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’.
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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
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
8
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 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
Pre Gerstner Gerstner
Exploration
0
200
04
00
0
1975 1980 1985 1990 1995 2000Year
Pre Gerstner Gerstner
Patents
-.0
2-.
01
0
1975 1980 1985 1990 1995 2000Year
Pre Gerstner Gerstner
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
Pre Gerstner Gerstner
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
Technological Proximity
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
Technological Proximity
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.
16
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
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.
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
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
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
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
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.
38
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
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
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,
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
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. 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.
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
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. 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.