Escaping competition and competency traps: identifying … · Escaping competition and competency...

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 when the firm’s competitors are closer in

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

Ewing Marion Kauffman Foundation. Errors and omissions remain the authors’.

1

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 Tobin’s q 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, and must contend with competitors’ innovation.

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

path from an inventor’s inspiration through applied research, product development,

manufacturing, marketing, distribution, sales, and ultimately a firm’s 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

2

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

March’s (1991) exploration vs. exploitation strategy with easily available patent 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 unaffected by the firm’s crowding in market space. Results remain robust

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

3

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 from competitors’ product lines. 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 new markets to increase and the proportion of a firm’s sales 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. In other words, when firms are “close” in technology space (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

knowledge would be able to more quickly follow, react, or even anticipate the focal firm’s

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

weaker than the technology crowding effect, because competitor’s are more likely to lack the

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

Compustat’s 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. Number of prior art citations to other patents (‘backward citations’).

6. Number of prior art citations to patents held by the same firm (‘self-backward citations’).

7. Number of claims a patent makes.

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

time difference between the first time an inventor occurs in the Fung Institute’s patent

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 the average time difference between the first time an inventor occurs in the Fung Institute’s

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 ‘exploitation’.

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

‘exploration’.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

never been cited together before, or had been heavily cited together before. In other words, a highly ‘original’ patent

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

9

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: This graph plots the component scores of ‘Exploration’ and ‘Exploitation’

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.

The impact of any strategy obviously depends on competitors’ prior strategies, capabilities and

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

firm’s and its competitors’ positions in space. For example, if firms face competitors that are

-50

510

Explo

ita

tion

-4 -2 0 2 4 6Exploration

Exploitation vs Exploration Scores

10

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

firm’s patent portfolio and all other firms’ patent portfolios in a given year, following Jaffe

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

time t as:

𝑇𝑃𝑖𝑗𝑡 =∑𝑓𝑖𝑘𝑡𝑓𝑗𝑘𝑡

𝐾

𝑘=1

/ (∑𝑓𝑗𝑘𝑡2

𝐾

𝑘=1

)

12

∗ (∑𝑓𝑗𝑘𝑡2

𝐾

𝑘=1

)

12

where 𝑓𝑖𝑘𝑡 is the fraction of firm i’s patents that belong to the main 3-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). Competitors’

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

instead of patents filed in 3-digit CPC technology class. Figure 2 illustrates that firms’ positions

in technology space needs not to overlap with firms’ positions in market space.

0

.05

.1.1

5.2

Tech

Pro

xim

ity

0 .05 .1 .15 .2Market Proximity

Tech vs Market Space

11

Figure 2: Technology proximity vs. market proximity.

Figure 3 plots the exploration and exploitation scores of IBM over time, as well as the number of

patents and technological proximity of competitors. IBM appears to have begun the 1970’s with

an exploration strategy but this decreases over time in favor of exploitation. The time series of

the number of patents and exploitation look similar, and by themselves would miss IBM’s

variation in search strategy. Consistent with the idea that exploration is less predictable and

harder to manage we see larger variation of exploration scores over time as compared to

exploitation scores. The firm’s near demise in 1993 is obvious in the outlier on the left of the

figure, as is a move towards exploitation in the latter years of CEO Lou Gerstner’s tenure. The

firm has attracted more similar market competition over time.

45

67

8

1975 1980 1985 1990 1995 2000Year

Pre Gerstner Gerstner

Exploitation0

12

34

1975 1980 1985 1990 1995 2000Year


Exploration

0

200

04

00

0

1975 1980 1985 1990 1995 2000Year


Patents

-.0

2-.

01

0

1975 1980 1985 1990 1995 2000Year


Technological Proximity

IBM

12

Figure 3: IBM innovation search strategy 1979-2001. 1993 marked the firm’s “near death”

experience as well as its lowest innovative exploration.

Figure 4 illustrates the same graphs for General Electric. Consistent with Jack Welch’s

reputation, we do see greater exploitation and lessened exploration during his tenure and in

particular, a step increase in exploitation in the 6th year after he became CEO. Figure 5 illustrates

how Intel appears to be relatively unique in its ability to increase exploration and exploitation

simultaneously. In contrast to both IBM and Intel, GE appears to have developed a more unique

market profile over time.

0.0

2.0

4.0

6.0

8.1

.12

.14

.16

1975 1980 1985 1990 1995 2000Year


IBM Market Proximity

13

Figure 4: General Electric innovation search strategy 1979-2001.

55

.56

6.5

1975 1980 1985 1990 1995 2000Year

Pre Welch Welch

Exploitation

12

34

5

1975 1980 1985 1990 1995 2000Year

Pre Welch Welch

Exploration6

00

100

01

40

0

1975 1980 1985 1990 1995 2000Year

Pre Welch Welch

Patents

0

.05

.1.1

5.2

1975 1980 1985 1990 1995 2000Year

Pre Welch Welch


GE

0.0

2.0

4.0

6.0

8

1975 1980 1985 1990 1995 2000Year

Pre Welch Welch

GE Market Proximity

14

Figure 5: Intel’s innovation search strategy 1979-2001. Intel appears to be relatively

unique in its ability to simultaneously explore and exploit.

02

46

8

1975 1980 1985 1990 1995 2000Year

Moore Grove

Barrett

Exploitation

-20

24

6

1975 1980 1985 1990 1995 2000Year

Moore Grove

Barrett

Exploration

0

750

150

0

1975 1980 1985 1990 1995 2000Year

Moore Grove

Barrett

Patents

-.0

25

-.0

1.0

05

1975 1980 1985 1990 1995 2000Year

Moore Grove

Barrett


Intel

0.0

2.0

4.0

6.0

8.1

.12

.14

1975 1980 1985 1990 1995 2000Year

Moore Grove

Barrett

Intel Market Proximity

15

Comparison of Figures 3, 4, and 5 invite a number of insights. It is first important to note the

differing scales. For example, while Intel appears to be one of the rare firms that increased its

exploration over time, it also dipped into a negative value of exploration in 1984. This may

illustrate the pressure on its DRAM business and transition towards microprocessors. And Intel

has simultaneously increased its exploitation over time, from scores near zero in the 1970s to

scores near seven in the 2000s. IBM demonstrates half as much change over the same time

period and GE half again as much. Perhaps this illustrates the transition of Intel from a relatively

small startup in the 1970s to a dominant manufacturer; in contrast, IBM and GE have been large

and established firms over the entire time period. The technological proximity measure also

varies greatly between firms and appears to correlate most closely to patenting and exploitation,

though it is important to note that it reflects competitors’ search strategies as well. Perhaps most

interesting are the differences between the measures; exploitation seems to keep a firm in more

crowded neighborhoods and exploration the opposite – though not always, as Intel manages to

increase exploration and compete in a more crowded neighborhood. Crowded technological

neighborhoods appear to make commercialization more difficult, as the regressions below will

demonstrate. Finally, the market position of a firm, at least as defined by 3 digit SIC codes,

bears little correlation to the technical position.

IBM and GE appear to exploit more as they age, and prompt the question of whether this is

typical of most firms. Figure 6 illustrates the relation between firm age (years since first

appearance in Compustat) and exploration and exploitation scores, respectively. Consistent with

the organizations and population ecology literature (Hannan 1998; Sorensen and Stuart 2001),

organizations typically appear to exploit more as they age.

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Figure 6: Age and typical innovation search strategy.

Measures and outcomes: descriptions and correlations

We investigate how a firms’ innovation strategy influences product market entry and

commercialization success by assessing a firm’s likelihood of entering a new to the firm product

market, the number of markets entered, and the amount of sales in new markets. Financial data

comes from Compustat segment files for US public firms’ sales per 3-digit SIC industry class.2

We first consider a binary indicator if a given firm enters at least one new product market,

defined as the first time appearance of positive sales in a given 3-digit SIC industry where the

firm has not generated sales previously. Second, we measure the number of newly entered

industries, defined as the total number of industries where the firm generates sales for the first

time in a given year. The third variable is the total amount of sales generated in all new

industries where the firm did not generate sales beforehand.3 Table 3 provides descriptive

statistics on these variables (the number of observations reduces due to fewer availability of sales

2 Results are robust to considering 4-digit level sales data instead. 3 Compustat’s sales data come from firms which may not always be brake down generated sales by product

categories rather than geographical location. For the definition of entry in new product markets we just count each

time sales are generated in a new to the firm specific SIC code, regardless of whether the sales may have been

generated outside the US only. Further, firms often report sales data more than once year. We took the largest

number of sales reported in a given year for a given industry as often even the largest number does not capture all

sales a firm has generated in given industry and year. All results are robust to taking the average sales per industry

and year instead of the maximum.

-.5

0.5

11.5

2E

xplo

ratio

n/E

xp

loitation

facto

r score

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31Firm Age

95% confidence band Exploitation

Exploration

Fractional polynomial fit without log transformation

17

data). On average we observe that 9.7% of the firms in our sample enter a new to the firm

product market in a given year.

18

Table 7 – Summary statistics – firm level measures

Variable N Mean Median Sd Min Max

Exploitation 22897 0.00913 -0.349 2.014 -3.775 7.847

Exploration 22897 0.0210 -0.0719 1.516 -3.259 6.981

R&D int. 22897 0.0849 0.0376 0.176 0 9.753

log(age) 22897 2.124 2.303 0.865 0 3.466

log(total assets) 22897 12.61 12.49 2.252 3.807 20.02

Enrtry exp. 22897 1.098 0 1.843 0 15

HHI 22897 0.167 0.122 0.133 0.0296 1

Entry 0/1 22897 0.210 0 0.407 0 1

No. entries 22897 0.305 0 0.704 0 7

log(new sales) 22897 1.039 0 2.277 0 11.10

Prod. proximity 5800 0.630 0.602 0.161 0.142 1.231 Notes: This table reports summary statistics of firm level measures used in the study. Exploitation and exploration are the

component measures derived from the PCA described above. R&D int. is R&D expenditures divided by total assets. Age is the

number years since first time occurrence in Compustat. Entry 0/1 is a binary variable that indicates if a given firm enters at least

one new product market, defined as the first time appearance of positive sales in a given 3-digit SIC industry where the firm has

not generated sales previously. No. entries is the number of newly entered industries, defined as the total number of industries

where the firm generates sales for the first time in a given year. New sales is the total amount of sales generated in all new industries

where the firm did not generate sales beforehand. The latter three variables are measured as the sum over the years t+1 to t+3. Prod.

proximity is the median value of firms’ pairwise proximity scores based on textual analysis of firms’ 10k fillings by Hoberg and

Phillips (2015, 2010), multiplied by 100.

Transforming technological discoveries into new products takes time. Hence we consider years

one to three (all results are qualitatively robust to taking 2 to 4, 3 to 5 instead) after observed

patenting activity and product market entry. Specifically, we will regress the above mentioned

product market entry variables on the exploration and exploitation components observed one to

three years beforehand. With respect to the binary entry indicator variable we will use a new

binary variable as dependent variable that is one if a firm entered a new to the firm market in t+1,

t+2, or t+3. With respect to the number of industries entered and sales in new to the firm

industries, we sum up all sales generated in t+1 to t+3 and take the logarithm of it as the

dependent variable.

When the dependent variable is a binary indicator of new market entry we estimate a Probit

model instead of OLS.4 All regressions include controls for R&D intensity as measured by R&D

investment scaled by total assets, because more R&D intensive firms might be more inclined to

enter new markets. The logarithm of total assets controls for firm size as larger firms may find it

easier to diversify and enter new markets. The logarithm of a firm’s age addresses a potential

4 All results are robust to estimating a linear probability model instead.

19

focus on new markets after existing products have been exploited and the likelihood that firms

find exploration more difficult with age. Next, a sales-based Herfindahl Index measured at the

SIC 3-digit level enters the regressions to control for variations in competition across industries

as well as the logarithm of a firm’s patent stock (total number of patents accumulated over time).

Further controlling for firms’ capabilities and ability to enter new markets we add the logarithm

of the number of previously entered new industries plus one. Finally, a full set of industry and

year dummies control for heterogeneity of market entry rates across industries and time.

Table 8 – Correlations between Exploration/Exploitation and product market entry

a b c d

Dependent variable Entry 0/1 No. entries New sales Prod.

proximity

log(pat stock) -0.025* -0.004 0.001 -0.008***

(0.014) (0.003) (0.021) (0.003)

R&D int. -0.258 0.000 0.559*** 0.247***

(0.199) (0.029) (0.127) (0.025)

log(Age) -0.035* -0.011** -0.059** -0.019***

(0.019) (0.005) (0.028) (0.003)

log(Total assets) 0.065*** 0.022*** 0.267*** 0.013***

(0.014) (0.003) (0.020) (0.003)

Herfindahl ind. 0.229*** 0.046*** 0.214*** -0.030***

(0.029) (0.008) (0.051) (0.006)

Entry exp. 0.539** 0.109* 0.826** -0.078*

(0.222) (0.060) (0.355) (0.042)

Exploitation 0.005 -0.001 -0.001 0.006***

(0.012) (0.003) (0.017) (0.002)

Exploration 0.065*** 0.014*** 0.094*** -0.004**

(0.011) (0.003) (0.018) (0.002)

N 22897 22897 22897 5800

Industry and time fixed effects yes yes yes yes

R2 / Pseudo R2 0.134 0.150 0.202 0.498 Notes: All dependent variables are measured in t+1 to t+3. Model (a) is a Probit model where the dependent variable indicates if a

given firm enters at least one new product market, defined as the first time appearance of positive sales in a given 3-digit SIC

industry where the firm has not generated sales previously. Model (b) is an OLS regression of the logarithm of (no. entries + 1).

Model (c) is an OLS regression of the logarithm of (new sales +1), where new sales is the total amount of sales generated in all

new industries. Model (d) is an OLS regression of product proximity based on textual analysis of firms’ 10k fillings by Hoberg and

Phillips (2015, 2010), multiplied by 100. Patent stock is the cumulative number of patents applied for since 1976. Exploitation and

exploration are the component measures derived from the PCA described above. Heteroscedasticity-robust standard errors are

clustered at the firm level and shown in parentheses. ***, **, * indicate statistical significance at the 1%, 5%, 10% level,

respectively.

Table 8 demonstrates an exploration strategy is always statistically significant below the 1%

level and positively related to (1) a firm’s propensity to enter a new market, (2) the number of

20

new markets entered as well as (3) sales generated in new to the firm markets. In terms of

economic magnitude the results indicate that a one standard deviation increase in exploration is

associated with an 11.5% increase in the propensity to enter at least one new market in the next

three years and a 15.3% increase in sales generated in those new to the firm markets.

Consistent with these findings for exploration, we also find that exploitation correlates

insignificantly and in two out three cases negatively with new product market entry, the total

number of product markets entered, and the sales firms generate in those markets. This picture

stays qualitatively the same even if we consider the same product market entry measures

observed 4 or 5 years after the observed focus on exploration and exploitation (not presented).

Table A1a in the Appendix illustrates that all results are more pronounced in terms of statistical

significance and economic magnitude if only firm-years are considered when firms filed at least

10 patents in a given year and our PCA patent portfolio measure is based on a more solid basis.

In this setting we consistently find a significant negative relation between firms’ focus on

exploitation and market entry. Table A1b further shows that the results hold after controlling for

the number of patents, where the number of patents itself demonstrates only weak explanatory

power.

Figure 7 illustrates the temporal relationship between an increase in the innovative search scores

and the amount of sales generated in new to the firm industries (only for firms with at least four

patents in given year). Sales are calculated as three year moving averages starting with the first

three years after observation of firms’ exploration and exploration scores, respectively. Both

effects become weaker as the time from the search strategy to commercialization increases.

21

Figure 7: Temporal correlation between search strategy and sales in new industries.

Table 3, column d, shows regressions of Phillips and Hoberg’s (2015) measure of product

proximity between firms based on textual analysis of firms’ 10k fillings (the number of

observations drops because measure is only available for the years 1996 onwards, hence our

calculation of the SIC overlap). The measure ranges from 0 to 100 (rescaled), where 0 means

largest possible distance to other firms in the product market, while 100 indicates maximal

possible overlap of a firm’s products with its competitors’ products. Consistent with the previous

results we find that a focus on exploitation increases comparability with competitors in the

product market, while a focus on exploration helps firms to move away from their competitors

(results are again robust with longer time lags).

MARA as an instrument

Despite impressive uptake of the explore/exploit model of innovative search in the organizations

and strategy literatures, there has been little rigorous identification of the idea empirically or

causal evidence that connects exploration and exploitation strategies to performance. In order to

strengthen causal inference from innovative search to subsequent new market entry, we use the

Michigan Anti-Trust Reform Act (MARA) of 1985 and anti-takeover regulations (ATO).

MARA inadvertently made noncompete agreements enforceable and has been used previously as

-.25

-.15

-.05

.05

.15

.25

coe

ffic

ient

siz

e

1 2 3 4 5 6 7 8

Time to Entry

b-coefficients 95%-confidence-interval

Exploitation and Sales in New Industries

-.25

-.15

-.05

.05

.15

.25

coe

ffic

ient

siz

e

1 2 3 4 5 6 7 8

Time to Entry

b-coefficients 95%-confidence-interval

Exploration and Sales in New Industries

22

an instrument to study within state mobility (Marx, Strumky, and Fleming 2009), brain drain

(Marx, Singh, and Fleming 2014), human capital and acquisition (Younge, Tong, and Fleming

2014), and human capital and firm valuation (Younge and Marx 2015). Empirically it appears

that MARA increased both exploration and exploitation though the effect of MARA on

exploitation remains small and barely significant. We discuss why MARA might have both

effects but remain agnostic on exact mechanisms here, as our intent is only to isolate the impact

of strategy on commercialization outcomes.

MARA could arguably increase both exploration and exploitation. Because MARA decreased

the mobility of engineers (Marx, Strumsky, and Fleming 2009), firms’ work forces may have

become stale, as engineers stayed with current employers. This might have caused greater

exploitation if firms had previously depended on hiring for new ideas. MARA could also have

increased the influx of different ideas, because engineers that did move within Michigan had to

move farther from their former employer in technological “distance,” in order to avoid being

prosecuted for their noncompete agreement (Marx 2013). Engineers that moved within

Michigan after MARA therefore made more career detours into new areas and based on this,

they invented more novel patents (at the expense of decreased productivity, see Arts and

Fleming, 2016). Michigan firms may have also have performed more explorative projects given

the increased stability of their workforce, because firms might have been less concerned about

employee departure and competitor appropriation. Conti (2014) demonstrated such an effect

following noncompete changes in Texas and Florida but not Michigan.

Firms operating in Michigan are considered treated, while the control group comes from firms in

states that had similar laws as Michigan before and after the MARA law change. We estimate the

corresponding differences in differences (DiD) models based on firm data ranging from 1979 to

1993, i.e. six years before and after MARA. In a first step, the exploration and exploitation

measures are taken as dependent variables. Table 9, columns a and b, contain the corresponding

results for exploitation and exploration, respectively. Firms in Michigan scored higher on

exploitation and exploration alike, though the effect size is more significant for exploration and

almost three times larger. As such, we would expect to see increased product market entry by the

treated firms. We next run the same regressions with the previously used measures of product

23

market entry as dependent variables and presence inside Michigan after MARA as the treatment.

Consistent with a move towards exploration, all market entry variables are positively and

significantly related to the treatment interaction (Table 9, columns c, d, and e). This result also

holds when we alternatively identify the influence of exploration by an IV regression. In this

case model b serves as the first stage regression. Table 9, columns f, g, and h, present the results

of the second stage, i.e. ‘exploration’ are now the predicted values from model b that carry

exogenous variation caused by MARA. Again, we see a significant and positive influence of

exploration on all our market entry variables. The size of the coefficients in the IV and DID

models are comparable. Table 9, model e, indicates that firms subject to MARA increased their

sales in new to firm markets by 59%. The corresponding IV regression, Table 9, model h,

indicates an increase of 59.1%. The propensity to enter a new market increased by 42.0%

according to the DID model (c) and 32.3% according to the IV model (f).

It appears that Michigan firms took advantage of the increased focus on exploration with new

market entry and performance. One reason for the considerably large effect could be that the

treated firms increased their exploration at the right time, when good market opportunities

existed. Firms also simultaneously increased their exploration and exploitation. Increasing both

has often been suggested as a particular successful strategy (Tushman and O’Reilly 2004),

formally modeled through simulation (Fang, Lee, and Schilling 2010) and empirically confirmed

with patent citations by (Manso et al. 2016). However, the rather large magnitudes could also

point to an undetected estimation bias that led to an overestimation, for instance, because other

unobserved market entry enabling factors that are correlated with our exploration measure, e.g.

increased demand, are not perfectly controlled for. In order to replicate our findings we

investigated using the staggered imposition of anti-takeover laws as a second and also arguably

exogenous shock to firms’ exploration focus.

24

Table 9 – MARA experiment

a b C d e f g h DID DID DID DID DID IV IV IV

Exploitation Exploration Entry 0/1 No. entries New sales Entry 0/1 No. entries New sales

log(pat stock) 0.668*** 0.024 0.102*** 0.024** 0.163*** 0.050** 0.017 0.094 (0.051) (0.036) (0.030) (0.010) (0.040) (0.021) (0.011) (0.058)

R&D int. 0.794*** 1.007*** 0.743*** 0.158** 0.824*** -1.478** -0.136 -2.123* (0.080) (0.133) (0.234) (0.059) (0.200) (0.753) (0.103) (1.053)

log(age) -0.210*** -0.071* -0.159*** -0.025** -0.136** -0.003 -0.004 0.070 (0.029) (0.037) (0.056) (0.008) (0.043) (0.059) (0.010) (0.096)

log(total assets) 0.202*** 0.325*** 0.035 0.016* 0.224*** -0.681** -0.078* -0.726* (0.034) (0.019) (0.036) (0.009) (0.037) (0.271) (0.041) (0.374)

Herfindahl ind. -0.093 0.233 0.025 -0.124 -0.714 -0.488 -0.192 -1.395 (0.244) (0.378) (1.106) (0.201) (0.983) (1.262) (0.227) (1.194)

Entry exp. -0.167* 0.085 0.132 -0.010 -0.223 -0.054 -0.035 -0.470 (0.086) (0.054) (0.133) (0.031) (0.206) (0.169) (0.034) (0.274)

Exploitation 0.035* 0.004 0.011 -0.158* -0.022 -0.244* (0.021) (0.005) (0.033) (0.085) (0.014) (0.117)

MARA 0.061* 0.202*** 0.444*** 0.059* 0.590**

(0.031) (0.040) (0.163) (0.027) (0.235)

Exploration 2.205*** 0.291* 2.926**

(0.807) (0.134) (1.168)

N 3100 3100 3100 3100 3100 3100 3100 3100

Industry, Time and State FE yes yes Yes yes yes yes yes yes

R2 / Pseudo R2 0.735 0.436 0.279 0.297 0.343 0.279 0.299 0.348

Notes: Models (a) and (b) are OLS regressions of exploitation and exploration measures, respectively, as derived from the PCA described above. Model (a) and (b) are estimated

controlling for exploration and exploitation, respectively. Dependent variables of models c to h are measured in t+1 to t+3. Models c and f are Probit models where the dependent

variable indicates if a given firm enters at least one new product market, defined as the first time appearance of positive sales in a given 3-digit SIC industry where the firm has not

generated sales previously. Models d and g represent regression of the logarithm of (no. entries + 1). Models e and g represent regressions of the logarithm of (new sales +1), where

new sales is the total amount of sales generated in all new to the firm industries. Patent stock is the cumulative number of patents applied for since 1976. Heteroscedasticity-robust

standard errors are clustered at the state level and shown in parentheses. ***, **, * indicate statistical significance at the 1%, 5%, 10% level, respectively.

25

Antitakeover as an instrument

The staggered introduction of antitakeover laws by American states in the late 1980s and early

1990s had a surprisingly strong and unexpected influence on patenting and provides a second

natural experiment. Following Atanassov 2013, who found that these law changes led to a

reduction in overall patenting activity, we focus on the Business Combination laws that were

introduced in different years by most states (see also Bertrand and Mullainathan, 2003, who

analyzed the effect of antitakeover laws on corporate governance performance, and the years in

which each state introduced Business Combination laws). “Business Combination laws impose a

moratorium (3 to 5 years) on specified transactions between the target and the acquirer holding a

specified threshold percentage of stock unless the board votes otherwise before the acquiring

person becomes an interested shareholder.”5

In order to analyze the effect of antitakeover laws on exploration/exploitation and market entry

we run DID models where the treatment indicator is a binary variable that marks all years that a

particular state has had an antitakeover law in effect. Due to the staggered introduction of the

antitakeover laws, firms in states that eventually got treated can still serve as a control group. We

removed all firms situated in California and Massachusetts from the control group as these states

saw a huge increase in patenting activity at the same time many other states introduced their

antitakeover laws, which may lead to spurious correlations (Lerner and Seru, 2015). For our

empirical test we restrict the sample to the years 1981 to 1995, i.e. four years before the first

introduction and four years after the last introduction of a business combination law. State fixed

effects in all our regressions account for remaining time-constant unobserved differences across

States. As we also employ time fixed effects and basically estimate a classic DiD model. That

means under the assumption that firms in the control and treatment follow similar trends our

treatment variable “postBC” captures the causal impact of the introduction of the BC laws on the

dependent variable of interest.

Table 10 details the same regressions as previously used with MARA. First, we check the impact

of Anti-takeover law introduction on exploitation and exploration, followed by estimating the

5 Business Combination laws were arguably the most effective law changes that made takeovers harder or more

costly to carry out. Other less significant changes are reported in Atanassov (2013).

26

impact on our market entry variables. Next, we present IV regression results, where model b, our

exploration regression, serves as the first stage. Apparently, antitakeover regulation did not

affect firms’ exploitation focus (model a) but significantly decreased firms innovation search

towards exploration (model b). We conjecture that the decline in exploration is related to fewer

opportunities for selling the firm to competitors or other firms, and generally reduced market

pressure to present new discoveries that please investors’ (possibly biased, see Fitzgerald et al.

2016) attention on novelty.

As with MARA, we remain agnostic on the exact mechanism, and focus on the effect of

antitakeover regulation on market entry instead. Models c, d, and e, represent DID regressions of

market entry. Consistent with a decreased focus on exploration we see a significantly decreased

propensity to enter new markets, a significantly decreased number of markets entered, and

insignificantly decreased new market entry success as measured by sales generated in new to the

firm markets. In terms of economic magnitude model c implies a reduction in the likelihood to

enter a new market by -14.9%. This decrease stems from a reduction in the exploration score of

0.105 points. Hence, the magnitude of the effect seems to be broadly in line with estimations

based on MARA where firms were associated with an increase in their exploration score by

0.202 points and a corresponding increase in the propensity to enter a new market by 42.0%.

The IV regressions are consistent with these results. Model f suggests that an equivalent decrease

of 0.105 in the exploration score reduces the propensity to enter a new market by 15.4%. Effect

sizes are considerably smaller compared to MARA but still large in economic magnitude. While

this may still point to estimation issues it is reassuring to find consistent results across

experiments and increases the possibility that the identified influence of exploration on market

entry may be causal.

27

Table 10 – Anti-takeover experiment

a b c d e f g h DID DID DID DID DID IV IV IV

Exploitation Exploration Entry 0/1 No. entries New sales Entry 0/1 No. entries New sales

log(pat stock) 0.685*** 0.064*** -0.018 -0.003 0.006 -0.102*** -0.020** -0.068 (0.015) (0.023) (0.019) (0.005) (0.030) (0.036) (0.009) (0.062)

R&D int. 0.564** 0.335*** 0.194** 0.043** 0.329** -0.244 -0.045 -0.057 (0.246) (0.123) (0.087) (0.018) (0.126) (0.179) (0.047) (0.334)

log(age) -0.135*** -0.081*** -0.022 -0.009* -0.045 0.084 0.012 0.048 (0.021) (0.030) (0.023) (0.005) (0.029) (0.056) (0.014) (0.089)

log(total assets) 0.158*** 0.280*** 0.088*** 0.021*** 0.254*** -0.278* -0.053 -0.069 (0.016) (0.018) (0.017) (0.004) (0.026) (0.156) (0.038) (0.268)

Herfindahl ind. 0.449* 0.009 -0.104 -0.011 0.404 -0.116 -0.013 0.393 (0.225) (0.231) (0.484) (0.121) (0.722) (0.483) (0.121) (0.721)

Entry exp. -0.134*** 0.024 0.306*** 0.062*** 0.305*** 0.274*** 0.056*** 0.277*** (0.046) (0.042) (0.042) (0.010) (0.064) (0.045) (0.011) (0.066)

Exploitation 0.099*** 0.003 0.001 0.009 -0.126* -0.025 -0.104

(0.030) (0.017) (0.004) (0.029) (0.065) (0.016) (0.111)

postBC 0.035 -0.105*** -0.138** -0.028* -0.121 (0.050) (0.037) (0.060) (0.015) (0.103)

Exploration 1.308** 0.264* 1.152

(0.567) (0.138) (0.980)

N 9520 9520 9520 9520 9520 9520 9520 9520

Industry, Time and State FE yes yes yes yes yes yes yes yes

R2 / Pseudo R2 0.722 0.388 0.148 0.156 0.191 0.148 0.299 0.348 Notes: Models (a) and (b) are OLS regressions of exploitation and exploration measures, respectively, as derived from the PCA described above. Model (a) and (b) are estimated

controlling for exploration and exploitation, respectively. Dependent variables of models c to h are measured in t+1 to t+3. Models c and f are Probit models where the dependent

variable indicates if a given firm enters at least one new product market, defined as the first time appearance of positive sales in a given 3-digit SIC industry where the firm has not

generated sales previously. Models d and g represent regression of the logarithm of (no. entries + 1). Models e and g represent regressions of the logarithm of (new sales +1), where

new sales is the total amount of sales generated in all new to the firm industries. Patent stock is the cumulative number of patents applied for since 1976. Heteroscedasticity-robust

standard errors are clustered at the state level and shown in parentheses. ***, **, * indicate statistical significance at the 1%, 5%, 10% level, respectively.

28

The role of close competitors in technological or market space

The efficacy of any strategy depends on opponents (Cockburn and MacGarvie 2011). Here we

look for an interaction between exploration strategy and crowding or competitors’ positions in

“technology space” space (Stuart and Podolny 1996; Aghion et al. 2005, Aharonson and

Schilling 2016). If firms face competitors that are active in the same technological areas or

markets 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. Close competitors are more

likely to see the value of a firm’s exploration; they are also in a better position to hire away

engineers and/or marketing and sales people and compete more quickly and effectively.

Now we estimate our previously presented IV regressions based on MARA and the ATO

experiments, including a dummy that indicates close competition as measured by falling in the

highest quartile of the close competitors distribution (this applies to all firms that have more than

13 close competitors in technological space; results are robust to considering at least 10 close

competitors) and an interaction term between this dummy and firms’ exploration scores

instrumented by the respective regulatory changes of MARA and ATO. Similarly, we include a

dummy indicating close competition in market space (to stay consistent with the same threshold

of 13 or more close competitors) and an interaction term between this dummy and firms’

instrumented exploration score.

Table 11 shows that exploration has the previously identified positive influence on market entry

but that this positive effect is significantly reduced when firms face strong competition in

technological space. While individual coefficients of close competition, exploration, and the

interaction term are sometimes statistically insignificant, they are always jointly significant

according to χ2–tests (models a and d) and F-tests (models b, c, e, and f), respectively. As we are

looking at different sample compositions (different time, type and location of firms) it is not too

surprising that the sizes of the coefficients vary across models. While these differences in effect

size appear large they are not significantly different. We see no consistent impact of market

competition on innovation search strategies or their efficacy.

29

Table 11 – Exploration, market entry, and competition in technology and market space

a b c d e f

IV-MARA IV-MARA IV-MARA IV-AT IV-AT IV-AT

Entry 0/1 No. entries New sales Entry 0/1 No. entries New sales

log(pat stock) 0.028 0.014 0.081 -0.101*** -0.021** -0.078 (0.038) (0.014) (0.072) (0.034) (0.008) (0.059)

R&D int. -2.356** -0.246 -3.576** -0.224 -0.048 -0.119 (1.151) (0.145) (1.602) (0.188) (0.049) (0.345)

log(age) -0.045 -0.004 0.072 0.081 0.014 0.069 (0.052) (0.009) (0.089) (0.057) (0.014) (0.088)

log(total assets) -0.668** -0.079* -0.745* -0.292* -0.057 -0.082 (0.267) (0.036) (0.368) (0.165) (0.040) (0.278)

Herfindahl ind. -0.416 -0.185 -1.353 -0.127 -0.020 0.391 (1.176) (0.223) (1.169) (0.485) (0.120) (0.716)

Entry exp. -0.223 -0.069* -0.633* 0.237*** 0.048*** 0.246*** (0.192) (0.035) (0.296) (0.045) (0.010) (0.061)

Exploitation -0.140* -0.021 -0.238** -0.131* -0.026 -0.116 (0.082) (0.012) (0.105) (0.067) (0.017) (0.114)

Close tech comp. 0.461 0.053 0.649 0.155 0.038 0.111 (0.311) (0.051) (0.396) (0.172) (0.039) (0.270)

Close market comp. -1.005*** -0.182*** -0.791*** -0.421*** -0.078*** -0.209*

(0.171) (0.032) (0.223) (0.108) (0.020) (0.112)

Exploration 2.110** 0.299** 3.078** 1.372** 0.293* 1.377 (0.847) (0.131) (1.232) (0.608) (0.146) (1.028)

Close tech. comp. x Explor. -0.163*** -0.044*** -0.396*** -0.117* -0.029** -0.267**

(0.055) (0.010) (0.121) (0.067) (0.014) (0.113)

Close mark. comp. x Explor. 0.323* 0.007 -0.110 0.086 -0.009 -0.230

(0.179) (0.058) (0.468) (0.076) (0.018) (0.152)

N 3100 3100 3100 9520 9520 9520

χ2-test / F-test 178.86*** 95.99*** 23.19*** 28.25*** 4.97*** 4.29***

Industry, Time and State FE yes yes yes yes yes yes

R2 / Pseudo R2 0.281 0.299 0.348 0.150 0.157 0.193

Notes: All dependent variables are measured in t+1 to t+3. Models a and d are Probit models where the dependent variable indicates

if a given firm enters at least one new product market, defined as the first time appearance of positive sales in a given 3-digit SIC

industry where the firm has not generated sales previously. Models b and e represent regression of the logarithm of (no. entries +

1). Models c and f represent regressions of the logarithm of (new sales +1), where new sales is the total amount of sales generated

in all new to the firm industries. Exploration are the first stage estimated values based on MARA in case of models a, b, and c.

Exploration are the first stage estimated values based on the Antitakeover change in case of models d, e, and f. Close comp. is a

dummy that indicates if firms fall into the highest quartile of the distribution of close competitors in technological space as measured

by a pair-wise technological proximity score higher than 0.95. χ2-test (models a and d) and F-test (models b, c, e, and f) scores

belong to tests of joint of significance of the close competition, exploration, and the corresponding interaction term coefficients.

Heteroscedasticity-robust standard errors are clustered at the state level and shown in parentheses. ***, **, * indicate statistical

significance at the 1%, 5%, 10% level, respectively.

30

Methodological discussion

A caveat of our empirical identification strategy is that exploration might not be the only channel

through which firms increase market entry and entry performance. In the DiD setup this would

remain a problem of unobserved heterogeneity, i.e. unobserved variables that co-vary with

exploration and market entry alike. The IV setup can deal with unobserved heterogeneity, but the

validity of the instrument relies on the exclusion restriction being fulfilled, which we cannot test

as we have only one instrument per setup. In this respect it is particularly reassuring to see

consistent results in two quasi-experiments that move our main explanatory variable of interest –

exploration – in opposing directions, and finding that our main dependent variables of interest –

measures of market entry – move in the expected opposing directions, too. It increases

confidence in our findings because it is rather unlikely that two different regulatory changes that

affected exploration through different channels in opposing ways had the same opposing

influences on unobserved third factors.

These concerns remain endemic to the strategy literature, which faces a fundamental challenge in

establishing causality; it is difficult if not impossible to replicate the randomized lab experiment

in the field and equally, the randomized lab experiment cannot speak to the richness of the whole

phenomenon. We approached this challenge by finding two exogenous shocks that pushed

treated firms towards particular search strategies and then observed the impact of those strategies

on new market entry and success in that entry. Despite the consistent results from two quasi-

experiments, we remain concerned about the unobserved path from exogenous shock to success.

It is improbable that any executives in the treated firms recognized the possible impact of the

shocks on their search strategy at the time. This is even more likely if you accept the severe

uncertainty of research and development processes – executives rarely can predict or even

understand the output of their research labs. More likely, decision makers became aware that

their technology assets had changed; in the case of exploration, they might have realized that

they now had innovations that would enable new products and new market entry. This is not

unlikely, if one assumes that managers are typically on the look out for new product and market

opportunity. This is essentially the basis of our informal model, however, we must acknowledge

that the model rests upon these untested assumptions.

31

Patent data have all sorts of problems (Lerner and Seru 2015), however, one of their strengths is

that they allow relatively consistent comparison of inventions and portfolios across time, firms,

and industries. This allows us to include multiple industries in our analysis, and use entry and

sales into new industries to measure performance. Rather than defining and focusing on one

industry, the patent record enabled us to consider all patenting industries simultaneously and to

use financial data to observe market entry. Hopefully our relatively simple principal components

analysis, performed on readily available patent data, can be combined with additional

experiments in the future, and thus enable stronger causal inference.

Theoretical implications

Schumpeter’s “perennial gale” metaphor of creative destruction (Schumpeter 1942) spawned a

classic and central theme of the technology strategy literature; how do innovating incumbents

avoid obsolescence and irrelevance? The question reappears in many forms and the answer

rarely reassures the previously successful market leader. Incumbents rarely survive the era of

ferment (Abernathy and Utterback 1975), discontinuous and competency destroying change

(Tushman and Anderson 1986), developments outside their absorptive capacity (Cohen and

Levinthal 1990), seemingly simple architectural change (Henderson

and Clark 1990) and even incremental improvement (Christiansen 1998). Stuart and Podolny

(1996) and Sorenson and Stuart (2003) illustrate the phenomenon with patent data. While not

explicitly targeted at technology strategy, March’s “competency trap” image contributed another

appropriate and popular metaphor to the literature (March 1991). All of this work describes and

stresses the difficulty of successful and continuous innovation, of converting raw invention and

technology into successful products and profits.

The threat of competition is never absent from this genre, indeed, the biggest fear of any

incumbent is that someone else will invent a breakthrough that sweeps all others away. Under-

emphasized, however, is the interaction of others’ technologies with the focal firm’s search

strategy, and the difficulties that these interactions can cause the incumbent. In contrast, this

work demonstrates how the efficacy of a firm’s search strategy is strongly influenced by where

that firm sits in technology space. Firms in crowded neighborhoods appear to have a much more

difficult time in taking a technology from invention to innovation and commercial success. In the

32

parlance of landscape models of search, local hilltops are always difficult to move away from,

and especially when your neighbors are crowding your view.

Surprisingly, competitors with a similar market profile appear to have little effect on innovation

search strategy, though this might have resulted from reliance on the correlation in SIC code

distributions. Phillips and Hoberg’s (2015) measure of product proximity between firms based

on textual analysis of firms’ 10k fillings may be a closer measure to the idea of product

competition; the SIC measure is closer to a market distribution of profile similarity. Current

work aims to refine these measures of market and product competition.

One of the methodological strengths of this work is that it demonstrated consistent results from

the application of simple statistics to two quasi-experiments that exogenously nudged firms’

search strategies in opposite directions; this method enables and motivates new theory. Firms on

average tend to exploit more as they age and stop exploring. On the other hand, the principal

components analysis and illustrations clearly show that exploration and exploitation are not polar

opposites. Most intriguingly, MARA pushed firms to both explore and exploit and Intel appears

to be able to manage both. Furthermore, these strategies and their efficacy appear to be

uninfluenced by competitors who operate in a similar distribution of markets. These results

highlight the importance for continued theoretical development in how firms can balance

exploration and exploitation.

Conclusion

How does a firm’s technology strategy influence its commercial success? We contributed to

answering this question in a variety of ways. We began by reducing a variety of widely available

and common patent measures into two surprisingly complete principal components. These

components clearly loaded onto axes of exploitation and exploration and thus operationalized

March’s (1991) theory (given the simplicity of the statistical technique and the availability of

these measures for the past 20 years, we are surprised that this approach does not appear to have

gained traction yet). These components enabled us to identify and leverage changes in legal

regimes that exogenously varied firms’ search strategies. Armed with the component measures

and an exogenous shock, we could strengthen causal inferences about search strategy and

33

commercial success. In particular, we showed consistent results that exploration precedes new

product and market entry, that exploitation does the opposite, and that an exploration search

strategy is less efficacious when a firm’s patent portfolio correlates more closely with

competitors. In other words, and consistent with much of the literature, bold innovation strategies

are much more difficult when a firm starts from a crowded technological neighborhood.

The work speaks to three challenges in the technology strategy literature. First, it theorized

about one pathway (of many) from invention and innovation search strategy to commercial

success. Second, it offered measures, methods, and illustrations of this pathway. Taken

together, these demonstrated the pathway in greater detail and avoided jumping from patent

counts straight to profits or valuation. Finally, it applied two quasi-experiments that had

opposite effects on strategy but consistent effects on outcomes. These experiments enabled us to

strengthen causal inference.

The results suggest a number of follow on studies. The concepts and measures of technology

and market space need much refinement. Currently, the measures aggregate a firm’s entire

patent or market portfolio and calculate an overall correlation with other aggregated portfolios.

Especially for large and diverse firms, however, this measure obscures individual research

programs and interactions of individual programs. For example, GE owns patents in

photovoltaic and jet turbine technology, yet they probably have few if any competitors whose

portfolios also contain both areas (though Siemens perhaps might do so). While it will be

difficult to measure the commercial success of individual research programs, we can observe

some of the successful outputs of individual programs in the patent record. More nuanced

interactions between technology and market space can also be studied, though in the present

case, the negative effect of technological crowding illustrated does not appear to have been

influenced by the market.

The visualizations of exploration and exploitation suggest a number of studies. While Intel has

long been held up as a paragon of a well-run technology firm, the work illuminates specific

mechanisms of this success. In particular, how did Intel consistently simultaneously improve

both its exploration and exploitation? While at the same time, competing in a more crowded

34

market space? Ambidextrous organizations are certainly desirable (Tushman and O’Reilly 2004;

Fang, Lee, and Schilling 2010), but detailed and systematic evidence on how they are

accomplished might now be possible. The measures and modern visualization tools will enable

researchers to investigate how research and development portfolios evolve and how project

selection and management translate into the firm’s overall commercial success. Future research

should combine qualitative fieldwork with recent development in visualization and data

analytics. Now that we can clearly observe exploration and exploitation, it would be interesting

to find firms besides Intel that can do both, and more interestingly, to understand just how they

accomplish that.

35

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Appendix

Table A1a – Exploration/Exploitation and product market entry, at least 10 patents filed

a b c d

Dependent variable Entry 0/1 No. Entries New sales Prod. proximity

log(pat stock) 0.112*** 0.031*** 0.208*** -0.022***

(0.034) (0.009) (0.063) (0.005)

R&D int. -1.524** 0.011 0.988** 0.340***

(0.664) (0.067) (0.445) (0.045)

log(age) -0.046 -0.018* -0.133* -0.011**

(0.036) (0.010) (0.070) (0.005)

log(total assets) 0.107*** 0.034*** 0.354*** 0.011**

(0.031) (0.009) (0.056) (0.005)

Entry exp. -0.035 -0.021 -0.142 -0.042***

(0.052) (0.015) (0.109) (0.008)

Herfindahl ind. 0.007 0.009 -0.070 -0.029

(0.373) (0.121) (0.815) (0.078)

Exploitation -0.085** -0.025*** -0.124* 0.013**

(0.035) (0.009) (0.065) (0.005)

Exploration 0.078*** 0.019*** 0.161*** -0.013***

(0.022) (0.006) (0.045) (0.003)

N 7274 7274 7274 2009


R2 0.188 0.192 0.211 0.573 Notes: All dependent variables are measured in t+1 to t+3. Model (a) is a Probit model where the dependent variable indicates if a








respectively.

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Table A1b – Exploration/Exploitation and product market entry, at least 10 patents filed

plus control for number of patents filed.

a b c d

Dependent variable Entry 0/1 No. Entries New sales Prod. proximity

log(pat stock) 0.106*** 0.029*** 0.186*** -0.025***

(0.035) (0.009) (0.064) (0.005)

R&D int. -1.589** -0.004 0.788* 0.309***

(0.673) (0.071) (0.465) (0.044)

log(age) -0.044 -0.017 -0.124* -0.009**

(0.036) (0.011) (0.071) (0.005)

log(total assets) 0.100*** 0.032*** 0.330*** 0.007

(0.033) (0.009) (0.059) (0.006)

Entry exp. -0.035 -0.021 -0.140 -0.042***

(0.052) (0.015) (0.109) (0.008)

Herfindahl ind. 0.006 0.008 -0.077 -0.037

(0.371) (0.121) (0.810) (0.078)

log(patents) 0.067 0.019 0.248* 0.036**

(0.088) (0.021) (0.142) (0.015)

Exploitation -0.121** -0.035** -0.257*** -0.006

(0.060) (0.014) (0.098) (0.010)

Exploration 0.068*** 0.017** 0.127*** -0.017***

(0.024) (0.007) (0.048) (0.004)

N 7240 7240 7240 2009


R2 0.165 0.192 0.211 0.577 Notes: All dependent variables are measured in t+1 to t+3. Model (a) is a Probit model where the dependent variable indicates if a








respectively.

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Table A2 – Summary statistics – MARA sample


Exploitation 3100 -0.409 -0.743 1.851 -3.738 6.260

Exploration 3100 0.0428 0.0282 1.403 -3.053 5.144

R&D int. 3100 0.0849 0.0482 0.159 0 2.882

log(age) 3100 2.083 2.303 0.845 0 3.135

log(total assets) 3100 12.03 11.82 2.167 3.807 19.08

Enrtry exp. 3100 0.977 0 1.646 0 14

HHI 3100 0.167 0.125 0.115 0.0386 0.973

Entry 0/1 3100 0.193 0 0.395 0 1

No. entries 3100 0.288 0 0.703 0 7

log(new sales) 3100 0.922 0 2.123 0 10.21

Close comp. 3100 13.20 1 25.65 0 125 Notes: This table reports summary statistics of firm level measures used in the study. Exploitation and exploration are the






where the firm did not generate sales beforehand. The latter three variables are measured as the sum over the years t+1 to t+3. Close

comp. is the number of firms with a patent portfolio that correlates with a value of at least 0.95 with the given firm’s patent portfolio.

Table A3 – Summary statistics – Antitakeover sample


Exploitation 9520 -0.174 -0.543 1.948 -3.774 6.910

Exploration 9520 -0.00836 -0.0716 1.410 -3.259 5.169

R&D int. 9520 0.0573 0.0269 0.157 0 9.753

log(age) 9520 2.271 2.565 0.857 0 3.258

log(total assets) 9520 12.61 12.57 2.190 4.533 19.34

Enrtry exp. 9520 1.284 1 1.873 0 15

HHI 9520 0.192 0.143 0.140 0.0386 1

Entry 0/1 9520 0.199 0 0.399 0 1

No. entries 9520 0.278 0 0.648 0 7

log(new sales) 9520 0.992 0 2.203 0 10.79

Close comp 9520 14.77 1 34.40 0 229 Notes: This table reports summary statistics of firm level measures used in the study. Exploitation and exploration are the






where the firm did not generate sales beforehand. The latter three variables are measured as the sum over the years t+1 to t+3. Close

comp. is the number of firms with a patent portfolio that correlates with a value of at least 0.95 with the given firm’s patent portfolio.

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