DISCUSSION PAPER SERIESMarch 2014

                   No. 2014-04

A Systematic Assessment of First-mover Advantages
Rikiya Tsuchihashi
Tomomi Hamada

A Systematic Assessment of First-mover Advantages

Rikiya Tsuchihashi
Faculty of Economics, Nagasaki University
Tomomi Hamada
Graduate School of Economics, Nagoya University

Abstract
Many strategic management and marketing scholars have been occupied by the question
as to whether early entrants into a market achieve higher performance. Despite a wealthof research related to the hypothetical first-mover advantage (FMA), there has been
little consensus as to whether the FMA actually exists. To redress these inconsistencies,
we perform a systematic literature review to reconcile the contradictory perspectives
offered by the extant empirical literature. The 31 articles selected for analysis contained
261 statistical tests of the relationship between entry timing and performance. Of thesetests, 168 (64.4%) provided statistical support for the relationship between early entry
and performance, 61 (23.4%) were non-significant, and 24 (9.5%) significantly refuted
the presence of the FMA (i.e., they provided evidence for first-mover disadvantages).
Key words: first-mover advantage, systematic review, entry timing, performance,
moderating effects

11. Introduction
The question of whether early entrants achieve higher performance has occupied many strategic
management and marketing scholars. Since Lieberman and Montgomery (1988), muchtheoretical and empirical research has been conducted from many perspectives. Despite thewealth of first-mover advantage (FMA) research, there is little agreement about whether FMA
actually exists. Some scholars are skeptical, suggesting that FMA’s existence depends oncontingent factors. Golder and Tellis (1993) pointed out that empirical research misunderstood
FMA due to sampling bias, and Schnarrs (1986) insisted that FMA was eroded by imitation.
We conduct a systematic review to reconcile the opposing perspectives offered by theempirical literature. Different from a narrative review, the widely used systematic review selects
a sample for analysis systematically. Its advantage is its exclusion of the sampling bias seen in
the narrative literature (Chalmers and Altman, 1995; Tranfield et al., 2003).
Though developed in the medical sciences, systematic reviews have been applied in social
science and business management studies. For example, David and Han (2004) conducted asystematic review on transaction cost theory, and Newbert (2007), following David and Han(2004), also conducted a systematic review. Our study adheres to those works in its
investigation of FMA’s mechanism.

2. First-mover Advantage Theory
Lieberman and Montgomery (1988) identified three primary sources of FMA: (1) technologicalleadership, (2) preemption of assets, and (3) buyer switching costs. First-movers, they who canrun down the learning curve most quickly, can gain comparative advantage in cost competition.
They can also engage in early research and development to advance new technology andaccumulate technological skills and knowledge. Moreover, first-movers can preemptively
exploit valuable and/or scarce resources such as input factors, store or factory locations, or
customers’ cognitive positions. Finally, buyer switching costs often prevent buyers from2

selecting transaction partners other than first-movers.
First-movers also have liabilities. The first is the free-rider effect, in which first-movers
frequently have their innovations imitated at a lower cost. Second, first-movers unintentionallyreduce technological and market uncertainty for later movers, who then benefit from thestability. Third, changes in technology or customer demand led by other innovators would cause
the efforts of a first-mover in a corresponding market to come to nothing. Fourth, incumbent
inertia can weaken the first-mover’s advantage.
Since Lieberman and Montgomery (1988), many researchers have tested for the presence of
FMA using multiple perspectives and by investigating the empirical data. For example,
Szymanski, Troy, and Bharadwaj (1995) and Robinson, Kalyanaram, and Urban (1994) foundthat FMA exists after reviewing the relevant empirical studies. On the other hand, Vandewerf
and Mahon (1997) claimed that conclusions about FMA’s existence depend on the study’s
method of analysis. They pointed out that studies applying “market share” as an indication offirm performance tended to posit FMA’s existence more than did studies applying otherindications, such as survival rate or benefit rate. Additionally, Kalyanaram, Robinson and Urban(1995) also argued that FMA exists but with some conditions. They claimed that not everyone
could be a pioneer because acquiring FMA requires the appropriate technological skills,
knowledge, and resources. Thus, the reviews of empirical FMA studies from the 1990s make it
clear that FMA exists, but it depends on certain conditions and on certain factors. However,since 1995, when the last of these reviews was published, few studies have analyzed the resultsof empirical tests for FMA.

3. Data and Method
Following the methodology used by past systematic review studies (e.g., David and Han, 2004;
Newbert, 2007), we reviewed a series of empirical articles that test for the presence of FMA
and/or its conditions.

3

First, we searched the ABI/Inform and EconLit databases for journal articles using five
keywords presumed to match words in the titles or abstracts of relevant articles: “first moveradvantage,” “early mover advantage,” “pioneering advantage,” “entry timing,” and “entry
order.”
Second, we eliminated irrelevant articles by matching the initial results against the following
22 additional title and abstract keywords: EXIT, SURVIVAL, PERFORMANCE, PROFIT,
MARKET SHARE, MARKETING, RESOURCE, LEAD TIME, RESOURCE BASED VIEW,
EARLY MOVER, FOLLOWER, LATE*, SUSTAINAB*, ENVIRONMENT*, BRAND,ASSET*, BARRIER*, ENTRANT*, INDUSTR*, SECOND, DISADVANTAGE*, and
LAGGARD*. This step extracted the empirical articles that applied the variables and researchconcepts appropriate for our purpose.
Third, in order to ensure “empirical” content, we selected articles whose abstracts or content
included at least one of the following seven keywords: DATA, EMPIRICAL, TEST,STATISTICAL, FINDING*, RESULT*, and EVIDENCE.
Fourth, we screened the resultant articles based on the kind of journals in which they
appeared. By applying a method used in past systematic review studies, we excluded articles
that appeared in journals in which any other relevant article was not published in order to ensure
the articles’ relevance to FMA.
Fifth, we read the abstracts of the articles remaining after the above process and extracted
those that analyzed the relationship between entry order and performance. We then applied thesame extraction by reading the article contents. In the last step, we eliminated duplicate articles
that appeared in the results for both ABI/Inform and EconLit. Ultimately, 31 articles (25 from
ABI/Inform and 6 from EconLit) remained. We read and analyzed the results of these articles,organizing the information into tables.

44. Results
The 31 articles selected for analysis contained 261 statistical tests of the relationship between
entry timing and performance. Of these, 168 (64.4%) were statistically supported, 61 (23.4%)
were insignificant, and 24 (9.5%) were statistically significant in the opposite direction (i.e.,
showing first-mover disadvantages). Below, we break down our results by independent variable,
dependent variable, independent–dependent variable pair, industry, and independent variableinteraction.

4.1 Independent and dependent variables
Table 1 shows the independent and dependent variable categories. The upper side of Table 1
presents the independent variable categories, of which there are four: entry order, time lag,category,

andpioneer.

The

interaction

terms

are

entry

timing*resource,entrytiming*environment, and entry timing*strategy.
Entry order reflects a firm’s position in the market entry order. If a firm entered the market
first, then i=1; if a firm entered the market second, then i=2; and so on. Out of 27 tests, 24(88.9%) were statistically supported. For example, Jakopin and Klein (2012) analyzed the effectof entry order, market share, and profit rate using a sample of 191 mobile network operatorsfrom 49 countries. Out of six tests, six (100%) were statistically supported.
Time lag reflects the time that has elapsed after the first market entry. Some studies haveused a proxy for years, others months and weeks. Out of 56 tests, 46 (82.1%) were statisticallysupported, while 3 (5.4%) were significant in the opposite direction. For example, Nehrt (1996)
found a positive relationship between investment timing and profit growth, with four out of four
(100%) tests statistically supported.
Third, category was employed through a dummy variable assigned to each group ofelements: early entrants, early followers, and late entrants. Out of 30 tests, 18 (60%) were
statistically supported; there were no counter tests.

5Fourth, pioneer is a categorical variable taking 1 if a firm is a first entrant, and 0 otherwise.
This variable does not consider either the second or the third entrant: studies suggest that one
problem with empirical research on FMA is the ambiguity of the term “first-mover,” and wehave formulated this variable strictly to avoid such ambiguity. Out of 51 tests, 33 (64.7%) were
supported and nine (17.6%) were significant in the opposite direction.
The bottom of Table 2 shows the classification of the dependent variables. The main
variables used in the empirical research are market share, profit, and survival. For market share,a widely used dependent variable, 77 (84.6%) were statistically supported out of 91 tests, while
two (2.2%) were counter-supported. For profit, 27 (65.9%) were supported and two (4.9%)
counter-supported out of 41 tests. Finally, for survival, seven (41.2%) were supported, while
five (29.4%) were counter-supported out of 17 tests.

4.2 Main independent–dependent variable pairs
Table 2 shows the main independent–dependent variable pairs. We selected four major
independent variables (i.e., entry order, time lag, category, and pioneer) and three dependent
variables (i.e., market share, profit, and survival). The most common pair is that of time lag andmarket share; out of 18 tests, 15 (83.3%) were supported. Min and Wolfinbarger (2005) tested
the effects of a time lag on market share and found support for two (100%) of two tests. The
next common variable pair is the relationship between pioneer and market share; out of 15 tests,
15 (100%) were supported. Among the many test results available, no empirical test of therelationship between entry order and survival has been conducted. There is only one test ofentry lag’s effect on survival, which resulted in 100% support. These results indicate that, whilesome variable pairs were used in numerous studies, few variables have been observed in theliterature.

4.3 Results by industry
Table 3 presents the results by industry in four categories: service, manufacturing, entry into

6

foreign countries, and across multiple industries. The entry into foreign countries categorycomprises cases in which a host country firm tried to widen its business boundaries by entering
a foreign country. The across multiple industries category reflects cases in which researchers
selected not just one industry for their purpose but various industries, regardless of service or
manufacturing industry.
The across multiple industries group has the highest support rate: all 35 tests (100%) were
supported. For example, Murthi, Srinivasan, and Kalyanaram (1996) analyzed a sample drawn
from the PIMS database and found that three out of three (100%) tests showed a positive effectof pioneering markets on market share.
On the other hand, the service industry category has the worst support rate, with 38 (56.7%)
tests supported out of 67. The following industries were used: mobile telecommunications(Jakopin and Klein, 2012), on-line retailers (Sungwook and Wolfinbarger, 2005), and money
market/mutual funds (Makadok, 1998). The entry into foreign markets category also has low
support; 27 (57.4%) out of 47 tests were supported.

4.4 Interaction effect variable
Whether a first-mover obtains above-normal returns depends not only on the timing of entry but
also on the moderating effects of firm resources and environmental factors. In order toinvestigate moderating effects, we set three interaction term categories: environment, resource,and strategy.
First, as seen in Table 4, the interaction effect between entry timing and environment
supported six (60%) out of 10 tests. The environment category includes external context
variables such as GDP, industry competition, and industry growth. The interaction effectbetween entry timing and resource supported 21 (47.7%) out of 44 tests. The resource categorycomprises a firm’s internal factors, marketing capability, technological capability, pre-entry
experience, and high R&D. Finally, the interaction effect between entry timing and strategy
supported 13 (30.2%) out of 43 tests; strategy comprises the cost leadership, entry mode, and7marketing variables.

5. Discussion and conclusion
In this section, we discuss five general findings on FMA. First, as shown in Table 1, 64.4% of
the tests in this study supported FMA, a support rate higher than any other in the systematic
review literature. For example, David and Han (2004) conducted a systematic review oftransaction cost theory, which was supported by only 47% of the tests. Similarly, the systematic
assessment of the resource-based view in Newbert (2007) was supported by only 53% of thetests. Thus, the results indicate that FMA is a more common phenomenon than is suggested bymajor strategic management theory. One may therefore say that entry timing is an importantfactor in firm performance.
Second, the performance variables used in the tests affect FMA’s support rate. When market
share is used as a dependent variable, 84.6% of the tests are supported. Using profit or survival
reduces the support rate to 65.9% and 41.2%, respectively. Kalyanaram, Robinson, and Urban(1995) surveyed both the established and emerging empirical generalizations and concluded thatentry order did not have a significant effect on survival. Vandewerf and Mahon (1997) foundthat empirical research using market share as a performance measure was significantly more
likely to find FMA than were tests using other measures (such as profitability or survival). Our
results lead to similar conclusions.
Third, the dependent variables have been operationalized since the 1990s. Lieberman andMontgomery (1998) pointed out that, although market share has been widely used in empiricaltests as a dependent variable, few tests have used other variables, such as profit or survival.
However, our results were produced by a variety of performance measures. Although market
share is the most common (28.8%), profit (19.2%) and survival (9.6%) have become
increasingly accepted in the empirical research. Other performance measures, such as profit
growth and SCARs, have also been used.

8Fourth, there is no significant difference between industries. The support rate is 60.7% formanufacturing, 56.7% for service, and 57.4% for entry into foreign markets. The low support
rate for entry into foreign markets is caused by the high uncertainty produced by the
undeveloped market systems and political instability of the foreign nations (Li, Lam, and Qian,
2001). For the service industry, the ease with which followers can imitate early entrants at low
cost causes the insignificant support for FMA.
Fifth, there is a moderating effect between entry timing and performance. As shown in Table
4, the interaction terms (i.e., environment, resource, and strategy) have a moderating effect on
entry timing. However, the degree of this effect varies among the variables. First, the interaction
effect of environment was supported by 60% of the tests. Suarez and Lanzolla (2007) observedthat firms’ external factors moderated the performance of first movers. On the other hand,
support for resource’s interaction effect is slightly low, with 47.7% of tests supporting it.
Lieberman and Montgomery (1998) found an interaction effect between the resource-based
view and FMA. They insisted that entry timing does not have a directly positive effect onmarket share but that market share is affected, either positively or negatively, when resource,
capability, and marketing interact with entry timing.
The resources and entry order interaction variables shown in Table 4 can be approximately
categorized as “technological capability and complementary assets” (Teece, 1986) or“experiences,” representing the resources directly applicable to the current business or thatproxy for firms’ information resources, such as knowhow and knowledge, respectively. The
high R&D firm, concentrated technologies, quality, specific resource, and technologicalcapability variables can all be interpreted as “technological capability”; their moderating effects
on FMA are supported with a 50% probability. Moreover, firm size and marketing capability, as
complementary assets, are supported as moderating effects with a 25% probability. On the other
hand, international experience depth and pre-entry experience, reflecting firm experience, are
not at all supported as moderating effects on FMA. These results indicate that followers with theappropriate technological capabilities or complementary assets can somewhat neutralize FMA,

9

but that a firm’s experience of an earlier entrant in a similar market or industry does not affect
FMA.
Finally, the interaction effect of strategy was supported by 30.2% of the tests, the lowest
support rate. In a global strategy, it is important for managers to decide whether to enter in ajoint venture (JV) with a local partner or as a wholly owned subsidiary. The first-mover mustalso decide whether differentiation or cost leadership is the best strategy. Although thesestrategic options are important moderators, we did not test which strategy moderates FMA.
Our study suggests possibilities for future empirical research on FMA. First, studies should
control for differences among industry structures. Using samples composed of developing
countries would allow us to examine the mechanism that generates the differences among
advanced countries. Second, more interaction effects should be investigated. Although
theoretical models of moderating effects have been advanced by many authors (e.g., Kerin et al.,
1992; Szymanski et al., 1995), few attempts have been made to study them empirically. To
accurately understand the mechanism of FMA, we must elaborate the methods and theoreticalmodels of our investigations.

10

Table 1. Independent and dependent variables
Variable
Independent variable
Time lag
PioneerInteraction of resource
Interaction of strategy
Entry category
Entry orderInteraction of environment
Interaction of resouce and strategy
Total
Dependent variable
Market share
ProfitSurvival
Market share growthCompetitive position
Firm performance (manager's perception)
Brand trial penetration
Profit growth
Conbined abnormal announcement return of thetarget and acquirer
Share of outlet
Industry performance
Performance satisfaction
Expense ratio
Marketing efficiency
SCARs
Time-of-peak-sales
First repeat purchases
GrowthHeight-of-prak-sales
Level of operational risk
Productivity
Sales
Trial penetration% Totalarticles# Ariticles a% Totaltests# Tests

#
%
# Not
% Not
# Counter % Counter
Supported Supported supported supported supported supported# Not counter
supported% Not counter
Supported

13
11
9
6
7
7
4
1
31

42%
35%
29%
19%
23%
23%
13%
3%

56
51
49
35
30
27
10
3
261

21%
20%
19%
13%
11%
10%
4%
1%

46
33
23
16
18
24
6
2
168

27%
20%
14%
10%
11%
14%
4%
1%
64%

7
8
15
12
12
3
3
1
61

11%
13%
25%
20%
20%
5%
5%
2%
23%

3
9
7
5
0
0
1
0
25

12%
36%
28%
20%
0%
0%
4%
0%
10%

0
1
4
2
0
0
1
0
8

0%
13%
50%
25%
0%
0%
13%
0%
3%

15

48%
32%
16%
6%
6%
3%
3%
3%

91
41
17
30
5
24
14
8

35%
16%
7%
11%
2%
9%
5%
3%

77
27
7
8
5
8
14
6

46%
16%
4%
5%
3%
5%
8%
4%

10
12
5
6
0
16
0
2

16%
20%
8%
10%
0%
26%
0%
3%

2
2
5
13
0
0
0
0

8%
8%
20%
52%
0%
0%
0%
0%

2
0
0
3
0
0
0
0

29%
0%
0%
43%
0%
0%
0%
0%

10
5
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%

6
4
3
3
2
2
2
2
1
1
1
1
1
1
1

2%

3
4
0
1
1
0
2

2%
1%
1%
1%
1%
1%
1%
0%
0%
0%
0%
0%
0%
0%

1
1
0
0
1
0
1
1

2%
2%
0%
1%
1%
0%
1%
1%
1%
0%
0%
1%
0%
1%
1%

3
0
3
2
0
2
0
0
0
0
0
0
0
0
0

5%
0%
5%
3%
0%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0
0
0
0
1
0
0
0
0
0
1
0
1
0
0

0%
0%
0%
0%
4%
0%
0%
0%
0%
0%
4%
0%
4%
0%
0%

Total
31
261
168
64%
61
23%
25
10%
a Because several articles employ multiple approaches ad multiple independent and dependent variables, the subtotals and total reported for this column do not equal their sums.

11

0
0
0
0
0
0
0

0%

1
0
1
0
0
0
0
0

0%
0%
0%
0%
0%
0%
14%
0%
14%
0%
0%
0%
0%
0%

7

3%

Table 2. Main independent-dependent variable pairs
Independent variable

Dependent variable

Time lag
Pioneer
Resource
Pioneer
Time lag
Strategy
Category/
Entry order
Pioneer
Environment
Strategy
Category
Entry order
Environment
Resource
Category
Resource
Environment
Time lagMarket share
Market share
Market share
ProfitProfitMarket share
Market share
Market share
Survival
ProfitProfitSurvival
ProfitMarket share
Survival
ProfitProfitSurvival
Survival# Ariticles a
7
6
5
5
4
3
3
3
3
2
2
2
2
2
2
1
1
1
1

% Total
%
# Not
% Not
# Not counter % Not counter# Tests % Total tests # Supported# Counter supported % Counter supported
articlesSupported supported supported
supported
Supported
23%
19%
16%
16%
13%
10%
10%
10%
10%
6%
6%
6%
6%
6%
6%
3%
3%
3%
3%

18
15
20
10
9
15
10
9
8
6
5
4
4
3
3
4
2
1
1

12%
10%
14%
7%
6%
10%
7%
6%
5%
4%
3%
3%
3%
2%
2%
3%
1%
1%
1%

15
15
16
5
6
12
10
6
5
4
1
0
4
2
2
4
2
0
0

14%
14%
15%
5%
6%
11%
9%
6%
5%
4%
1%
0%
4%
2%
2%
4%
2%
0%
0%

2
0
2
4
3
2
0
3
0
2
3
4
0
1
1
0
0
0
0

7%
0%
7%
15%
11%
7%
0%
11%
0%
7%
11%
15%
0%
4%
4%
0%
0%
0%
0%

1
0
1
1
0
0
0
0
3
0
1
0
0
0
0
0
0
1
1

Total
31
147
109
74%
27
18%
9
a Because several articles employ multiple approaches ad multiple independent and dependent variables, the subtotals and total reported for this column do not equal their sums.

11%
0%
11%
11%
0%
0%
0%
0%
33%
0%
11%
0%
0%
0%
0%
0%
0%
11%
11%

0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0

0%
0%
50%
0%
0%
50%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

6%

2

1%

Table 3. Results by industry
Industry
Manufacturing
ServiceAcross maltiple industry
Entry into foreign markets% Total% Total
#
%
# Not
% Not
# Counter % Counter # Not counter % Not counter# Tests
articlestests
Supported Supported supported supported supported supported
supported
Supported
35%
43%
40%
34%
68%
0
0%
11
112
68
21
17
26%
26%
23%
46%
4%
14%
8
67
38
28
1
1
29%
13%
21%
0%
0%
0
0%
9
35
35
0
0
19%
18%
16%
20%
28%
86%
6
47
27
12
7
6

# Ariticles a

Total
31 261 168
64%
61
23%
25
10%
7
3%
a Because several articles employ multiple approaches ad multiple independent and dependent variables, the subtotals and total reported for this column do not equal their sums.

12

Table 4. Interaction of firm resources, environmental factors and strategy# Ariticles a % Total articles
Resource
Firm size
High R&D firm
Marketing capability
Marketing capability * Low
cost strategy
Concentrated technologies
Quality
Resource commitment
Specific resource
Technological capability
International experience
depth
Majority ownershipMarketing capability
Pre entry experience
Subtotal

1
1
1
1
14

Environment
Industry competition
Industry growth
GDP per capita
Munificence
Really new productStability
Serial acquirer
Stock consideration
Subtotal
Strategy
Entry mode(EJV)
Entry mode(WOS)High advertisement firm
Low cost strategy
Intensity of investment
Cost leadershipMarketing intensity
Strategy/Marketing
subtotal% Total
#
%
# Not
% Not
# Counter % Counter # Not counter % Not counter
tests
Supported Supported supported supported supported supported
supported
Supported# Tests

2
1
1

6%
3%
3%

3
20
4

4%
29%
6%

2
8
0

7%
27%
0%

1
4
4

3%
14%
14%

0
5
0

0%
63%
0%

0
3
0

0%
75%
0%

1

3%

3

4%

2

7%

1

3%

0

0%

0

0%

1
1
1
1
1

3%
3%
3%
3%
3%

2
2
2
2
2

3%
3%
3%
3%
3%

2
0
2
2
2

7%
0%
7%
7%
7%

0
0
0
0
0

0%
0%
0%
0%
0%

0
1
0
0
0

0%
13%
0%
0%
0%

0
1
0
0
0

0%
25%
0%
0%
0%

3%
3%
3%
3%
45%

100%
1
1
1
17

1
1
1
1
1
1
1
1
8

3%
3%
3%
3%
3%
3%
3%
3%
26%

2
2
1
1
1
1
1
1
10

6%
6%
3%
3%
3%
3%
3%
3%
32%

1%

0%

1%
1%
1%
24%

0
1
0
0
21

3%
0%
0%
70%

1
0
1
1
13

2
2
1
1
1
1
1
1
10

3%
3%
1%
1%
1%
1%
1%
1%
14%

1
1
0
1
0
1
1
1
6

3%
3%
0%
3%
0%
3%
3%
3%
20%

1
1
1
0
0
0
0
0
3

4
4
20
6
4
2
2
1
43

6%
6%
29%
9%
6%
3%
3%
1%
61%

2
2
4
0
2
1
1
1
13

7%
7%
13%
0%
7%
3%
3%
3%
43%

2
1
0
6
2
1
1
0
13

3%
0%
3%
3%
45%

0
0
0
0
6

0%
3%
3%
3%
0%
0%
0%
0%
0%
10%

0%
0%
0%
75%

0
0
0
0
4

0%
0
0
0
0
1
0
0
0
1

0%
7%
3%
0%
21%
7%
3%
3%
0%
45%

0%

0%
0%
0%
0%
13%
0%
0%
0%
13%
0%
13%
0%
0%
0%
0%
0%
0%
13%

0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0
0
0
0
0
0
0
0
0

total
31 70 30
43%
29
41%
8
11%
4
a Because several articles employ multiple approaches ad multiple independent and dependent variables, the subtotals and total reported for this column do not equal their sums.

13

0%
0%
0%
100%
0%

0
0
0
0
0
0
0
0
0

0%
0
1
0
0
0
0
0
0
1

0%

0%
0%
0%
0%
0%
0%
0%
0%
0%
6%

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18