Escaping competition and competency traps: identifying how innovative

search strategy enables market entry*

Benjamin Balsmeier a, Gustavo Manso b and Lee Fleming b

a) ETH, Zurich, Switzerland

b) University of California, Berkeley, USA

December 2016

Abstract: Innovation is usually assumed to be a crucial component of firm performance, yet the

optimal strategy and progression from invention to performance remains unclear and poorly

identified empirically. Likewise the idea of a fundamental tradeoff between exploration and

exploitation has been extremely influential, however, the stages and causal linkages between

search strategy and performance have not been established. We first demonstrate that a variety of

simple patent based measures clearly load onto exploration and exploitation principal

components and illustrate the temporal relationship between exploration and new market entry.

To identify the effect of innovative strategy on entry and successful entry, we rely on exogenous

shocks that precede exploration (non-compete enforcement switch) and exploitation (anti-

takeover regulatory reform). Using these exogenous shocks with different and opposite

mechanisms but consistent effects on market entry, we isolate one pathway from invention to

performance and demonstrate how exploration enables market entry and increased sales in new

markets. Exploration strategies appear less effective

technology space; closeness in market space appears to have no effect on the impact of

technology strategy.

Keywords: Exploration, Exploitation, Patents, Innovation, Strategy, Market Entry, Experiment

* The authors thank Guan Cheng Li for invaluable research assistance. We gratefully acknowledge financial support

from The Coleman Fung Institute for Engineering Leadership, the National Science Foundation (1360228), and the

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Introduction

According to much popular press, we live in a knowledge economy and an age of innovation.

Academic research has only begun, however, to establish how the invention of technology and

innovative search strategy influences firm performance. Our current understanding of the

relationship comes mainly from economics, finance, and strategy. Griliches (1984) built a log

linear model of physical and intangible assets to estimate the value of a patent to be about

$200,000; Kogan and co-authors (forthcoming) built an event study that implies a median value

of $3.2 million in 1982 dollars. At the portfolio level, Hall, Jaffe, and Trajtenberg (2005) found

a positive correlation with and future citations; past innovative efficiency (as measured

by patents/R&D dollar, see Hirschleifer, Hsu, and Li 2013) or R&D performance (Cohen,

Diether, and Malloy 2013) predict abnormal returns. Firms search locally (Stuart and Podolny

1996) and enter markets more proximal to their current technological capabilities and experience

(Silverman 1991, Nerkar and Roberts 2004, Helfat and Lieberman 2002). Greater innovation

capabilities facilitate entry and competition decreases entry (de Figueiredo and Kyle, 2006,

Cockburn and MacGarvie 2011). Modularity aids innovation when not taken to extremes

(Schilling 2017); similarly, moderate exploration, as measured by text analysis of news articles,

correlates with financial performance (Uotila et al. 2009). At the risk of over simplification,

innovation appears to improve future performance, tends to build cumulatively on past

innovation and success

Research opportunity persists, however, in at least three areas. First, there is probably no single

path through applied research, product development,

manufacturing, marketing, distribution, sales, and ultimately financial success; many

contingent paths probably exist, some better than others, and successful journeys may well

branch and recombine a variety of intermediate strategies. Theory that jumps directly from

invention to performance misses this nuance, complexity, and variety of successful strategy

combinations. Second, much work proceeds empirically and regresses financial outcomes

directly on patent and citation counts. Better measures could capture intermediate richness and

outcomes and motivate more nuanced theory. Third, little work separates the endogeneity of

strategy choice from the impact of the choice. This is particularly important for a field where the

object of study is fundamentally wrapped up with numerous unobservable variables; while it is

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easy to download patent data and measures of firm performance, it remains very difficult to

observe and measure the decision processes and wealth of inside information that went into the

strategy process and choices.

We begin to address these issues by 1) focusing on the link between search strategy, market

entry, and performance, 2) developing a principal components method that operationalizes

measures, and

3) using two exogenous experiments to isolate and identify the impact of exploration vs.

exploitation strategies on new market entry and performance. First demonstrating how eight

basic measures of patent portfolios load 79% of their variance onto two components, we present

lagged regression models that illustrate the temporal relationship of innovative search strategy

and new product market entry (exploration correlates positively with entry, exploitation

correlates negatively, and both effects weaken with time). To strengthen causal inference, we

use two exogenous shifts, in labor markets and governance, namely, the passage of the Michigan

Anti-trust Reform Act of 1985 and anti-takeover regulations and (we refer to these as MARA

and ATO, respectively). MARA moved firms towards more exploration and ATO moved them

towards more exploitation. Their mechanisms were different; for MARA it appears that

inventors moved into new technical fields, both within and across firms (Arts and Fleming,

2016), and possibly because firms undertook more risky research and development (for evidence

of the effect from changes in Texas and Florida but not Michigan law, see Conti, 2014); for ATO

we conjecture that opportunities for selling the firm declined and that the market pressure for

novelty decreased. Despite differences in mechanisms, however, the result of exploration on new

market entry is consistent and strong; MARA induced a 0.202 point increase in the exploration

measure that resulted in a 42% increase in the propensity to enter a new market, while ATO

induced a 0.105 point decrease that resulted in a -14.9% decrease in entry. Sales in new markets

changed in a similar manner. While the exogenous push towards exploration appears to have

been less beneficial for firms which had been operating in more crowded technological space, it

appears to have been Results remain robust

across correlations, instrumental variables, and differences in differences models.

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Innovation Strategy and Performance

Invention and innovation are often modeled as a search process (March and Simon 1958). (Here

we adopt the typical convention of referring to raw patents and technology as invention and the

commercialization of such as innovation; search strategy encompasses the choices and processes

of both invention and innovation.) Individuals, groups, or firms search for novel and creative

solutions to problems or societal and market needs. Novelty is often defined as a new

combination of things, ideas, or processes (one can call these the components of recombination

see Gilfillan 1935, Schumpeter 1942, and Henderson and Clark 1990). To the extent that a

searcher combines familiar and well-understood and previously used components, they search

locally and exploit; to the extent that they use less familiar or previously unused components or

recombine them in new ways, they pursue distant search and explore. Exploitation is more

certain and likely to pay off and pay off sooner, though rewards may be smaller and incremental;

exploration is risky, more likely to fail completely or discover a breakthrough, and take longer to

bring to fruition. Local search is more accessible but ultimately often leads the searcher to a

competency trap and strands them on a local maximum (March 1991).

If this model of search is correct, it presents firms with a strategic and fundamental conundrum.

On the one hand, they can explore new areas of technology, for example, by hiring outside of

their current expertise, acquiring firms from new industries, and funding speculative projects that

seek breakthroughs in new areas. The reward to such a strategy will probably be a more skewed

distribution of outcomes, with a lower mean and more complete failures and breakthroughs. On

the other hand, the firm can stick to its knitting, refine current trajectories, and build on its

current expertise. The rewards to this strategy will be quicker and more assured successes, and

fewer completely failed projects, though also fewer breakthroughs. Ultimately it may also trap

the firm on a local maximum and competency trap.

While this model should generalize to a variety of search strategies, we focus on one obvious

path, namely, from invention to new market entry and competitive conditions for success with

that entry. Firms can enter new markets with a variety of strategies, for example, re-labeling an

existing product from an existing market, foreign expansion using extant products, superior

manufacturing and/or distribution, or acquisition, however, we focus on new technology based

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entry. The hypothesis is simple; firms that explore will develop new technologies that provide

the opportunity to build new products, differentiated at least from their current product line and

possibly This capability will enable and facilitate entry and

should be observed in an increased probability of entry and number of new markets that are

entered - assuming that managers see value and pursue such a strategy. If the strategy succeeds,

one would expect sales from ne in new

markets to increase. Exploration on average should move the firm further away from other

firms, as it enables differentiation with new to the world products; it is less likely that new

technology capabilities will precede follower entry strategies.

One would also expect reactions from competitors that would lessen the benefits of exploration

and market entry (Wang and Shaver 2016), especially when other competitors have similar

portfolios of technologies. (Stuart

and Podolny 1996; Aharonson and Schilling 2016) and the focal firm resides in a crowded

technological neighborhood, the appropriability and effectiveness of an exploration strategy will

decrease, for a variety of reasons. This occurs because competitors can understand and respond

to the exploration strategy more easily due to more similar absorptive capacities (Cohen and

Levinthal 1990). Knowledge transfer will be easier, from diffusion of patents, papers, and other

codified knowledge, and from personnel transfer, as poached employees can more readily

suffuse their prior knowledge from the exploring firms to competitors. As a result, we would

anticipate decreased efficacy for an exploration strategy on new market entry and performance,

for firms that pursue such a strategy from a crowded starting point in technology space. We

would also assume a negative effect of crowding in market space, defined as firms that operate in

a similar set of industries. We propose similar mechanisms, that firms with similar market

exploration strategy. We would also expect, however, that the market crowding effect would be

technical absorptive capacity that will enable and facilitate response.

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Data and measures

The empirical analysis is based on all public US based firms that field at least one patent in a

given year between 1977 through 2001 as identified in the NBER patent data. Data on basic firm

characteristics comes from Compustat North America and market entry and sales from

Historic Segment File. Detailed information on each patent provides the raw

observations for our reduced measures exploration and exploitation, are taken from the United

States Patent and Trademark Office, the NBER patent database, and the Fung Institute database

at UC Berkeley (Balsmeier et al. 2016). Based on the year of application of a given patent, we

aggregate our measures to the firm level of analysis. As patent based measures have no obvious

value in case of non-patenting activity, the sample comprises only observations when a firm

applied for at least one patent in a given year (as such, the results do not generalize to firms

without a patentable innovation strategy). Table 1 shows the distribution of firm-year

observations over the sampling period.

Table 1 Frequency count of firm-year patent portfolio observations. Year Frequency Percent Cum.

1977 718 2.97 2.97

1978 718 2.97 5.94

1979 747 3.09 9.03

1980 756 3.13 12.16

1981 765 3.17 15.33

1982 770 3.19 18.52

1983 763 3.16 21.67

1984 783 3.24 24.91

1985 831 3.44 28.35

1986 832 3.44 31.8

1987 855 3.54 35.34

1988 873 3.61 38.95

1989 844 3.49 42.44

1990 877 3.63 46.07

1991 937 3.88 49.95

1992 1,024 4.24 54.19

1993 1,106 4.58 58.76

1994 1,191 4.93 63.69

1995 1,348 5.58 69.27

1996 1,315 5.44 74.71

1997 1,347 5.57 80.29

1998 1,323 5.48 85.76

1999 1,232 5.1 90.86

2000 1,141 4.72 95.58

2001 1,067 4.42 100

Total: 24,163 100

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The increasing number of observations over time reflects the increase of patenting firms during

the sampling period. In order to limit selection in and out of the sample we require firms to be

observed at least 4 times (results remain robust to 2, 3, 5, 6 or 7 years as the threshold value).

The following eight patent portfolio characteristics are used to assess the direction of innovation

pursued by companies in terms of exploration and exploitation (further detail and

characterization of the data and measures are provided in Manso et al. 2016). Table 2 shows

summary statistics of these measures.

1. Number of patents that are filed in a 3-digit technology classes where the given firm has

never filed beforehand in that class.

2. Number of patents that are filed in a 3-digit technology classes where the given firm has

filed beforehand in that class.

3. Number of new technology classes entered where the given firm has never filed

beforehand in that class.

4. Technological proximity between the patents filed in year t and the existing patent

portfolio held by the same firm up to year t-1 (the normalized correlation between two

years of the proportion of activity in a given class, calculated according to Jaffe 1989).

5.

6. Number of prior art citations to patents held by the -

7. Number of claims a patent makes.

8. Average age of the inventor(s) mentioned on the patent document as calculated by the

database and the application year of a given patent.

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Table 2 Summary statistics patent portfolio measures

Variable N mean Median sd min max

Patents 24163 34.26 4 137.2 1 4054

New tech classes entered 24163 2.539 1 4.559 0 89

Patents in new classes 24163 3.016 1 6.183 0 185

Patents in known classes 24163 31.25 3 134.4 0 4051

Technological proximity 24163 0.541 0.581 0.328 0 1

Av. age of inventors 24163 3.633 3.063 3.131 0 26

Backward citations 24163 331.0 41 1387 0 48540

Self-citations 24163 42.55 1 280.6 0 11413

Claims 24163 528.5 66 2357 1 85704

Patent stock 24163 312.7 23 1279 0 34942 Notes: This table reports summary statistics of patent portfolio variables used in the study. Patents is the total number of eventually

granted patents applied for in a given year. New classes entered is the number of technology classes where a firm filed at least one

patent but no other patent beforehand. Patents in new/known classes is the number of patents that are filed in classes where the

given firm has filed no/at least one other patent beforehand. Technological proximity is the technological proximity between the

patents filed in year t to the existing patent portfolio held by the same firm up to year t-1, calculated according to Jaffe (1989).

Average Age of inventors measures

patent database and the application year of a given patent. Backward citations is the total number of citations made to other patents.

Self-citations is the total number of cites to patents held by the same firm. Claims is the total number of claims on each patent.

Patent stock is the sum of all patents held by a given firm up to the year t-1.

To reduce the dimensions of these data, we run a principal components analysis based on the

eight variables (similar results are obtained with a count based approach, or running a PCA at the

patent level). Two components have an eigenvalue above one, suggesting that extracting two

components are sufficient to explain the joint variation of the variables of interest. It supports

mapping the theoretical focus of exploration vs. exploitation onto two dimensions of innovative

search.

The output shown in Tables 4 to 5 indicate that 79 percent of the joint variation of the eight

patent variables of interest can be explained by these two principal components. In order to

optimize the factor loadings and reflecting the idea that exploration and exploitation are two

distinct dimensions of innovative search, we apply a Varimax rotation of the two extracted

components (results are robust to other orthogonal rotations). Table 4 shows the corresponding

results and Table 5 shows how much and in which direction each variable loads on the two

components. Loadings below 0.2 are not shown for easier comparability. Patents in known

classes, technological proximity, inventor age, backward citations, self-backward citations, and

claims all positively load on component 1, from which we label component 1 as .

The number of new technology classes entered and patents in new to the firm technology classes

strongly and positively load on component two. Negatively related to component two is the

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technological proximity and the age of the inventors. Consistent with characterizations that firms

are more likely to explore if we observe new technological areas, we label component 2 as

1

Tables 4 and 5 Principal Component Analysis

Component Variance Difference Proportion Cumulative

Comp1 4.02 1.72 0.50 0.50

Comp2 2.30 0.29 0.79

Notes: This table reports the results of a Principal Component Analysis after Varimax

Rotation. Only components with Eigenvalues above one are extracted. The 8 variables

that entered the PCA are: new classes entered, patents in new/known classes,

technological proximity, av. age of inventors, backward citations, self-citations, and

claims; all variables log-transformed.

Variable Comp1 Comp2 Unexplained

New tech classes entered 0.58 0.08

Patents in new classes 0.58 0.08

Patents in known classes 0.45 0.09

Technological proximity 0.39 -0.38 0.47

Backward citations 0.41 0.10

Self-citations 0.45 0.16

Claims 0.41 0.09

Av. age of inventors 0.31 -0.37 0.60

Notes: This table reports the results of a Principal Component Analysis after Varimax

Rotation. Only components with Eigenvalues above one are extracted. All variables

log-transformed. Variable definitions provided above.

Table 6 KMO test Variable KMO

New tech classes

entered 0.70

Patents in new classes 0.70

Patents in known classes 0.85

Technological proximity 0.86

Backward citations 0.92

Self-citations 0.89

Claims 0.89

Av. age of inventors 0.87

Overall 0.83

Notes: This table reports the Kaiser-Mayer-

Olkin (KMO) measures on sampling adaquacy.

All variables log-transformed. Variable

definitions provided above.

1 Measures of originality and generality (Hall, Jaffe and Trajtenberg 2001 - does the patent cite a wide variety of

classes and is it cited in turn by a wide variety) do not load on either of our components (neither at the firm nor

patent level). The measures do not map clearly to our theory; a patent could cite a wide variety of classes that had

could be citing a previously uncombined set of classes or a very commonly combined set of classes.

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The Kaiser-Mayer-Olkin measure of sampling adequacy, shown in Table 6, confirms that the

data can be summarized using a PCA analysis. The correlation between the two factors is 0.37.

While this correlation indicates that there are some firms working in areas that score high on

exploration and exploitation, the correlation is far from being perfect, implying substantial

independent variation. Figure 1 illustrates this by plotting the factor values of the exploration

component against the factor values of the exploitation component. Red lines represent the

median values of each component. In the multivariate empirical analyses below, the scores of the

exploration and exploration component, respectively, will be our main explanatory variables of

interest. In a simple robustness check (not shown) we find similar results when counting the

number of variables that score above or below the median value for each variable in a given year

(the score varies from 0 to +8, though the empirical range is 0 to +6).

Figure 1: Scatter Plot of PCA scores

Notes:

extracted from the Principal Component Analysis shown above. Red lines mark the

median values of each factor. 19% of the observations are each in the upper left and

lower right quadrants, 31% in each of the other quadrants.

reactions. In the current context of search this can be conceptualized and visualized as a

position in technological or market space (Stuart and Podolny 1996; Aghion et al. 2005,

Aharonson and Schilling 2016). The efficacy of a particular search strategy will depend on a

For example, if firms face competitors that are

-50

510

Explo

ita

tion

-4 -2 0 2 4 6Exploration

Exploitation vs Exploration Scores

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active in the same technological or market areas, it might be harder for those firms to realize the

benefits from exploration as it may be easier for close competitors to anticipate or follow search

success.

To assess technology space empirically we calculate pairwise correlations between a given

(1989). Specifically, we calculate the technological proximity TP between each firm i and j at

time t as:

where is the fraction of firm i -digit CPC patent class k at

time t. To detect firms that compete closely in technological space we counted for each firm in a

given year how many other firms are close in technological space as measured by a TP score

higher than 0.95 (results are robust to alternatively taking 0.9 or higher thresholds)

positions in market space are calculated similarly with sales generated in 3-digit sales classes

instead of patents filed in 3-

s in market space.