NETWORK ATTRIBUTES
OF IMPORTANCE FOR INNOVATION
–EXPERIENCES OF HUNGARY
János FELFÖLDI - Krisztián KOVÁCS
University of Debrecen, Centre for Agricultural and Applied Economic Sciences
Department of Agrobusiness Management
E-mail: jfelfoldi@agr.unideb.hu
kovacskrisz@agr.unideb.hu
HUNGARY, 4032 Debrecen, Böszörményi str. 138.
ABSTRACT
The research objective was to identify the most important network attributes what
determine the SMEs success joining an innovative network in the food sector in Hungary.
This research is the part of the NETGROW project, where one of the tasks is to identify
attributes at international level. This paper is to introduce how we tuned to find those five
attributes and the paired comparison to increase the reliability of the different expert’s
opinion. The most important five networks attribute which may be of importance to the
Hungarian food SMEs when deciding on whether to join an innovative network are: 1.
Relevance of network's goal for the firm, 2. Presence of common values and willingness to
collaborate, 3. Clearness of goals 4. Degree of internal information openness, 5. Degree of
commitment by members.
INTRODUCTION
Together with partner universities and research institutions throughout Europe, the Hungarian
University of Debrecen has been conducting a European project of 4 years, called NetGrow,
which seeks to enhance the network behaviour of food SMEs and the performance of networks.
Within the NETGROW project, which has the objective to contribute to the innovativeness of
food SMEs through revealing the strategic network behaviour and network learning performance,
one of the tasks is to identify the network attributes which are relevant in prompting network
learning and innovation among agri-food SMEs, and to identify of their associated levels.
One of the deliverables of the project includes the findings of identification of relevant
network attributes, collected by UNIBO and LAS through the review of the relevant literature,
and through the data collected during the plenary brainstorming session held in Bonn in June,
2011. Among the participants, stakeholders from the triple helix coming from the partners’
countries were involved, and some international experts were also invited. The basic
methodology was developed by UNIBO together with UGENT. A professional facilitator was in
charge of the direction of the plenary brainstorming session, and the results were mapped out
graphically by a visual facilitator. A provisional list of attributes emerged, and the results were
analysed by UNIBO and LAS, and compared with the attributes emerged from the literature
review (Netgrow Deliverable 3.1, July 2011).The major objective of this deliverable was to focus
on the identification of the twenty most important attributes for network innovation for the
SMEs, and their levels.
There was a brainstorming session among the NETGROW partners, running with the
participation of a group of external experts, where the business component was represented by
food SMEs managers. It is aimed at identifying the list of attributes and related levels, according
to a national perspective. A common procedure was used throughout the case study countries,
and it was intended to encourage the elicitation of an independent list of attributes from the
experience of the participants, rather than asking general statements or commenting pre-defined
list of attributes (e.g. the general list of attributes developed in Bonn).
Most of the attributes seemed to be pretty similar across EU Countries in which national
brainstorming sessions took place (Belgium, France, Hungary, Ireland, Italy, Sweden), at least in
the possibility to group them according to macro-areas. Moreover, the emerged attributes are also
consistency with those obtained from the plenary brainstorming session in Bonn.
The identification of the attributes levels was satisfactory, but their listing and definition
at the national level resulted less consistency and with different levels of detail and operationality
for the different attributes. In particular, while some attributes’ levels may be quantified through
measurements, others are mainly qualitative ones.
In conclusion, the application of the brainstorming methodology in the six Netgrow
partners involved helped to reach our objective, the identification of the list of 20 attributes, their
levels and their definition. These results provide useful insights for the next step of Netgrow
project, the organization of the Delphi rounds, which will lead to a ranking of the current list of
20 attributes, and to the final identification of the most relevant five.
MATERIAL AND METHODS
In this research the paired comparisons analysis was applied, because we had to consider several
attributes and several experts’ opinion to find out the top five relevant attributes in our research.
In the scientific literature there are several methods to choose the important and most relevant
factors for our research, but most of them are based on the paired comparison method.
Description of the data
During the NETGROW brainstorming session, the experts identified a list of 37 attributes
relevant for SMEs networking and innovation, later we reduced this attributes to 12. Then, these
attributes were grouped and then ranked (through dots) by the participants, which were asked
later to define the characteristics of the most voted ones. In the following table presents the 12
selected variables for the analysis. In the second column of the table we can read the definitions
of the selected variables (or attributes).
Table 1.: Selected variables to evaluate and its definitions
Degree to which information is shared openly with members
within the network
Degree to which the goals of the network are clearly defined
1.
Degree of internal information openness
2.
Clearness of goals
3.
Main services provided by the network
4.
Type of members
5.
7.
Relevance of network's goal for the firm
Presence of common values and
willingness to collaborate
Variety of industry sectors
8.
Degree of commitment by members
9.
Linkages to other networks or
institutions
10.
Diversity and open-mindedness
11.
Representativeness of the network with
respect to the sector
Degree to which the network covers the full sector
12.
Type of food sector of the members
Desired composition of the network in terms of the
homogeneity/heterogeneity of food sectors in which the
members work
6.
Type of preferred services provided by the network
Composition of the network in terms of the type of members
(e.g. other firms, advisors, etc.)
Degree to which the network's goal is relevant for the firm
Degree to which the members of the network share common
values and a willingness to collaborate
Specificity of network's focus with respect to the food sector
Degree of commitment by members of the network towards
the network and other members
Linkages of the network/its members to other networks or
institutions
Degree to which the members of the network show/value
diversity and open mindedness
In the following table (Table 2.) about the descriptive statistics, the general list of attributes is
provided with a number of observations (N), the mean, standard deviation, minimum and
maximum value of the ranking. In our research we had interviewed 18 Hungarian experts from
Innovative Agri-Food sector. The experts belong to SMEs, public bodies, and research
institutions, or network management organizations. Table 2 introduces the 12 variables mean
scores given by the experts. If we just measure the means of the scores, then might have the
feeling that is too simple to be solid enough. Not blaming those who apply the means to set the
order of objects or factors, we set the goal to go into a bit deeper.
Table 2.: Descriptive statistics of selected variables
DESCRIPTIVE STATISTICS
N
1. Degree of internal information
openness
2. Clearness of goals
Mean
18
18
5,86
5,03
Rank
Std. Deviation
Minimum
2,55
3,44
1,50
1,00
Maximum
11,00
12,00
3. Main services provided by the
network
4. Type of members
5. Relevance of network's goal for
the firm
6. Presence of common values and
willingness to collaborate
7. Variety of industry sectors
8. Degree of commitment by
members
9. Linkages to other networks or
institutions
10. Diversity and open-mindedness
11. Representativeness of the
network with respect to the sector
12.Type of food sector of the
members
18
18
6,83
7,92
3,54
2,90
1,00
1,50
12,00
12,00
18
2,92
2,44
1,00
9,00
18
18
4,81
8,61
3,27
2,90
1,00
2,00
12,00
12,00
18
5,58
2,91
2,00
12,00
18
18
5,08
8,53
2,49
2,60
1,50
2,00
10,00
12,00
18
7,86
2,57
2,00
12,00
18
8,97
3,11
1,00
12,00
Method of analysis
The method of paired comparisons has a long history, originating in the field of psychophysics.
Within psychology it is most closely associated with the name of Louis Thurstone (1959), an
American psychologist working in the 1920s – 1950s who showed how the method could be used
to scale non-physical, subjective’ attributes such as ‘perceived seriousness of crime’, or
‘perceived quality of handwriting’ (Bramley-Oates 2010).
In practice, the paired comparison method typically is very demanding – it can be extremely
resource- and time-intensive. The issue for its deployment depends not least on reaching a
judgment regarding its benefit-effort ratio in a specific context. In an effort to increase the
efficiency of the process, Bramley (2005) showed how the same principles could be used to
create a scale if the experts were asked to put several objects into a rank order rather than
comparing just two. Using rankings of several objects allows many more comparisons to take
place in the same time, with the advantage of allowing whole mark scales to be linked, rather
than just grade boundary points (Bramley-Oates 2010).
To get a clear view for each of the attribute combination scores, we used a preference matrix
(Table 3.). This matrix includes each decision on paired attributes that the individual experts
made. In the rows and columns of the matrix we can see that different attributes (E1-E6) to be
compared. So for instance to compare the E1 attribute (in the row) with an E2 attributes about
which are the most important for our research proposal, in our case - what is the importance of
the network characteristic (attribute) for a SME when deciding to join an innovation network. If
the row attribute (E1) is more important than the column (E2) attribute, than the result in the
matrix intercept will be 1. If the row attribute (E1) is less important than the column (E2) attribute,
than the result will be 0, like in our preference matrix table at the below. The comparisons follow
the row by row order, so first the experts fill the first row of the table and then move on the
second row and so on. Of course the diagonal of the matrix does not mean anything (with same
attribute comparison) so the value of the diagonal matrix will be x. During the comparison
procedure the experts has to compare for instance the E1 with the E2, but later they have the
opposite comparison, like E2 with E1. It is the same decision but in another way, to test the
experts confidences. Finally we can calculate the sum of the individual rows and columns for the
future analysis.
Table 3.: Individual preference matrix for the paired comparison method as an example
E0 E1 E2 E3 E4 E5 E6 a a2
E1 x 0 1 1 1 0 3 9
E2 1 x 0 1 1 0 3 9
E3 0 1 x 1 1 0 3 9
E4 0 0 0 x 1 0 1 1
E5 0 0 0 0 x 0 0 0
E6 1 1 1 1 1 x 5 25
 2 2 2 4 5 0 15 53
E= attributes to be compared
a=row sums of scores
To test the experts’ consistency, we have to calculate another index, which is the coefficient of
consistency (K). We use the preference matrix values to calculate this index. To measure the
inconsistent decisions made by the experts, we can use the following equation:
where
𝑅𝑚𝑎𝑥 − 𝑅
𝑑=
2
𝑅𝑚𝑎𝑥 =
𝑛3 −𝑛
12
n: number of attributes
R: sum of the individual rows
If the K value is near to 1, that means that the specific expert decisions are highly consistent, if
the value of the index is near to 0, that means that the expert decision is really hectic, thus that
expert is not reliable for the certain topic. We can say that a value between 0,5-0,7, or a higher
one might be acceptable.
To decide the ranks between the attributes we use the Kendall’s coefficient of concordance (W).
The equation of the index is the following:
where:
W: Kendall’s coefficient of concordance index
Cj: rank scores
Ĉ: average
m: number of experts
n: number of attributes
If the value of Kendall’ W is 1, that means, there are a fully agreeing among the experts opinion
of a certain decision. Contrarily if the Kendall’ W value is zero, that means that there are no
agreeing among the experts opinion about the ranking of the attributes.
To measure the dynamism of the experts opinions, in other words, how the different expert
opinions represent the same trend among the attributes ranking, we can use the Kendall’s
accordance index (V). We can calculate this V value from the cumulated preference table using
the aggregated rank of it and applying the following equation:
where:
m: number of expert
n: number of attribute
Γ: number of the similar experts opinions
γ: frequency value from the aggregated preference table under the diagonal
If the value of Kendall’ V is around 0 (a bit less than 0 which is up to the number of
respondents), that means that the expert opinion is totally deferent from each other. If V value is
near to 1, that means that the experts have the same opinion about the attribute ranking.
RESULTS and DISCUSSION
Table 4 presents each expert cumulative linked ranks of each attributes. For instance the Expert 1
opinion was that the most important attribute to consider for the innovative food SME’s is
attribute 5 named “Relevance of network's goal for the firm”. Expert 1 ranking had three
attributes that just right followed attribute 5. They are attribute 2 named “Clearness of goals”,
attribute 8 named “Degree of commitment by members”, and attribute 11 named “
Representativeness of the network with respect to the sector”. Therefore, we used linked ranks to
express that there is no difference among them, thus each of them got the same rank, actually
rank 3.
Table 4.: Linked ranks of attributes by experts (respondents)
Expert ID/ Attribute
Expert 1
Expert 2
Expert 3
Expert 4
Expert 5
Expert 6
Expert 7
Expert 8
Expert 9
Expert 10
Expert 11
Expert 12
Expert 13
Expert 14
Expert 15
Expert 16
Expert 17
Expert 18
1
8
11
2
5,5
6
2,5
5
7
7,5
5
5,5
4
10
7
4,5
1,5
8
5,5
2
3
4
3
1
4,5
1
9
1
7,5
2
7,5
2
7,5
4
3
8,5
10
12
3
6
9
1
10
4,5
7,5
5
11
12
4
3,5
12
7,5
5
2
11
3
9
4
12
12
6,5
8
8
10
9
7
7,5
7
11,5
6
3,5
8
7,5
12
1,5
5,5
5
1
2
5
2
1
2,5
9
2,5
2
1
3,5
3
1
1
1
1,5
8
5,5
6
10,5
6
4
3
3
4,5
1
12
7,5
3
1
1
2
3
7,5
6,5
8
3
7
10,5
2
11
11
9,5
11
5
7
7,5
10
11,5
11
7,5
10
12
4,5
5
9
8
3
9
12
4
2
6
5
7
7,5
6
2
8
3,5
2
10
3
5
5,5
9
6
2
8,5
5,5
7
4,5
5
2,5
2
9
5,5
5
5
6
10
4,5
1,5
2
10
9
6
6,5
9
12
7,5
11
7
2
11
9
8
12
9
10
8,5
5
11
11
3
9
10
7
9,5
12
2
7
7,5
8
7,5
8
7,5
11
6
6,5
11
9
12
6
6
8,5
12
11
9
12
7
7,5
12
10
10
11
12
4,5
10
12
1
By using this table the Kendall’s coefficient of concordance (value W) is 0,292 (around 0,3),
which means that the experts agree 30% of the attributes importance ranking. This could occur,
because the 18 experts represented from different business fields like SMEs, public body,
research institutions or network management organisations. These different orientations show
different preference systems with different point of views. But we got an overall picture showing
that there is some common orientation representing a pattern of attributes’ importance.
Table 5.: Cumulative attribute score matrix
ATTRIBUTES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
1.
0
9
9
5
12
9
8
7
7
6
7
6
2.
7
0
8
7
10
8
5
7
8
4
5
5
3.
10
11
0
9
14
12
7
12
8
7
9
6
4.
14
12
12
0
14
9
7
12
11
6
6
6
5.
6
9
4
1
0
6
4
6
8
5
8
5
6.
4
11
6
2
10
0
8
7
10
6
7
5
7.
12
16
15
10
16
15
0
12
13
10
8
8
8.
9
10
6
2
13
13
2
0
11
7
8
8
9.
16
12
15
7
15
15
7
12
0
8
10
5
10.
14
14
14
11
17
17
8
15
9
0
9
7
11.
13
12
14
10
15
16
5
13
7
5
0
9
12.
10
14
16
13
15
18
10
13
10
8
9
0
To reach our paper’s major objective we had to rank the attributes for the different expert views.
We use the cumulative attribute score matrix to decide which attributes are considered important
ones for the innovative food SMEs (Table 5).
From the cumulative attribute score matrix we can compute the aggregate order of the different
attributes, which is applied together with the mean ranks we gained from the process of
computing Kendall’s W. Thus, we could create the combined or verified order of the important
attributes. This final score rank is shown by the following table (Table 6.) Thus the top 5 most
imported attributes that are important for the SMEs when joining an innovative network are:
1. Relevance of network's goal for the firm
2. Presence of common values and willingness to collaborate
3. Clearness of goals
4. Degree of internal information openness
5. Degree of commitment by members
Table 6.: Attribute ranks comparison
sum of
Attributes
1
2
3
4
5
6
7
8
9
10
Degree of
internal
information
openness
Clearness of
goals
Main services
provided by the
network
Type of
members
Relevance of
network's goal
for the firm
Presence of
common values
and willingness
to collaborate
Variety of
industry sectors
Degree of
commitment by
members
Linkages to
other networks
or institutions
Diversity and
openmindedness
difference
of
the row
and the
column
aggregate
rank
average
rank
Kendall's
W
aggregate
order
order
order
Kendall's
W
combined
row
colum
n
115
85
30
4
6,03
4
6
4
130
74
56
3
5,14
3
3
3
119
105
14
6
6,86
6
7
7
77
109
-32
8
8,03
8
9
8
151
62
89
1
3,03
1
1
1
138
76
62
2
4,92
2
2
2
71
135
-64
11
8,11
11
11
11
116
89
27
5
5,69
5
5
5
102
122
-20
7
5,14
7
4
6
72
135
-63
10
8,08
10
10
10
11
12
Representativen
ess of the
network with
respect to the
sector
Type of food
sector of the
members
86
119
-33
9
7,97
9
8
9
70
136
-66
12
9
12
12
12
In this paper we surveyed 18 experts’ opinion about the important network attributes. But the
experts has different background knowledge, thus their reliability can also be different. K values
of experts were used to express their reliability as experts. Computing the average of coefficients
of consistency of each group of experts gives us reliability values for each type of organizations.
For the future research, it might be useful to be aware of what experts or which type of experts
can help us reach our objectives. The next table concludes four groups of organizations and their
expert’s average K values to highlight the difference among them. The higher the K value the
more confident the organizational experts’ group.
Table 7.: Reliability values of different types of organizational experts in Hungary
Type of the organization
Average K value
network management organisation
0,68
public body
0,53
research institution
0,49
business partner
0,43
Table 7 shows that the most confident experts in Hungary are the network managers with 0,68
and the second is the public bodies with 0,53 average K value. Thus, in the future if we want to
get reliable and detailed information about the Hungarian food SMEs network aspects, it is
advisable to focus on these 2 groups of experts. For the business partners, this level of
examination seems to be too abstract, thus they are rather aware of network operation than
evaluation.
CONCLUSIONS
As a result of the survey we got a clear order of attributes, of which the top five can be explicitly
selected. Taking ranks of six and seven into consideration, the attributes behind them have clear
meanings and they seem to be complementary for the top five. Experts of network management
experience can be more confident respondents of the field of networking than those of concerned
by a specific field or aspect of it.
This draws the attention to the importance of network management, while leading us to the need
for revealing the operation of such networks.
REFERENCES
 Netgrow Project (2011). Deliverable 3.1: Literature study + proposal of relevant attributes
 Netgrow Project (2011). Deliverable 3.2a: Selection approx. 20 relevant attributes after brainstorming
 Tom Bramley & Tim Oates, July 2010. Rank ordering and paired comparisons – the way Cambridge Assessment
is
using
them
in
operational
and
experimental
work,
http://www.cambridgeassessment.org.uk/ca/digitalAssets/187192_TB_TO_Rank_ordering_stocktake_final.pdf
 Thurstone, L.L. (1959). The measurement of values. Chicago: University of Chicago Press.
 Bramley, T. (2005). A rank-ordering method for equating tests by expert judgment. Journal of Applied
Measurement, 6(2), 202-223.
 Bramley, T. (2007). Paired comparison methods. In P.E. Newton, J. Baird, H. Goldstein, H. Patrick, & P. Tymms
(Eds.), Techniques for monitoring the comparability of examination standards. (pp. 246-294). London:
Qualifications and Curriculum Authority.
 Novakovic, N., and Suto, I. (2010) ‘The reliabilities of three potential methods of capturing expert judgment in
determining grade boundaries’. Research Matters: A Cambridge Assessment Publication 9, 19-24.