To assess whether any of the combinations generated
by the fsQCA truth table algorithm are subsets of the
outcome, frequency and consistency thresholds must
be specified. We first reduced the truth table by specifying
a minimum frequency of one rather than choosing
a higher number of countries to constitute the
minimum number of countries per configuration. A
frequency of one enabled us to capture all countries
and not eliminate any cases, which also puts the Diamond
Model to a strict test to ascertain whether there
are alternative paths, even if only displayed by one
country, to achieving high national competitiveness.
In fact, policy makers and academics often recognize
that a single exemplary case may be an example to
be emulated.
FsQCA calculates consistency scores for measuring
the degree to whichmembership in a given configuration
is a subset of membership in the outcome. A
coverage score is also used to describe the relevance
of a condition or a set of conditions to a particular outcome,
such that close-to-zero coverage indicates triviality
(see Ragin, 2008). User-provided consistency
thresholds are used to classify the remaining configurations
as either exhibiting the outcome or not. We
used 0.8 for the consistency threshold, which is above
the minimum recommendation of 0.75 (Ragin et al.,
2006; Ragin, 2008) and consistent with past work (e.
g., Bell, Filatotchev, and Aguilera, 2013). We also
used 0.70 as the threshold for PRI consistency
(Misangyi and Acharya, 2014).
RESULTS
Necessity analyses
We first conducted a necessity test to examinewhether
causal conditions are necessary for high national
5 We coded this middle group of countries as 0.501 because coding
a country as exactly 0.50, causes the fsQCAsoftware to ignore
the data point (see Crilly et al., 2012).
National Competitiveness and Porter’s Diamond Model 91
Copyright © 2016 Strategic Management Society Global Strategy Journal, 6: 81–104 (2016)
DOI: 10.1002/gsj.1116
Table 2. Calibration scores and descriptive statistics for 90 nations
Country National
competitiveness
Factor
conditions
Demand
conditions
Context
for rivalry
Related and
supporting
industries
MNE
penetration
Governance
quality
Albania 0.09 0.33 0.37 0.30 0.00 0.39 0.08
Algeria 0.10 0.00 0.00 0.00 0.00 0.10 0.02
Argentina 0.20 0.35 0.29 0.01 0.00 0.28 0.12
Australia 0.99 0.99 0.95 1.00 0.50 0.53 1.00
Austria 0.99 1.00 0.96 0.99 1.00 0.43 1.00
Azerbaijan 0.06 0.10 0.93 0.17 0.00 0.20 0.25
Bahrain 0.61 0.56 0.97 0.90 0.00 0.74 0.25
Bangladesh 0.01 0.00 0.03 0.01 0.00 0.00 0.01
Belgium 0.99 1.00 0.98 0.95 1.00 1.00 0.99
Bolivia 0.02 0.01 0.01 0.00 0.00 0.45 0.03
Brazil 0.05 0.19 0.67 0.45 1.00 0.36 0.34
Bulgaria 0.11 0.39 0.05 0.47 0.00 1.00 0.38
Cambodia 0.01 0.01 0.83 0.11 0.00 0.69 0.03
Cameroon 0.01 0.00 0.00 0.13 0.00 0.25 0.01
Canada 0.98 1.00 1.00 0.99 1.00 0.40 1.00
Chile 0.36 0.76 0.95 0.86 0.00 1.00 0.98
China 0.05 0.68 1.00 0.57 1.00 0.07 0.08
Colombia 0.30 0.11 0.6 0.49 0.00 0.39 0.03
Costa Rica 0.18 0.9 0.72 0.52 0.00 0.53 0.85
Cote de Ivorie 0.01 0.00 0.00 0.01 0.00 0.33 0.01
Croatia 0.66 0.65 0.01 0.32 0.00 0.52 0.62
Czech Republic 0.74 0.84 0.52 0.92 0.00 0.71 0.93
Denmark 0.97 1.00 1.00 1.00 1.00 0.48 1.00
Dominican Republic 0.18 0.04 0.00 0.14 0.00 0.48 0.10
Ecuador 0.08 0.24 0.05 0.02 0.00 0.22 0.02
Egypt 0.05 0.00 0.00 0.02 0.00 0.36 0.02
Estonia 0.45 0.95 0.06 0.94 0.00 0.98 0.96
Ethiopia 0.01 0.00 0.01 0.01 0.00 0.17 0.01
Finland 0.98 1.00 1.00 0.99 1.00 0.40 1.00
France 0.99 0.99 0.84 0.97 1.00 0.40 0.98
Germany 0.97 1.00 0.97 1.00 1.00 0.24 0.99
Ghana 0.01 0.02 0.05 0.53 0.00 0.53 0.37
Greece 0.91 0.47 0.35 0.41 0.00 0.00 0.36
Guatemala 0.06 0.02 0.39 0.37 0.00 0.22 0.03
Hungary 0.52 0.44 0.01 0.66 0.00 0.80 0.82
India 0.02 0.05 0.73 0.48 0.50 0.09 0.05
Indonesia 0.02 0.37 0.59 0.19 0.00 0.30 0.05
Iran 0.46 0.01 0.42 0.08 0.00 0.01 0.01
Ireland 1.00 0.99 0.96 0.96 0.00 1.00 0.99
Israel 0.91 0.97 0.91 0.75 0.50 0.35 0.60
Italy 0.97 0.75 0.93 0.82 1.00 0.18 0.65
Jamaica 0.10 0.14 0.17 0.45 0.00 0.89 0.20
Japan 0.95 1.00 1.00 0.98 1.00 0.00 0.99
Jordan 0.11 0.24 0.10 0.32 0.00 0.90 0.36
Kazakhstan 0.12 0.59 0.81 0.20 0.00 0.91 0.05
Kenya 0.01 0.05 0.14 0.27 0.00 0.00 0.01
Korea (South) 0.89 0.98 0.98 0.81 1.00 0.13 0.85
Kuwait 0.99 0.11 0.29 0.19 0.00 0.08 0.51
Latvia 0.35 0.76 0.19 0.60 0.00 0.56 0.78
Lithuania 0.62 0.84 0.01 0.51 0.00 0.00 0.88
92 S. Fainshmidt, A. Smith, and W. Q. Judge
Copyright © 2016 Strategic Management Society Global Strategy Journal, 6: 81–104 (2016)
DOI: 10.1002/gsj.1116
competitiveness. This is in keeping with Schneider
and Wagemann (2012), who recommend removing
necessary and redundant conditions in order to simplify
analyses. The necessity statistics measure the
extent to which national competiveness is a subset of
each causal condition (Ragin, 2006). For each causal
condition, a consistency score of 1.00 indicates necessity
at all instances in the sample. A causal condition is
Table 2. (Continued)
Country National
competitiveness
Factor
conditions
Demand
conditions
Context
for rivalry
Related and
supporting
industries
MNE
penetration
Governance
quality
Malaysia 0.26 0.96 0.98 0.99 0.00 0.56 0.64
Mexico 0.28 0.22 0.27 0.47 0.50 0.35 0.06
Morocco 0.03 0.03 0.12 0.47 0.00 0.44 0.13
Netherlands 0.98 1.00 1.00 0.99 1.00 0.80 1.00
New Zealand 0.88 1.00 0.96 0.99 0.00 0.59 1.00
Nigeria 0.01 0.00 0.18 0.29 0.00 0.35 0.00
Norway 1.00 1.00 0.97 0.99 1.00 0.48 1.00
Oman 0.96 0.21 0.71 0.62 0.00 0.33 0.73
Pakistan 0.02 0.00 0.37 0.11 0.00 0.04 0.00
Peru 0.06 0.11 0.39 0.63 0.00 0.42 0.05
Philippines 0.02 0.18 0.47 0.10 0.00 0.12 0.03
Poland 0.66 0.58 0.34 0.91 0.00 0.50 0.93
Portugal 0.66 0.90 0.47 0.59 1.00 0.52 0.96
Qatar 1.00 0.99 0.98 0.91 0.00 0.24 0.98
Romania 0.11 0.21 0.08 0.45 0.00 0.48 0.33
Russia 0.27 0.48 0.49 0.34 0.00 0.34 0.02
Saudi Arabia 0.99 0.30 1.00 0.92 0.00 0.45 0.30
Senegal 0.01 0.01 0.00 0.12 0.00 0.18 0.19
Serbia 0.19 0.06 0.00 0.09 0.00 0.76 0.15
Singapore 1.00 1.00 1.00 1.00 0.00 1.00 1.00
Slovenia 0.84 0.95 0.02 0.70 0.00 0.32 0.96
South Africa 0.17 0.01 0.93 0.93 0.00 0.46 0.35
Spain 0.98 0.90 0.72 0.95 0.50 0.46 0.90
Sri Lanka 0.03 0.55 0.98 0.49 0.00 0.06 0.13
Sweden 0.99 1.00 1.00 1.00 1.00 0.80 1.00
Switzerland 0.97 1.00 1.00 0.99 0.00 1.00 1.00
Tanzania 0.01 0.00 0.01 0.03 0.00 0.48 0.09
Thailand 0.05 0.64 0.76 0.61 0.00 0.58 0.06
Trinidad and Tobago 0.88 0.05 0.42 0.23 0.00 1.00 0.28
Tunisia 0.10 0.14 0.61 0.81 0.00 0.75 0.13
Turkey 0.42 0.13 0.02 0.87 0.00 0.25 0.14
Uganda 0.01 0.00 0.00 0.05 0.00 0.03 0.03
Ukraine 0.04 0.47 0.06 0.06 0.00 0.75 0.05
United Arab Emirates 1.00 0.89 1.00 0.98 0.00 0.31 0.95
United Kingdom 0.98 1.00 1.00 1.00 1.00 0.66 0.99
United States 1.00 1.00 1.00 1.00 1.00 0.27 0.99
Uruguay 0.16 0.42 0.40 0.22 0.00 0.44 0.95
Venezuela 0.14 0.03 0.05 0.00 0.00 0.16 0.00
Vietnam 0.01 0.11 0.34 0.31 0.00 0.70 0.17
Yemen 0.02 0.00 0.00 0.00 0.00 0.02 0.00
Sample mean 0.44 0.47 0.51 0.53 0.20 0.44 0.46
Sample median 0.23 0.41 0.47 0.50 0.00 0.41 0.34
Sample std. dev. 0.41 0.41 0.40 0.37 0.35 0.29 0.41
National Competitiveness and Porter’s Diamond Model 93
Copyright © 2016 Strategic Management Society Global Strategy Journal, 6: 81–104 (2016)
DOI: 10.1002/gsj.1116
considered ‘almost always necessary’ if its consistency
exceeds 0.90 (Schneider, Schulze-Bentrop, and
Paunescu, 2010). While some consistency scores approach
the 0.90 threshold, none indicate that a condition
is ‘almost always necessary.’ This suggests that
national competiveness can be achieved without the
presence or absence of any single condition. Thus,
we proceeded without removing any of the conditions
from the model (Schneider and Wagemann, 2012).
Sufficiency analyses
The sufficiency analyses results are presented in
Table 3. As in Fiss (2011), we refer to the presence
of the outcome condition (i.e., national competitiveness)
above the crossover point as ‘high national competitiveness.’
We display the intermediate solution,
which is appropriate when only ‘easy’ counterfactuals
(i.e., configurations for which there are no cases, but
that are consistent with prevailing theory) are utilized.
By specifying expected associations between conditions
and the outcome a priori, the solution may be
simplified by using the assumption that adding a redundant
causal condition to a configuration already
linked to high national competitiveness would still
produce that outcome. In our case, we specified the
presence of the four Diamond elements as well as governance
quality as ‘should be’ associated with high
national competitiveness. Due to conflicting views in
prior literature, however, we did not specify a priori,
any theoretical direction for MNE penetration. Additionally,
we compared the intermediate solution with
the parsimonious solution in order to distinguish core
from peripheral conditions.6
The four configurations identified in Table 3 are
each sufficient for high national competitiveness. Specifically,
this solution covers 76 percent of fuzzy
membership in high national competitiveness, with
consistency of 0.93. The overall solution consistency
indicates that the four identified configurations are
very strongly associated with high national competitiveness,
and the coverage score indicates that the solution
accounts for a majority subset of it. Notably,
solution coverage improved dramatically over a
model with the Diamond Model alone without governance
quality and MNE penetration. Without the two
6 The parsimonious solution is a further simplified solution based
on ‘difficult counterfactuals.’ Whereas ‘easy counterfactuals’ reduce
the solution only by including remainders (i.e., configurations
for which there are no cases or that were dropped during
the frequency cutoff specification) consistent with theory, ‘difficult
counterfactuals’ make no such distinction and reduce the data
regardless of researchers’ assumptions, producing the most concise
and simplified way to express the solution. For core conditions,
those that are also in the parsimonious solution, there is
an empirically stronger association with the outcome that is not reduced,
even in the face of additional plausible configurations.
However, for peripheral conditions, the evidence for a causal relationship
with the outcome may be weaker, depending on the plausibility
of the difficult counterfactuals. See Grandori and Furnari
(2012) for an excellent and detailed discussion of counterfactual
analysis and the differences between solutions.
Table 3. Sufficiency analysis results
High national competitiveness
Causal condition Configuration 1 Configuration 2 Configuration 3 Configuration 4
Factor conditions ● ● ●
Demand conditions ● ●
Related and supporting
industries
●
Context for rivalry ● ● ● ●
Governance quality ● ● ● ●
MNE penetration Ø ø
Raw coverage 0.69 0.46 0.42 0.42
Unique coverage 0.15 0.06 0.01 0.02
Consistency 0.93 0.93 0.98 0.91
Solution coverage 0.77
Solution consistency 0.92
Note: Following past research (e.g., Judge et al., 2014), we indicate the presence of a condition with a filled circle (‘●’) and the absence of a
condition with a slashed circle (‘Ø’). A blank space denotes that a condition does not matter (i.e., can be absent or present) in that given
configuration. As in Crilly (2011), we distinguish between core and peripheral conditions using the size of the circles (larger circles are
core conditions). N= 90.
94 S. Fainshmidt, A. Smith, and W. Q. Judge
Copyright © 2016 Strategic Management Society Global Strategy Journal, 6: 81–104 (2016)
DOI: 10.1002/gsj.1116
additional causal conditions, coverage was only 0.43
with a consistency score of 0.95. Thus, the two additional
causal conditions greatly improved the empirical
relevance or explanatory power of the model. In
addition, the fact that there are multiple causal conditions
in each of the four identified configurations indicates
that none of the elements are sufficient by
themselves to bring about high national
competiveness. This provides further support that a
set-theoretic methodology is appropriate.
Configuration 1 contains three of the four elements
of the Diamond—only related and supporting industries
is missing. In addition to these three elements,
governance quality serves as a core causal condition.
Configurations 2 and 3 indicate that countries can
achieve national competitiveness without sophisticated
local demand. Configuration 2 contains the presence
of two elements of the Diamond Model—factor
conditions and context for rivalry. The presence of
governance quality is again a core condition here. In
this configuration, the absence of MNE penetration
actually contributes to high national competitiveness.
Configuration 3 contains three of the four components
of the Diamond Model. In these cases, related and
supporting industries replaces demand conditions
from Configuration 1. Configuration 3 also contains
the presence of governance quality. Finally, Configuration
4 contains two of the elements of the Diamond
Model—demand conditions and context for rivalry. In
addition it contains the absence of MNE penetration
and the presence of governance quality.
Context for rivalry and governance quality are
present in each of the four identified configurations,
indicating their centrality in achieving high national
competitiveness. Interestingly, only governance quality
is a core condition in all four configurations, further
highlighting its primacy for achieving high national
competitiveness. However, it is important to note that
governance quality by itself is not sufficient for high
national competitiveness. Instead, it must be
complemented by elements within the Diamond
Model.
The raw coverage values presented in Table 3 show
the fraction of national competitiveness that is
encompassed by each respective configuration, and
unique coverage indicates the portion of national
competiveness that is covered only by that specific
configuration. Coverage statistics here are quite high
in comparison to other studies (e.g., Crilly, 2011; Fiss,
2011; Garcia-Castro et al., 2013), indicating that the
identified configurations of causal conditions are in
fact very relevant to high national competiveness.
Robustness tests
Fuzzy-set analysis requires researchers to make several
important judgment calls (e.g., frequency cutoff,
consistency threshold) that may have an impact on results
(Fiss et al., 2013; Schneider and Wagemann,
2012). Thus, to substantiate the stability of our results,
we checked whether different specifications will ‘lead
to solution terms that are not in a subset relation with
one another…or to differences in the parameters of
fit that are large enough to warrant a meaningfully different
substantive interpretation’ (Schneider and
Wagemann, 2012: 286). Overall, we find largely consistent
results in the robustness tests using these different
standards. For the sake of brevity, we report the
results of those tests in the Appendix.
DISCUSSION AND CONCLUSIONS
Overview
This study had both theoretical and empirical objectives.
First, we sought to advance theory on national
competitiveness by examining how elements of the
traditional Diamond Model, coupled with governance
quality and MNE penetration, may collectively influence
a country’s level of aggregate productivity. Second,
we empirically examined Porter’s Diamond
Model coupled with two theoretical extensions and
evaluated its systemic linkages to national competitiveness
across a broad range of countries. In doing
so, we arrived at a more comprehensive national competitiveness
model emphasizing the centrality of governance
quality and the way the elements of the model
combine in complementary and substitutingmanner to
bring about high national competitiveness.
Using fsQCA, we analyzed data from 90 developed
and developing nations. The sufficiency analysis
identified four distinct configurations associated with
national competitiveness. This extends prior literature
that uses a variance approach. Grein and Craig (1996),
for instance, were able to identify only factor conditions
as a significant predictor of national competitiveness
when controlling for other elements. By using
fsQCA, we were able to identify four distinct configurations
of causal conditions associated with national
competitiveness. In each of these configurations, multiple
aspects of the Diamond Model appear to be
salient.
Importantly, empirical results indicate that a strong
Diamond in all four elements is not necessary for high
National Competitiveness and Porter’s Diamond Model 95
Copyright © 2016 Strategic Management Society Global Strategy Journal, 6: 81–104 (2016)
DOI: 10.1002/gsj.1116
national competitiveness. However, the configurations
we identified do lend much credence to Porter’s
original framework as a means of identifying why
some countries in general are more competitive than
others. At the same time, we also find support for
some of the arguments proposed by Porter’s critics.
Next, we discuss the implications of these findings
and use illustrative cases from our comparative
analysis.
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