STEERING THE VOLUNTEERING DATA ENVELOPMENT ANALYSIS OF [629318]

STEERING THE VOLUNTEERING – DATA ENVELOPMENT ANALYSIS OF
VOLUNTEERS ’ RETENTION EFFICIENCY IN CIVIL SOCIETY UNITS
Abstract
Civil society is positioned somewhere in the area between business , government and private sector.
As organizations of civil society are not profit oriented, they are often relying on the engagement
of volunteers, i.e. workers who are not paid for their effort. Successful managing of human
resources in organizations dependant on wor k of volunteers can prove even harder than managing
work of employees in business entities. Someone’s work effort , productivity and devotion are
influenced by many factors far beyond technical conditions so it is not possible to separate
someone’s work fro m the rest of his/her personality traits . In civil society organizations, attracting,
motivating and keeping volunteers willing to conduct needed tasks and actively participate in
organization’s activities might prove to be quite challenging. For the purpo se of this paper, a survey
was conducted among 42 organizations of civil society units (CSUs) that use the help of volunteers
in fulfilment of their activities. In order to analyse relative efficiency of the civil society units,
appropriate input and output variables were selected and analysis was conducted with non –
parametric DEA method. It was decided to take 4 inputs and 2 outputs in the analysis. The obtained
results show that 69% of 42 CSUs are relatively efficient, 31% relatively inefficient and 26.19%
below the average. The results of analysis has enabled the identification of efficient and inefficient
units. The reference set was calculated for each inefficient unit in order to determine which inputs
cause better performance output. Calculated p rojections can be useful to CSU’s managers and serve
as a benchmark for detecting the source of inefficiency within their humanitarian organizations.
They can also serve as guidelines for improving inputs and thus achieving higher levels of outputs
i.e. du ration of volunteering and number of volunteers.
Keywords : organizations of civil society, volunteers, Data Envelop ment Analysis (DEA) , human
asset specificity
JEL: M54, C67, L31
1. INTRODUCTION
In organizational sciences human resources have always been recognized as one of the key elements
of an organization ’s success . However, managing human labour has become one of the focal points
of organization al science and practice since the beginning s of the modern industrial era. During
the first few decades of the 20th century, the focus was on the technical aspect s of work. The works
of Taylor, Gilberth, Fayol as well as others greatly contributed to the huge increase on labour
productivity and over all efficiency. For example, the task cycle for an average worker on Ford’s
assembly line shortene d from 514 minutes in 1908 to 2. 3 minutes in 1913, and all that before the
introduction of the assembly line (Womack et al, 2007, p 26). The enlightenment tha t workers’
efforts are influenced beyond just the technical aspects, brought around the emphasis on human
relations within organizations. Motivation theories that were developed during that period have
helped to understand the complexity of human nature. Someone’s effort at work is considered to
be a result of various factors such as understanding of the importance of one’s individual task and

its contribution to the overall work, communication with peers, superiors and subordinates, team
spirit, inclusion in decision making process etc.
The specificity of the human factor arises from the inseparability of one’s work their personal traits,
background, expectations, issues, motives, interests, beliefs etc. Moreover, the effort itself and the
person putting that effort are also inseparable, so the employee/worker has substantial control over
the volume of physical and mental energy they are willing to invest . Unlike machines and
equipment, humans are aware of their working environment and conditions and the way they are
being treated. T hey are actively focused on changing the conditions they do not find beneficial.
Therefore, due to the unique nature of human resources they present the greatest challenge for
organization theory and management practice ( Jaffee 2008) .
An important theoretical contribution to the understanding of the organizational issues related to
managing labour (as a resource ) was gi ven by Oliver Williamson (2002) . The f ocal point of his
analysis is the link between an organization’s decisions on how to manage its business transactions
and asset specificity , which is closely related to the human factor .
When it comes to other, non -human resources, when there is no specificity involved, organization s
can choos e to obtain them on the mark et (buy ). In cases when specificity occurs, pure market
relations can prove to be inadequate . In such cases organization s can opt for hazardous contractual
relations without safeguards , firmer contractual relations that contain safeguard, or internalization
of transaction by building new facility or buying a provider. Most organization have to deal with
human asset specificity due to different levels of skills and knowledge among its wo rkforce and
their different working experience. Organizations can not and do not own people in the way that
they own other resources . Therefore, managing human asset specificity is based on various types
of contracts , from simple contracts for occasional tr ansaction that do not involve specificity to
employment contract s which can contain some protection clauses.

2. LITERATURE REVIEW
Managing the work of volunteers, especially in situations when human asset specificity occurs, is
even harder than managing human resources in general . Even if organizations, which use
volunteers with specific knowledge and skills, were to try to set these volunteers to sign strong,
binding contracts , it is not to expect that many of them would agree .

When human resources are scarce (which is typically the case with volunteering), when
organizations do not offer financial compensations for work, and when binding contractual
relations are not applicable, managing human resources becomes even more demandi ng. In such
organizational environment , managers besides their usually recognized tasks, should do their best
to create “communityship”, word coined by Henry Mintzberg ( 2017, 2009), implying a balance
“between individual leadership on one side and collecti ve citizenship on the other”. Although he
was not explicitly speaking about organizations of civil society, his explanation that communityship
as a non -egocentric but rather engaged and distributed leadership, suits them perfectly. In other
words, voluntee rs should not be used, but their talents, skills, and time should be utilized in order
to make the volunteering experience more meaningful to the volunteers and more productive for
the organization (Kummerfeldt, 2011) . Charities and other similar organizations are constantly
trying to balance between the needs of organization and the needs of collective. Many of them are
attempting to become more professional by paying for work in the key parts of their organizations
because professionalization leads to increase in efficiency (Parsons, 2004 , Parsons & Broadbridge,
2006 ).
A volunteer is a person who freely offers to take part in an enterprise or undertake s a task or who
works for an organization without being paid (Oxfo rd Living Dictionaries). There are many factors
that may attract someone to become a volunteer like: social needs, family needs, overcoming some
life transition, search for companionship, strengthen social ties, extension of formal workplace
relationships, self -realisation etc. (Bains, Lie & Wheelock, 2006). Although volunteering from
economic perspective can be perceived as irrational, those who donate their time and expertise can
get some internal satisfaction – a “warm -glow” – joy from giving (Andreoni, 2015). For working
people, volunteering can be seen as a leisure activity, or at lea st something between work and leisure
(Holmes at al. 2010). However, taking parts of leisure for volunteering is understandable as
volunteering is known for having positive effect on few volunteer’s aspects of well -being: self –
reported life satisfaction, happiness, health, and life mastery (Huang 2019) . From the marketing
standpoint, volunteers should be perceived as costumers as they are heterogeneous group with
various qualities, skills and background. They can make their decision to buy or not to buy, a nd if
they buy, they may or may not become a loyal customer. (Dolnic ar & Rendle, 2004). Although
volunteers may be found in almost every human activity, the focus of this paper is primarily on
managing volunteers in organizations of civil society and/or non-governmental organizations

(NGO) .1 Most organizations of civil society rely on voluntary work for the fulfilment of (at least a
part of ) their activities, and many of them are actually dependent on it.
As many people these days work very hard and have limited time to spend with their families and
on their hobbies, attracting volunteers can be challenging for most organizations. This is especially
evident now with the constant depopulation of many countries in Europe, Croatia included .
Fortunately, the civil society in Croatia still seem s to be growing , and the database provided by the
Croatian Ministry of Public Administration is currently counting 50.5 thousand active NGOs
(Croatian Ministry of Public Administration ). The number woul d be even higher if local branches
of some well -known humanitarian organizations like Caritas were included. S mall, yet the most
numerous , units of Caritas are organized on parish level and are not recognized as legal entities.
These organizations, like many others rely mostly on voluntary work. The importance of
organizations of civil society in Croatia is expected to grow because the Ministry of Agriculture is
actively working on establishing a sustainable food donation system. Although the organizatio nal
design of the food bank system is not yet completely finished , organizations of civil society and
their volunteers will in any case serve as a link between food banks and final recipients of the
humanitarian aid (Lovrencic et al., 2017).
Managing activities done by volunteers can prove to be very hard, sometimes even frustrating. As
they are not being paid for their work, even if they have a signed contract with the organization
they agreed to volunteer for (which is often not the case) , there are typically no legal and , certainly ,
no financial consequences for volunteer who do not perform well , or do not perform at all . When
volunteer s neglect their duties, an organization will eventually just stop relying on that person.
Therefore, most of the voluntary work is done based on hazardous contractual relations. There is
a different psychological contract between volunteers and the organisation where they work (Kim
et al., 2009).
An additional worry for organizations relying on the work of volunteers is a problem of a turnover
– number of volunteers leaving the organization who have to be replaced. As argued by Ficher and
Schaffer (1993), high turnover is especially serious when an organization requires volunteers with
special skills , when they require intensive training or when the job needs long -term commitments.
Motivating and retaining volunteers is closely connected with them having positive experiences
with volunteering. Positive experience in volunteering is perceived as the one tha t allows volunteers

1 Civil society organizations and non -governmental organizations do not necessary need to be the s ame ones but
for the purpose of this paper, they will not be differentiated

to feel needed and appreciated, that allows them to feel a sense of accomplishment, provide job
satisfaction, offers opportunities to develop friendship etc. (Starnes & Wymer, 2001)
Although efforts to keep running the activities of an civil society organization heavily dependent
on the work of volunteers might seem futile, there are many organizations in civil society that are
rather successful in “steering” the volunteering. Someone might rightfully argue that it is just a
matter of believing in a good cause. Therefore, people are not equally motivated to serve as
volunteer in different organizations or on different projects. Even if they have high motivation to
volunteer, it is for them to decide to participate i n a specific project, the opportunity needs to fit
in with the rest of their lives (West & Pateman, 2016). However, an organization’s ability to
persuade others to believe in a good cause is necessary to attract and to keep volunteers active, but
it is not enough. Many i ssues like bad management of human relations, flaws in organization of
activities, lack of success in presenting the importa nce of work done by volunteers or appreciations
toward their efforts will quickly steer them awa y from volunteering at least for that organization.
Considering all this , it seemed important to gather data related to volunteering in civil society
organizations and analyse them using DEA method.

3. SAMPLE AND DATA
In Croatia there are four regional hubs in the four biggest cities that network willing volunteers
with organizations of civil society, which are in need for their services. Their publically available
data on civil society organizations (NGOs) was used to gather contact all these organizations. They
were contacted by e -mail with a request to participate in this survey . The survey was conducted in
2018 using an on-line questionnaire composed of 44 questions . A total of 42 organizations
completed the questionnaire . According to the scope of their activities , most of them are involved
in social or humanitarian activities (28), educational activities (5), religious activities (4), youth
related activities (3) etc. Most organisations in the sample claime d that they have more than 26
active volunteers and their scope of activities is local (Figure 1). The majority of organizations (83.3
%) have more female than male volunteers, and most volunteers are between 21 and 40 years old,
and are typically staying in organizations for a period of few years (59.5%).
Figure 1. Organizations in the sample

Source: Authors’
Besides demographic data, questions in questionnaire were grouped in six sections. The first section
was comprised of questions about attracting and including volunteers in an organization. Second
section questioned organizational, communication, social an d computer skills required from
volunteers. Third and fourth sections were about application forms for volunteers and selection
process. The fifth section was about occurrence and reasons for absenteeism among volunteers.
The questions in sixth section wer e routed to find out if there is: a formal contract between an
organization and a volunteer, opportunity for additional training to acquire needed skills, system
of stimulations (rewards and/or punishments) for provided effort.
Most respondents expresse d that securing sufficient number of volunteers is moderately hard.
When they were asked about working experience of their volunteers and their time available for
volunteering, most answers were neutral as respectively 50 an d 42. 9 % have chosen neutral answer
(3) on Likert scale (1 -5). However, most respondents think that their volunteers have altruistic
motives (66.7%) and that their organizations are enabling their volunteers to do their work in
adequate social and organizational c onditions (95.2%). According to responses from our sample,
during the interview it is important to understand candidates’ motives for applying for volunteering,
their general expectation and assessment of time they can afford to invest .
In a question abou t absenteeism of their volunteers, 45.2% of the respondents in our survey
reported that volunteers are often or very often absent without announcement. When ask to
estimate reasons for volunteers to quit working for their organization, most respondents have
emphasised that volunteers had a lack of affordable time (69.1%). They mostly think that it was
not the problem that the work within organization failed to meet expectations of the volunteers
(70.1%). Very few of them (7.2%) concluded that their volunteers have skipped to work in some
other organization . 21%
24%
7%
5%43%No. of volunteers
less than 10 10-15 16-20 21-26 more than 2639%
20%24%17%Geographic scope
Local Regional National International

When asked to access the importance of skills that are important for volunteers, they have put
most emphasis on social skills (average grade 4.21), wh ile computer skills are not seen as important
(average grade 2.66) (Figure 2).
Figure 2. Importance of volunteers’ skills

Source: Authors’
Majority of the respondents (57.1%) stated that their organization offers some kind of
training/education needed for work done by volunteers. As many as 71.5% or organizations in our
sample has some kind of rewards for highly motivated volunteers, and just 11.9% has some kind
of sanctions for those who are least motivated. In 42.9% of organizations from our sample, there
is no written contract between the organization and volunteers.
For further analysis, the Data Envelopment Analysis (DEA) was the chosen. To use DEA, parts
of gathered data needed to serve as inputs and outputs in this analysis were picked. Selected inputs
and outputs are explained in following part of this paper.

4. METHODOLOGY AND INTERPRETATION OF OBTAINED RESULTS
As non -governmental organizations are usually non–profit organizations, it is logical to evaluate
their performance according to aspect s other than financial. Mostly non -profit organizations are
aimed at creating social-impact and therefore should be evaluated with respect to specific
inputs/outputs. Epstin and McFarlan (2011), developed a performance metr ics for non -profit
organizations by the grouping organization’ s activitie s into five categories in line with the theory
of change: input, activity, o utput, results and impact. This research went a step further and fuse 11,522,533,544,5
Communication skills Organization skills Social skills Computer skills
Skills are graded 1-5(higher grade -> more important skillset)

this theory with Data Envelopment Analysis (DEA) to determine level of efficiency for 42 CSU
(civil society units ) with respect to their ability to find, motivate and retain volunteers .
Data Envelopment Analysis is known as a nonparametric data oriented approach commonly used
for efficiency evaluation of non-profit organizations like hos pitals (Rabar, D., 2010.) , local
government units (Jardas Antonić, J. et al., 2017.) , universities (Visibal -Caldavid, D. et al., 2017)
and humanitarian organizations (Kim, H and Lee, C.W., 2018.; Tof alis, C and Sargeant, A., 2000. ).
The level of efficiency in this analysis is measured by empirically calculating a n envelope (frontier)
that serves as the reference set for evaluating individual CSUs efficiencies , so it represents ideal
tool for benchmarking .
Being compared with other methods DEA has numerous advantages and some of them are :
 considering simultaneously multiple outputs and inputs in different measurement units,
 measuring relative efficiency and therefore is suitable for benchmarking, because suggests
relative competitiveness by measuring the relative efficiency of the entity subject to the
efficient entity from the reference set,
 it can find if the inefficiency exists and it can suggest potential improvements using
projections.
 For each inefficient unit it calculates its own reference set
 It is more practical then econometrics

The m ost frequently used models in Data Envelopment A nalysis are the CCR (Charnes Cooper
Rhodes) and the BCC (Banker, Charnes Cooper) model. According to them , for each decision
making unit ( CSU) virtual inputs and virtual outputs as well as weights
iv and
ru are formed. Four
general assumptions were followed for each selected input and output (Cooper et al., 2006) :
a) Data should be available and have positive values for each input and output .
b) Inputs, outputs and selected CSUs should reflect management i.e. analyst's interest in
the components entering the evaluation of the relative efficiency.
c) The results of efficiency should be a reflection of the principle according to which a
smaller amount of inputs and a larger quantity of outpu t is preferable.
d) Various inputs and outputs may be expressed in different measurement units.

If it is assume d that m inputs and s outputs satisfy the first two assumptions , and if input and output
vectors are given as
 mj j j j x xxx ,…,,,3 2 1 and
 sj j j j y yyy ,…,,,3 2 1 , then the relative efficiency of
every CSU is measured once in line with the selected data. This means that n optimization problems
should be solved , one for each CSU j for j=1,…. n. The p urpose of the model is to form a virtual
output and input for every DMU by using output weight s (ur) (r = 1, …, s) and input weight s (vi) (i
= 1, …, m). The main goal is to determine the weights that maximize their ratio.
The problem is represented by Cooper, Seiford and Tone (2006) in fractional programming form
as follows :
(
oRP )
mom o osos o o
vu xv xv xvyu yu yu
……max
22 1122 11
,
with respect to
1……
22 1122 11
mjm j jsjs j j
xv xv xvyu yu yu
n j,…,1

0 ,…,,2 1su uu
Once the CSU j is evaluated on the basis of the CSU o, ranging from 1 to n, the fractional
programming can be expressed in a linear form to obtain values for input weights
iv (i=1,…,m)
and output weights
ru (
s r ,…,1 )2 . The f ractional programming problem can thus be
transformed i nto the linear programming form and can be solved
Constraints ensure that the rat io of "virtual output" and " virtual input" do not exceed value 1 for
each CSU. The m ain goal is to obtain weight values (
iv ) and (
ru ) that maximize the ratio of
evaluated unit . In accordance to the defined constraint set, the optimum value obtained for
* is
1.
The CCR and the BCC models differ in one condition. The BCC model includes an additional
condition of convexity , thus achieving that the frontier has piecewise linear and concave features,
leading to the concept of variable return to scale , as shown in Figure 3. The efficiency is the

2 Values ur and vi represent variables of the given problem.

envelope spanning between efficient solutions ( CSU) from the reference set

 
 s
rm
iiji rjr o xv yu j E
1 1* * /:
3 .
Figure 3. Graphical representation of the CCR and BCC models

Graphical representation of the CCR -model Graphical representation of the BCC -model
Source: Authors ’
In the initial analyses there were two additional inputs. However, as they were negatively correlated
with outputs, it was decided to exclude them out from further analysis. It was then decided to
evaluate CSUs with respect to four inputs and two outputs:
SELECTED INPUTS Selected OUTPUTS
x1j – business conditions (I1)
x2j – number of absence (I2)
x3j – education al level of volunteers (I3)
x4 j – awards and privileges (I4)
y1j – length of volunteering (O1)
y2j – number of volunteers (O2)

Data Envelopment Anal ysis is adequate to analyse efficiency in civil society organization units
volunteering because it represents a problem of multiple inputs/outputs with different
measurement units. In this problem Data Envelopment Analysis is used for the analysis of the
relative efficiency of 42 civil society units in the Republic of Croatia. The total number of inputs
and outputs is limited i.e. it should not exceed 1/3 of civil society units taken into analysis . One of

3 According to CCR effic iency definition DMU is CCR efficient if
1* and if there is at least one optimal
solution (
* *,uv ) for which following applies:
0*v ,
0*u .

Efficiency frontier

INPUT
O
U
T
P
U
T

INPUT
Efficiency
frontier
O
U
T
P
U
T

the main traps of the Data Envelopment Analysis is that efficiency scores are sensitive to the
number of included inputs and ou tputs. Namely, the DEA methodology has limitations regarding
the number of inputs and outputs because the efficiency scores can be overestimated in cases when
the number of inputs and outputs is too high in regard to the number of variables i.e. observatio ns.
According to the studied literature, overestimation can be prevented if the number of inputs and
outputs is tied to the number of CSUs (number of observations/ number of observed units) in
the following way (Dyson et al., 2001); n (number of observati ons) > 2ms, where m and s represent
the number of inputs and outputs respectively. Another relevant solution in determining the
adequate number of inputs/outputs, according to Raab and Lichty (2002), is given either by the
relation n > 3(m + s) or according to Despotis (2002), where n ≥ max { m*s; 3(m + s)}. In this case
the maximum number (in sum) of inputs/ outputs should not exceed 14 because the results of the
analysis might be arguable. The decision on the number of selected inputs and outp uts is based on
the assumption that the sum of selected inputs and outputs should be at least two or even three
times smaller than the number of units included in the analysis . As a result it was decided to take 4
inputs and 2 outputs in the sum into the analysis . Data Envelopment Analysis models can be output
or input oriented. Orientation is chosen according to the nature of the problem or the researcher’s
perspective. In an input orientation, the DEA minimizes the input for a given level of ou tput; in
other words, it indicates how much a DMU can decrease its input for a given level of output. In
an output orientation, the DEA maximizes the output for a given level of input; in other words, it
indicates how much a DMU can increase its output for a given level of input.
According to the nature of our problem, the output -oriented model was selected in which the
projections are calculated in the way that the same amount of inputs maximizes the outputs
(number of volunteers i.e. time spent volunteering ).
In the initial analyses two basic output models were used: the CCR – output oriented model and the
BCC -output oriented model. The obtained general and individual results are presented in Tables
1 and 2 respectively.
Table 1 . General results
MODEL CCR BCC
Number of CSU 42 42
Number of relatively efficient CSU 21
(50%) 29
(69%)
Average
0.8343 0.89

Max value 1 1
Min value 0.2576 0.2576
Number of relatively inefficient
units under the average value 18 11
Source: Authors’ calculation

The initial results showed a representative difference in the number of units found efficient by
these two models i.e. 50% and 69% respectively (Table 1) . This significant difference implies
appearance of the variable return to scale (VRS), w hich further implies that changes in inputs do
not cause a linear increase in length of volunteering and number of volunteers in this case.
Therefore, only the BCC output oriented model was continued to be used .
Table 2. Efficiency results with respect to basic models
BCC model results CCR model results
No. DMU Score Rank No. DMU Score Rank
1 CSU1 LGBT,H, EDUC 0.75 34 1 CSU 1 LGBT,H, EDUC 0.75 31
2 CSU2 OOU 0.2727 41 2 CSU 2 OOU 0.2727 41
3 CSU3 OOU 1 1 3 CSU 3 OOU 1 1
4 CSU4 OOU, ZZŽ 1 1 4 CSU4 OOU, ZZŽ 1 1
5 CSU5 NFO 1 1 5 CSU5 NFO 0.7143 35
6 CSU6 UM 1 1 6 CSU6 UM 0.7937 29
7 CSU7 UM 0.5 38 7 CSU7 UM 0.3571 39
8 CSU8 OUK 1 1 8 CSU8 OUK 1 1
9 CSU9 SZU 0.75 34 9 CSU9 SZU 0.5357 37
10 CSU10 SHDU 0.8 32 10 CSU10 SHDU 0.8 26
11 CSU11 SHDU 1 1 11 CSU11 SHDU 1 1
12 CSU12 SHDU 1 1 12 CSU12 SHDU 1 1
13 CSU13 SHDU 1 1 13 CSU13 SHDU 1 1
14 CSU14 SHDU 1 1 14 CSU14 SHDU 0.8223 25
15 CSU15 SHDU 1 1 15 CSU15 SHDU 0.7954 27
16 CSU16 SHDU 1 1 16 CSU16 SHDU 1 1
17 CSU17 SHDU 1 1 17 CSU17 SHDU 1 1
18 CSU18 SHDU 1 1 18 CSU18 SHDU 1 1

19 CSU19 SHDU 1 1 19 CSU19 SHDU 1 1
20 CSU20 SHDU 1 1 20 CSU20 SHDU 0.7954 27
21 CSU21 SHDU 1 1 21 CSU21 SHDU 0.8636 24
22 CSU22 SHDU 1 1 22 CSU22 SHDU 1 1
23 CSU23 SHDU 1 1 23 CSU23 SHDU 1 1
24 CSU24 SHDU 1 1 24 CSU24 SHDU 1 1
25 CSU25 SHDU 0.75 34 25 CSU25 SHDU 0.75 31
26 CSU26 SHDU 1 1 26 CSU26 SHDU 1 1
27 CSU27 SHDU 0.75 34 27 CSU27 SHDU 0.75 31
28 CSU28 SHDU 0.9697 30 28 CSU28 SHDU 0.7924 30
29 CSU29 SHDU 0.7879 33 29 CSU29 SHDU 0.6438 36
30 CSU30 SHDU 0.9091 31 30 CSU30 SHDU 0.7429 34
31 CSU31 SHDU 1 1 31 CSU31 SHDU 0.9107 22
32 CSU32 SHDU 0.5 38 32 CSU32 SHDU 0.5 38
33 CSU33 SHDU, OOU 1 1 33 CSU33 SHDU, OOU 1 1
34 CSU34 SHDU, OOU 1 1 34 CSU34 SHDU, OOU 1 1
35 CSU35 SHDU, UM 1 1 35 CSU35 SHDU, UM 1 1
36 CSU36 SHDU, VZ 1 1 36 CSU36 SHDU, VZ 1 1
37 CSU37 SHDU, VZ 1 1 37 CSU37 SHDU, VZ 1 1
38 CSU38 SPU 0.3846 40 38 CSU38 SPU 0.2857 40
39 CSU39 SUU 0.2576 42 39 CSU39 SUU 0.2576 42
40 CSU40 OI 1 1 40 CSU40 OI 1 1
41 CSU41 VZ 1 1 41 CSU41 VZ 0.9067 23
42 CSU42 VZ 1 1 42 CSU42 VZ 1 1
Source: Authors’ calculation
The obtained results by the BCC output -oriented model show that 69% of 42 CSUs taken into the
analyses are relatively efficient, 31% relatively inefficient and 26.19% below average. All input and
output values taken into the analysis are positively correlated , which confirms that the input and
output data are well selected. The results also indicate that privileges and a higher number of
volunteers do not automatically imply higher level of efficiency .
Table 3. Reference set table
No. CSU SCORE RANK REFERENCE SET (LAMBDA)
9 CSU9 SZU 0.75 34 CSU14 SHDU 0.75 CSU18 SHDU 0.25
25 CSU25 SHDU 0.75 34 CSU12 SHDU 1

27 CSU27 SHDU 0.75 34 CSU12 SHDU 0.867 CSU16 SHDU 0.133
7 CSU7 UM 0.5 38 CSU14 SHDU 0.75 CSU35 SHDU,
UM 0.25
32 CSU32 SHDU 0.5 38 CSU16 SHDU 0.824 CSU18 SHDU 0.176
38 CSU38 SPU 0.3846 40 CSU16 SHDU 1
2 CSU2 OOU 0.2727 41 CSU37 SHDU,
VZ 0.333 CSU40 OI 0.667
39 CSU39 SUU 0.2576 42 CSU37 SHDU,
VZ 0.118 CSU40 OI 0.882
Source: Authors’ calculation

In Table 3, the reference sets for eight inefficient CSUs are presented. The frequency of appearance
of a particular CSU in the reference sets confirms its ranking as an efficient unit. Namely, since all
efficient units are rated with the maximum value of 1, the re -occurrence of a CSU in reference
sets can tell us just how “strong” that evaluation really is. Also, one of the advantages of DEA lies
in the possibility to calculate projections, which may serve as benchmarks for improving efficiency,
we calculated the projections for every inefficient unit in our sample. For example, CSU25 has only
one CSU (CSU12) in the re ference set. This means that CSU25 can improve its performance if it
takes CSU 12 as an example of good practice that generally implies that with an equal level of
inputs , it can improve output s; length of volunteering and number of volunteers . Howe ver, after
calculating projections for the inefficient un it (Table 4) it can be seen that CSU12 has achieved
higher level of volunteers and length of volunteering with the same level of inputs in comparison
to CSU25. According to the projections, CSU25 can improve its performance with the same level
of inputs; educational level, awards and privileges, business conditions, and number of absence,
and still the number of volunteer s can be raised by 50% (i.e for six volunteers more ) and the length
of volunt eering by 33.3% (Table 5 ) and thus it will be moved to the efficiency frontier and thus
become relatively efficient . Same interpretation it can be given for each inefficient unit using its
reference set and projections.
The reason for inefficiency could be found in the volunteers’ age or maybe business conditions do
not include possibility for improvement, s o administrative managers may include more effort to
motivate volunteers to stay and to raise their number. Also, th e inner motivation of volunteer can
be crucial factor becau se most of the efficient CSU are from the humanitarian spectrum, and that
can also be the reason for long length of volunteering.
Table 4. Example of CSU projections

LENGHT H OF
VOLUNTEERING NUMBER OF VOLUNTEERS
DMU Score Rank Data Projection Diff.(%) Data Projection Diff.(%)
CSU25 SHDU 0.75 34 3 4 33.333 12 18 50

Source: Authors’ calculations
Table 5 . Comparison between efficient unit from the reference set and associated
inefficient unit
DMU (I)terms of
business (I)absence (I) training (I) reward (O)lenght of
volunteering (O)No.of
volunteers
CSU12 SHDU 4 1 2 4 4 18
CSU25 SHDU 4 1 2 4 3 12
Source: Authors’ calculations
Projections can be very useful to CSU’s administrative managers and serve them as a benchmark
for detecting the source of inefficiency within their humanitarian organizations . At the same time,
projections can serve as guidelines for improving inputs and thus achieving higher levels of outputs
i.e. duration of volunte ering and number of volunteers.

5. CONCLUSION
It is rather difficult to measure the efficiency of organizations operating within th e civil sector ,
especially non -profit organizations, where the focus is more on social -impact then the financial
outcome . Furthermore, the success of business activities of civil society organizations is almost
entirely dependent of human resources i.e. the enthusiasm of individuals and the working
atmosphere. These two features, as it is well know n, are intangible and hard t o measure. This
problem is even broader because civil society organizations are faced with a constant lack of high
quality human resources or human resources per se. As Data Envelopment Analysis (as a non –
parametric method) is very adoptable toward differe nt measurement units (i.e. they can be
quantitative or qualitative) it enabled us to measure the efficiency of CSUs various variation of
inputs and outputs . In this paper DEA was used to expl ore the level of efficiency of 42 selected
civil society units from the aspect of volunteering. It was based to the four selected inputs (business
conditions, absence, volunteers’ educational level and received awards/privileges) and the achieved
outputs ( length of volunteering, number of volunteers ).
The analysis showed that according to the BCC model 69% of the analy zed 42 CSUs are relatively
efficient, 31% relatively i nefficient and 26. 19% below average. By using the BCC model , the
projections and reference sets were calculated and they might prove useful to decision -makers
(CSU managers) and serve as a guideline to improve efficiency level s. Another advantage of the
DEA method is that it measures the relative efficiency among entities that work in similar
conditions enabling thus be each entity to be compared with others in its group. This allows the
entity to detect the sources of its inefficiency. This way is easier to detect sources of inefficiency.
If more quantitative data could b e obtained and used as an input (like financial data , participation

in and organization of humanitarian and other events etc.), the analysi s would surely yield better
and more accurate results. Moreover, additional data would enable us to use categorical variable to
differentiate CSUs within groups . With th e perspective of efficient units, analysis can be improved
by using super -efficiency model to get distinction between efficient CSU .
In future research on this issu e, DEA can be combined with other methods such as regression or
multi -criterial analysis t o get more detailed quantitative results, or to compare ranked units
respectively . In spite of thes e limitations , the value of this research lies in its empirical approach
performing the empirical approach to efficiency analysis, their ranking into efficie nt and inefficient
units, and possibilities of improvement in form of projections.
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