a key role in making those
important decisions and structuring their product development activities. Given the findings of the inertia of
firms backing the wrong technology, the stories of the firms that manage to make the transition to LCD and
the decision processes that they used become very important. My interviews suggested two ways that some
managers were better able to early investments in failed technologies. First, some firms seemed to have been
more focused on the “business problem” of developing flat panel displays (mostly for the purpose of
introducing laptop computers) instead of the “technical problem” of LCD, plasma, or other technology. This
made redirecting organizational momentum from one technology to the other more acceptable, as the
technological setbacks were seen as “bumps in the road” to solving the business problem. Second, some
firms were able to access more resources by working together through strategic alliances or joint ventures.
While firms like IBM and Toshiba (who formed one of the early market leaders, DTI Technology) discussed
how their joint venture allowed each firm to access capabilities and knowledge that the other firm possessed,
both admitted that the real reason the joint venture was necessary was to defray investment risk. Both firms
had been slower to recognize the emergence of LCD technology than key competitors (primarily Sharp and
Seiko, Hitachi and Matsushita), and sharing the speculative risk with another partner was a key way that
managers were able to obtain enough funding to make the project work. The findings suggest that the
impediments to adaptation may be erected by managers who are reluctant to continue escalating their
commitment to an uncertain market, but some managers are able to make good decisions about how to
35
proceed. This relates to the emerging speculation on dynamic managerial capabilities (Adner & Helfat, 2003;
King & Tucci, 2002), and raises the question of what types of managerial characteristics, outlooks, and
tactics allow some managers to help their firms successfully navigate through this process. This work offers
some qualitative suggestions for where to look, but more work is clearly needed.
The findings also raise an interesting question about market evolution. Despite the rise of plasma
TVs in the international market in recent years, this research demonstrates that those TVs have not (largely)
been produced by the firms that initially supported research in plasma in the 60s, 70s, and 80s. So where did
the firms come from that are the current leaders in plasma screen technology? How are they related to the
firms that did the pioneering work in plasma technology, and did the exit of those firms from research in
plasma (whether by organizational failure, abandonment of the market, or a switch to LCD) create an
opportunity for new firms to gain access to physical and knowledge resources that shaped the plasma submarket? How do the processes that create the “winners” in this technological niche market differ from the
ones that operate in the broader overall market? A detailed look at the evolution of this market may help
develop new knowledge about what happens to the knowledge generated from failed technological paths.
Finally, this study adds to the small body of research on failure at the organizational level. These
studies are valuable because studies in other areas of management and strategy generally focus more on
successes and less on failures. While only one study, this research demonstrates that researchers would lose
important granularity on which firm occupied which eventual position in the LCD market if we looked only
at entry order (first-mover advantages) or at research activity in LCD (the winning technology). Failure
events appear to have a significant and normally unmeasured impact on outcomes, and research that ignores
those failures does not properly capture the firm-level consequences of early and uncertain investment
choices. Future researchers on a variety of topics would do well to consider the potential for closely related
decisions (such as investments in contemporaneously evolving technologies and opportunities) that both
restrict their sample (due to exit decisions) or skew the later decision-making of firms.
36
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10(2): 302-328.
41
Figure 1 – The Emerging Dominance of LCD Technology
8,000
2,000
1,800
7,000
6,000
1,400
5,000
1,200
1,000
4,000
800
3,000
600
2,000
400
Plasma Patents
1,000
200
42
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
1966
1965
1964
Annual Patent Counts (Bars)
LCD Patents
Cumulative Patent Count (Lines)
1,600
Figure 2 – Time Series Patenting Curves for Two Hypothetical Firms (1982-1992), Based on Regression Coefficients
Firms are identical in history except for one firm’s early investment in plasma (since abandoned)
70
30
Cumulative Difference
(right axis)
Annual Predicted LCD Patenting (# Patents)
50
20
40
15
LCD-Only Firm
10
30
Mixed Plasma & LCD Firm
20
5
10
-
1982
1983
1984
1985
1986
1987
1988
43
1989
1990
1991
1992
Cumulative Difference (# Patents)
60
25
n (observations)
Pre-1983 plasma patents
Pre-1983 LCD patents
Pre-1983 other FPD patents
Late (1983+) entrant
De novo entrant
Alliance or partner
Japanese focus
American focus
Western European focus
Correlation between pre-1983 LCD and plasma for early entrants
Pre-1983 plasma patents for early entrants only
Pre-1983 LCD patents for early entrants only
Total firms in sample (1963-2000)
Early (pre-1983) entrants
Table 1: Brief summary statistics for data
St. Dev.
12.46
9.89
44
14,574
Overall
Mean
St. Dev.
1.7102
8.8269
1.6757
7.3503
2.3921
10.6123
0.6046
0.4889
0.6191
0.4856
0.3082
0.4618
0.5012
0.3988
0.1728
0.2205
0.1675
0.2338
0.2234
Mean
3.64
3.44
1,422
233
Max
109
81
5,086
LCD Panel (83-92)
Mean
St. Dev.
1.4343
7.7455
1.5083
6.8666
2.0464
9.4860
0.5869
0.4924
0.4446
0.4970
0.2354
0.4243
0.5325
0.3959
0.1632
0.2163
0.1850
0.2467
Min
-
10,048
Plasma Panel (89-00)
Mean
St. Dev.
0.7782
5.5764
0.8662
5.3599
1.1127
7.2428
0.7744
0.4180
0.5914
0.4916
0.3194
0.4663
0.5352
0.3967
0.1600
0.2162
0.1396
0.2106
Table 2: Exit decision based on initial research activity in failed technology (1963 to 2000)
Model: Discrete time logit hazard model on exit from flat panel patenting, errors clustered on firms
Evaluates H1: The greater a firm’s research activity in a “failed” technology in the early, uncertain period, the
greater the likelihood of abandoning the market once the dominant technology is identified.
Model 1
Model 2
Pre-1983 plasma patents * Uncertainty period
Model 3
0.593 *
(0.257)
Pre-1983 plasma patents * LCD emergence period
-0.004
(0.433)
Pre-1983 plasma patents (ln)
0.071
(0.127)
Pre-1983 LCD patents * Uncertainty period
-0.103
(0.156)
-0.494
(0.568)
Pre-1983 LCD patents * LCD emergence period
0.445
(0.338)
Pre-1983 LCD patents (ln)
-0.325 *
-0.371 *
(0.150)
(0.187)
Pre-1983 other FPD patents * Uncertainty period
-0.428
(0.463)
Pre-1983 other FPD patents * LCD emergence period
0.086
(0.371)
Pre-1983 other FPD patents (ln)
Late (1983-2000) entrant
"Window" (1978-1982) entrant
De novo entrant
Japanese focus
American focus
Western European focus
Firm age
Firm age squared
N
Clusters (firms)
Failure events
1.991 ***
1.937 ***
2.064 ***
(0.307)
(0.325)
(0.341)
1.225 ***
1.299 ***
1.418 ***
(0.299)
(0.338)
(0.337)
0.584 ***
0.542 ***
0.540 ***
(0.126)
(0.130)
(0.540)
-0.719 *
-0.725 *
-0.714 *
(0.303)
(0.312)
(0.304)
0.679 ^
0.657 ^
0.664 ^
(0.376)
(0.375)
(0.378)
0.991 **
0.975 **
0.986 **
(0.352)
(0.351)
(0.354)
0.053 *
0.051 *
0.046 ^
(0.053)
(0.022)
(0.024)
0.001
0.001
0.001
(0.001)
(0.001)
-0.123
-0.162
0.096
(0.373)
(0.384)
(0.407)
-0.380 ^
-0.391 *
-0.535 *
(0.200)
(0.200)
(0.224)
-6.060 ***
-5.952 ***
-6.063 ***
(0.439)
(0.457)
(0.469)
(1,618.4)
(1,616.0)
(1,610.7)
14,574
14,574
14,574
1,422
1,422
1,422
372
372
372
^p < 0.10
*p < 0.05
**p < 0.01
standard errors listed in parentheses below coefficient estimates
***p < 0.001
LCD emergence period dummy (1983-1989)
Log Likelihood
0.196
(0.231)
(0.001)
Uncertainty period dummy (1965-82)
Constant
0.100
(0.181)
45
Table 3: Consequences for response to emergence of dominant technology (LCD) (1983-92)
Model: Panel negative binomial model of effect of prior patenting on patenting in LCD
Evaluates H2a [H2b]: The greater a firm’s research activity in a “failed” technology in the early, uncertain period,
the more [less] aggressive the firm will be in pursuing new technological opportunities within the same market.
Model 1
Model 2
Pre-1983 plasma patents (ln)
Model 3
-0.281 **
(0.096)
Pre-1983 LCD patents (ln)
0.304 **
(0.117)
Pre-1983 other FPD patents (ln)
0.191 ^
(0.105)
LCD patents (t-1, lagged DV)
Competing patents -- plasma (t, ln)
Alliance or partner (t-1)
Late (1983+) entrant
De novo entrant
Japanese focus
American focus
Western European focus
Sample selection
0.056 **
(0.020)
0.365 ***
0.455 ***
(0.103)
(0.105)
0.869 ***
0.512 ***
0.536 ***
(0.097)
(0.090)
(0.090)
-0.655 **
-0.369 *
-0.266 ^
(0.197)
(0.154)
(0.160)
-0.187 ***
-1.122 ***
-0.820 ***
(0.165)
(0.159)
(0.219)
-1.885 ***
-1.179 ***
-0.833 *
(0.533)
(0.315)
(0.361)
-1.595 *
-1.335 **
-1.550 ***
(0.671)
(0.433)
(0.424)
-3.476 ***
-2.084 ***
-1.771 ***
(0.613)
(0.413)
(0.438)
6.822 ***
3.930 ***
1.969 ^
(0.912)
(0.639)
(1.131)
-3.372 ***
-1.672 **
-0.765
(0.813)
(0.547)
(0.682)
<included>
<included>
<included>
(6,940.0)
(6,743.0)
(6,718.9)
Constant
Year fixed effects
0.061 **
(0.020)
Log Likelihood
N (observations)
N (firms)
5,086
734
5,086
734
5,086
734
^p < 0.10
*p < 0.05
**p < 0.01
***p < 0.001
standard errors listed in parentheses below coefficient estimates
46
Table 4: Consequences for response to niche technology opportunity (plasma) (1989-2000)
Model: Panel zero-inflated negative binomial model of effect of prior patenting on patenting in plasma
Evaluates H2a [H2b]: The greater a firm’s research activity in a “failed” technology in the early, uncertain period,
the more [less] aggressive the firm will be in pursuing new technological opportunities within the same market.
Evaluates H3: The greater a firm’s research activity in a “failed” technology in the early, uncertain period, the
more aggressive the firm will be in pursuing the same technology for a specific niche.
Table 3: Investment Consequences for Response to Opportunity in Plasma
Zero-Inflated Negative Binomial Model of Effect of Prior Patenting on Investment in Plasma (1989-2000)
Level
Model 1
Zero Inflation
Pre-1983 plasma patents (ln)
Pre-1983 LCD patents (ln)
Pre-1983 other FPD patents (ln)
Plasma patents (t-1, lagged DV)
Plasma patents 1983-1988 (ln)
LCD patents 1983-1988 (ln)
Alliance or partner (t-1)
Late (1983+) entrant
De novo entrant
Japanese focus
American focus
Western European focus
Sample selection
Constant
Year fixed effects
Log Likelihood
N (observations)
N (non-zero observations)
N (firms)
Level
Model 2
Zero-Inflation
0.259 **
-0.252 *
(0.097)
(0.124)
0.230 ^
-0.137
(0.122)
(0.134)
-0.418 **
0.337 *
(0.127)
(0.138)
0.053 **
1.045 ***
0.052 **
1.054 ***
(0.018)
(0.140)
(0.017)
(0.138)
0.558 ***
0.374 **
0.466 **
0.439 ***
(0.126)
(0.109)
(0.147)
(0.126)
-0.091
0.112 *
-0.064
0.102 ^
(0.072)
(0.056)
(0.071)
(0.058)
0.119
0.354 ***
0.166
0.325 **
(0.191)
(0.097)
(0.190)
(0.096)
-0.088
-0.209
-0.222
-0.097
(0.250)
(0.165)
(0.247)
(0.186)
-0.548 *
-0.142
-0.564 *
-0.121
(0.226)
(0.113)
(0.226)
(0.113)
-1.520 ***
-0.094
-1.673 ***
-0.018
(0.399)
(0.174)
(0.375)
(0.176)
-2.362 **
0.148
-2.448 **
0.198
(0.782)
(0.352)
(0.0769)
(0.351)
-2.792 ***
0.043
-2.802 ***
0.004
(0.726)
(0.337)
(0.754)
(0.340)
-0.236
0.974
0.319
1.658 ^
(1.011)
(0.880)
(0.938)
(0.809)
0.041
-1.368 ^
-0.189
-1.397 *
(0.913)
(0.738)
(0.883)
(0.704)
<included>
<included>
<included>
<included>
(3,928.3)
(3,913.3)
10,048
944
1,308
10,048
944
1,308
^p < 0.10
*p < 0.05
**p < 0.01
***p < 0.001
standard errors listed in parentheses below coefficient estimates
Note: To improve interpretability of the coefficients, I have reversed the reported signs on the zero-inflation coefficients, so
that a positive coefficient in this table indicates a higher likelihood of being non-zero
47
Table 5: LCD product value implications of initial activity in failed technology (1987-2005)
Model: Hierarchical model, products nested in firms, random intercepts & random slopes based on product category
Evaluates H4: The greater a firm’s research activity in a “failed” technology in the early, uncertain period, the
lower the firm’s ability to produce products in the dominant technology that the market values highly.
Model 1
Model 2
Model 3
Pre-1983 plasma patents (ln)
Model 4
0.184 *
(0.083)
Pre-1983 LCD patents (ln)
-0.008
-0.069
(0.114)
(0.115)
-0.003
-0.112
(0.110)
(0.118)
-0.213
-0.219
-0.215
(0.210)
(0.218)
(0.225)
Pre-1983 other FPD patents (ln)
Alliance dummy (t-1)
Late (1983+) entrant
De novo entrant
Japanese focus
Cumulative exit hazard (t-1, ln)
Sales (t-1, ln)
OEM dummy
0.278
0.131
0.072
(0.202)
(0.283)
(0.284)
-0.493 *
-0.442 *
-0.453 *
(0.200)
(0.211)
(0.221)
0.264
0.453
0.531 ^
(0.190)
(0.304)
(0.311)
0.007 *
0.007 *
0.007 *
(0.003)
(0.003)
(0.003)
0.006 *
0.006 *
0.002
(0.003)
(0.003)
(0.003)
-0.015
0.017
0.036
0.035
(0.147)
(0.135)
(0.138)
(0.138)
-0.507
-0.661
-0.584
-0.571
(0.639)
(0.673)
(0.712)
(0.711)
Year fixed effects
<included>
<included>
<included>
<included>
Firm random effects
<included>
<included>
<included>
<included>
(923.5)
(922.5)
(925.0)
(924.3)
Constant
Log Likelihood
N (observations)
N (firms)
694
55
694
55
694
55
694
55
^p < 0.10
*p < 0.05
**p < 0.01
***p < 0.001
standard errors listed in parentheses below coefficient estimates
48
Appendix 1 – Rationale for selection effect models and the use of those models
In the LCD panel, plasma panel, and LCD product value regressions reported here I include a selection
effect control. While the value of each control is different (as each model tests a different time period and
uses a different sample), the rationale for the inclusion of this control is the same in each case. Over the
evolution of this industry, it is possible to subsegment the overall population of firms based on the
patenting activity of those firms. Some firms had greater or lesser numbers of patents overall, some firms
entered the market at different times, and some firms pursued different technologies. Each of these
subsegments faced different selection pressures. The results of these heterogeneous selection pressures is
that, by the time we reach later stages of the industry’s development, the only remaining firms in some of
these subsegments may be “high type” firms (in ways that have nothing to do with the primary focus of
this paper). The most likely scenario is described in the table below, where firms initially supporting LCD
technology (whether they are inherently high or low type firms) face a weak selection environment, as
their initial bet was the right one. On the other hand, firms supporting plasma technology initially may
face a more stringent selection environment. This environment may encourage low type firms to exit the
market, while high type firms may remain in the market. Under these circumstances, there is a high
likelihood during any subsequent period in the industry that a firm who initially supported plasma
technology but is still in the industry is a high type firm, while we would have no such information about
firms initially supporting LCD. While this is the most likely scenario, it is not the only one. To the extent
that there is any correlation with my primary independent variables and the selection environment faced
by those firms (whether the relationship is positive or negative), a selection effect is warranted.
While the exit pattern itself is endogenous to the primary independent variables discussed in this paper
and is of interest (thus Hypothesis 1 and the market exit models), the fact that some groups may be
comprised only of high type firms should be controlled for as best as possible. By including a selection
effect in each model, I am attempting to control for what knowledge I may have about the likely high
type/low type status of any given firm. While this is somewhat important for the discussion of the
response to the emergence of both LCD and plasma as technological opportunities in the same market,
this is especially important for the product value ratings regressions, where we would expect a strong
correlation between high type firms and high value products. In addition to the use of random effects in
the multilevel model used to assess product value, this selection effect is an attempt to control for this
high type effect.
I do not report the results of the selection effect models here, though they are available upon request. The
primary reason they are not included is that they are substantively similar to the exit model reported to
evaluate Hypothesis 1 about market exit. Thus, the results of these regressions are in effect already
discussed in the paper.
Initial LCD Focus
Initial Plasma Focus
High Type Firms
Low selection pressure
= in sample
High selection pressure
= in sample
Low Type Firms
Low selection pressure
= in sample
High selection pressure
= early exit
49