Proceedings of the 12th European Conference on Knowledge ...

Camelia Burja and Vasile Burja
In order to assess <strong>the</strong> knowledge economy’s stage we used in this paper 11 analytical indicators
related to educational system (3 items), impact <strong>of</strong> science, technology, innovation (4 items), and
information society (4 items). These indicators are input variables in <strong>the</strong> analysis model and reflect
efforts made for achieving <strong>the</strong> KE goal. The selected indicators characterize significant aspects <strong>of</strong> <strong>the</strong>
objectives stated in <strong>the</strong> Lisbon Strategy and National Strategy for Research, Development and
Innovation 2007-2013, for Romania.
To ensure comparability <strong>of</strong> data, a series <strong>of</strong> factors <strong>of</strong> average or relative level have been considered.
They express <strong>the</strong> manifestation intensity <strong>of</strong> <strong>the</strong> phenomena present in each economy.
Highlighting <strong>the</strong> position occupied by Romania in <strong>the</strong> group <strong>of</strong> recent EU members from <strong>the</strong>
perspective <strong>of</strong> <strong>the</strong> performance recorded in knowledge economy may be achieved with <strong>the</strong> Data
Envelopment Analysis (DEA) method. This methodology establishes an efficiency score in relative
size for each analyzed system. The score evaluates <strong>the</strong> system’s distance from <strong>the</strong> efficiency frontier,
which consists <strong>of</strong> <strong>the</strong> entities with <strong>the</strong> best performances, considered benchmarks, in this regard.
DEA is an alternative method to <strong>the</strong> regression analysis, which establishes an optimal allocation <strong>of</strong>
resources or achieves results, by making a comparison with one or several reference systems
identified as having maximum efficiency. Efficient units are part <strong>of</strong> a production frontier that envelops
inefficient units.
The DEA approach doesn’t need to know <strong>the</strong> functional relationship that correlates consumed
resources with outputs, being considered a non-parametrical measuring method <strong>of</strong> <strong>the</strong> efficiency <strong>of</strong>
DMUs. Ano<strong>the</strong>r advantage <strong>of</strong> using DEA against regression is <strong>the</strong> low volume <strong>of</strong> information needed
to study multiple inputs and outputs. At <strong>the</strong> same time, <strong>the</strong> flexibility feature <strong>of</strong> <strong>the</strong> method allows
using a wide range <strong>of</strong> indicators to express input variables or outputs, which increases <strong>the</strong>
applicability range <strong>of</strong> <strong>the</strong> method (Thanassoulis 2009). DEA models provide for each organization a
measurement <strong>of</strong> overall performance, which ensures <strong>the</strong> possibility <strong>of</strong> ranked alternative valuations
that may be useful for some decision makers (Sarkis 2000).
Initially, DEA was used as a model that has constant returns to scale (CRS), called also CCR after its
creators Charnes, Cooper and Rhodes (1978). Subsequently, o<strong>the</strong>r models were developed by
Banker, Charnes and Copper (1984), which are applicable to technologies with variable returns to
scale (VRS). Analysis models can be input or output-oriented models and lastly <strong>the</strong>y become linear
programming problems (Cooper et al. 2004; Ray 2004).
Being widely used in <strong>the</strong> analysis <strong>of</strong> technical efficiency, <strong>the</strong> CCR model considers k decision making
units that have n inputs and m outputs.
The efficiency <strong>of</strong> decision making unit k is:
Ek
=
subject to:
m
∑ v j ykj
j=
1
n
∑ ui
xki
i=
1
m
∑ v j ykj
j=
1
≤ 1
n
∑ ui
xki
i=
1
(1)
(2)
141