Original Scientific Article
Efficiency Analysis of Restaurants Operating
in the Rural Areas: The Case of Slovenia
Tanja Planinc
University of Primorska, Faculty of Tourism Studies – Turistica, Slovenia
tanja.planinc@fts.upr.si
Marko Kukanja
University of Primorska, Faculty of Tourism Studies – Turistica, Slovenia
marko.kukanja@fts.upr.si
The purpose of this paper is to analyse the efficiency performance of restaurants op-
erating in the rural areas of Slovenia. The sample consisted of 52 independently run
rural restaurant facilities. Data were obtained from restaurant managers and restau-
rant firms’ financial reports. Based on a convenience sampling method, only those
restaurants whose only source of operating revenues was providing food in a restau-
rant setting were included in the sample. In order to assess restaurants’ efficiency
performance, Data Envelopment Analysis (dea) was used. Financial variables were
used as inputs and outputs to perform dea. This paper contributes to the grow-
ing body of literature in the field of restaurant efficiency measurement by providing
valuable insights into rural-restaurants efficiency performance. The findings of this
study have several significant implications for future research and practice.
Keywords: efficiency, restaurant industry, Slovenia, smes, dea
https://doi.org/10.26493/2335-4194.12.133-145
Introduction
Generally speaking, tourism is a vital economic ac-
tivity and, in the previous decade, tourism achieved
higher growth rates of gross domestic product in com-
parison to the world economy (wttc, 2019b). In
Slovenia, tourism plays a critical economic role. In
2018, tourism contributed to more than 12 of na-
tional gross domestic product and offered employ-
ment to almost 13 of all employees (wttc, 2019a).
Within the tourism industry, restaurants play an es-
sential role. According to the official standard classi-
fication of activities in Slovenia, restaurants are a part
of the category ‘Food and beverage service activities,’
which, together with accommodation activities, rep-
resents the main activity called ‘accommodation and
food service activities.’ Food and beverage service ac-
tivities are comprised of three subsectors (restaurants
andmobile food service activities; event catering; bev-
erage service activities) (see https://www.stat.si). The
food and beverage sector is a vital part of the tourism
industry, since tourists and visitors have to eat, and
food is recognised as an indispensable tourism prod-
uct. Approximately one-third of travel expenditure
can be assigned to food consumption (Bélisle, 1983),
and this figure can be even higher nowadays (Boyne,
2001).
A closer look at the Slovenian food and bever-
age sector reveals that in 2018, there were 6,597 busi-
ness entities (5.4  of all business entities), employ-
ing 18,622 employees (3.41 of all employees). The
largest and most important part of this sub-sector
is restaurants and inns, which represent 55.87 of all
business entities in the food and beverage sector (see
http://www.ajpes.si). Consequently, this sub-sector is
Academica Turistica, Year 12, No. 2, December 2019 | 133
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
the focus of our research. In the food and beverage
sector, most business entities earn revenues not only
from the restaurant business but also from other ac-
tivities (e.g., they also offer accommodation facilities).
The restaurant sector is also characterised by monop-
olistic competition, low barriers to enter the business,
demand volatility, and high fluctuation of employees
(Lee, Hallak, & Sardeshmukh, 2016). Since the focus of
our analysis is the restaurant sector, only those restau-
rant facilities whose sole source of operating revenues
presented the restaurant business were included in the
study.
Rural areas are considered to be economically less
developed regions (Roberts & Hall, 2001) and, ac-
cording to Dashper (2014), such areas are continually
struggling with a decline in traditional economic ac-
tivities (such as agriculture) and population (younger
population is migrating to urban areas). Tourism is
often seen as a tool to revive rural areas since it of-
fers economic and social benefits that can boost rural
development (George, Mair, & Reid, 2009). There-
fore, many governments see rural tourism as a rem-
edy for rural areas and encourage its development in
order to slow down or even reverse the negative trend
of economic and social development (Briedenhann
& Wickens, 2004). A significant segment of tourism
is rural tourism and, according to Roberts and Hall
(2001), tourism in rural areas makes up to 20 of
all tourism activity and more than 20 of European
tourists choose rural areas as their holiday destination
(ibid). Because of the low entry barriers, restaurant
activity is a vital resource in rural development. Rural
restaurants are also a crucial element in promoting lo-
cal food and gastronomy heritage and also represent
an essential element of income source for the local
community (Bessière, 1998).
According to the Organisation for Economic Co-
Operation and Development’s (oecd) definition of
rural areas, the entire Republic of Slovenia is classi-
fied as a rural area. In addition, this definition dis-
tinguishes between two types of rural areas: predom-
inantly and moderately rural regions (Ministrstvo za
kmetijstvo in okolje, 2013). For the purpose of our re-
search, the definition from the Geographical Termino-
logicalDictionary (Geografski terminološki slovar, 2013)
was used, since it defines rural areas as a cultivated
landscape with agriculture and forestry as predomi-
nant economic activities, and with an above-average
share of the rural population. According to Sedmak,
Planinc, and Planinc (2011), this definition is more
convenient when defining and identifying rural areas
in Slovenia.
In order to contribute to the development of ru-
ral areas, tourism companies must perform efficiently.
This means that firms have to produce ‘a maximum
output from a given set of inputs’ (Farrell, 1957, p. 254).
In academic literature, efficiency analysis in the field of
tourism has gained popularity in the previous century
(since 1950). The majority of studies are concerned
with the lodging sector (Poldrugovac, Tekavcic, &
Jankovic, 2016; Barros, Dieke, & Santos, 2010; Pérez-
Rodríguez & Acosta-González, 2007; Barros & San-
tos, 2006; Brown & Ragsdale, 2002), while the restau-
rant sector is somehow neglected (Planinc, Kukanja, &
Planinc, 2018; Alberca & Parte, 2018; Kukanja & Plan-
inc, 2018b; Reynolds & Biel, 2007) although it is a vital
part of the tourism industry.
The main research objective of this paper was to
determine the efficiency level of restaurants located in
Slovenian rural areas and to provide suggestions for
the decision-makers in order to improve their opera-
tional efficiency. The second research objective was to
analyse if there are statistically significant correlations
between restaurants’ physical characteristics and op-
erational efficiency.
Literature Review
There are numerous expressions that describe ru-
ral tourism, such as ‘agritourism, farm tourism, soft
tourism, alternative tourism, ecotourism,’ and others
(Sharpley & Sharpley, 1997, p. 9). The oecd (1994)
suggested that rural tourism should be located in rural
areas, sustainable, connected with local inhabitants,
developed on a small scale and, most importantly, it
should be used in a way to conserve the rural natu-
ral and cultural environment. The term ‘rural tourism’
has also been adopted by the European Community
to refer to all tourism activity in rural areas (Roberts
& Hall, 2001).
Rural tourism offers many potentials benefits for
134 | Academica Turistica, Year 12, No. 2, December 2019
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
rural areas. These benefits can be grouped into three
segments: economic, social, and environmental (Mac-
Nulty, 2004). Generally speaking, jobs in the tourism
sector do not require high education or advanced
training and consequently, tourism can be an essential
element in providing employment for local residents
with lower education (Nigam & Narula, 2011). Job op-
portunities also arise from the fact that, especially in
the restaurant industry, there are low barriers to entry
into the business market (Assaf, Deery, & Jago, 2011).
In addition, tourism also improves local quality of life
through investments in infrastructure and is an im-
portant source of local tax revenues (Nigam&Narula,
2011). As far as environmental benefits are concerned,
rural tourism plays a vital role in protecting the natu-
ral and cultural environment (MacNulty, 2004). Food
expenditures are an essential part of tourists’ and visi-
tors’ budgets, and this is something restaurants in ru-
ral areas should be aware of. In addition, restaurants
in rural areas offer a link between local gastronomy
and culture and can, therefore, be a vital promotion
tool of a rural destination image (Boyne, Williams, &
Hall, 2002).
Regardless of the well-acknowledged importance
of rural tourism, Hall, Roberts, and Morag (2016)
pointed out that rural areas, in general, will gain max-
imum benefit from tourism only when it is engaged as
one part of actions to revive such areas. In the case of
a weak economy and social degradation, tourism can
additionally contribute to income inequality. Never-
theless, although rural tourism is unable to solve the
problems of all rural areas, it still offers numerous
possibilities for economic growth and development
(Dashper, 2014).
According to Farrell (1957, p. 254), a firm is efficient,
when it produces ‘maximum output from a given set
of inputs.’ In operational efficiency measurement, we
compare the observed (actual) and the optimal val-
ues of input(s) and output(s). If the optimal values are
identified in terms of production possibilities, then the
efficiency is defined as technical. In contrast, if the op-
timal values are identified thru firm’s behavioural goals
(in terms of cost, revenue, and profit), the efficiency is
defined as economic (Fried, Knox Lovell, & Schmidt,
2008).
Traditionally, firms have used partial ratio analysis
in order to estimate their operational efficiency and to
perform a benchmark analysis with competitors (Ri-
ley, 1999). Despite its ability to quickly assess the firms’
performance, the usage of ratios has several limita-
tions, including the fact that it only uses two static vari-
ables.
Consequently, there was a growing need for a more
thorough approach to efficiency analyses since firms
use multiple inputs to produce multiple outputs si-
multaneously (Donthu, Hershberger, & Osmonbekov,
2005). As a result, the efficiency of frontier approaches
was developed. There are two groups of frontiermeth-
ods to estimate firms’ operational efficiency, paramet-
ric and nonparametric frontier approaches (Bogetoft
& Otto, 2011). With parametric methods (such as
stochastic frontier analysis (sfa)), there is a need of
a pre-specification of the functional form in the esti-
mation of production frontier technologies (Assaf &
Agbola, 2011), whilst nonparametricmethods (such as
dea) are not so strict. dea forms a production fron-
tier of best practices and enables the calculation of ef-
ficiency scores for each observed unit (Oliveira, Pedro,
& Marques, 2013). An observed unit is 100 efficient
(and therefore lies on the frontier) when no output
can be increased without increasing its inputs (Wöber,
2007). An efficiency score of less than 100  indicates
that the observed unit is inefficient. Efficiency mea-
surement using frontier analysis is a quite useful and
valuable tool in determining critical areas of cost con-
trol (Assaf &Matawie, 2009). Themost crucial benefit
of frontier analyses is that they reveal the gap between
a firm’s actual and optimal performance. In addition,
frontier analyses are able to combine multiple inputs
and outputs simultaneously. This cannot be said for
the traditional methods of performancemeasurement
(accounting-based ratios, cost, volume profit analysis,
etc.), although they are still commonly used in assess-
ing firms’ operational performance.
The abovementioned advantages are the main rea-
son for the rapid growth of academic literature on per-
formancemeasurement with efficiency frontier analy-
ses (Assaf & Josiassen, 2016). In the last decade, the use
of dea has become quite popular for assessing the rel-
ative efficiency of business entities (Martić,Novaković,
Academica Turistica, Year 12, No. 2, December 2019 | 135
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
& Baggia, 2009). In addition, dea has proved to be a
reliable tool for assessing efficiency in various busi-
ness fields (Emrouznejad & Yang, 2018). dea com-
bines multiple inputs and outputs simultaneously, and
it also allows the usage of controllable (within man-
agers’ influence) and uncontrollable variables (man-
agers have no influence on these variables). Requisite
assets (goods and material used, services, labour, tan-
gible and intangible assets) are controllable inputs, and
no business entity can operate without them. There-
fore, it is vital for managers to know how efficiently
they are using them in achieving operational effi-
ciency. In this view, the requisite assets represent a
solid starting point for evaluating a firm’s operational
efficiency.
Efficiency analyses in academic literature in the
field of tourism has gained popularity since the late
1980s and, according to Wöber (2007), 35 studies of
tourism efficiencywith dea were published in the pe-
riod from 1985–2006. Sainaghi, Philips, and Zavarrone
(2017) performed a content analytical meta-approach
on performance measurement in tourism. Most re-
searchers studied the efficiency of the lodging in-
dustry, while the restaurant sector was somehow ne-
glected (although it is a vital part of the hospitality
industry). Their study included almost 1,000 scien-
tific papers, and the efficiency measurement appeared
in 170 papers with a first jump in the volume of paper
in the period from 2007–2010 and a second jump in
the period from 2011–2014.
In the academic literature, there is a growing body
of studies related to the efficiency measurement us-
ing dea in the restaurant industry (see also Table 1).
The first study dates to 1986, when Hrusckha used
the panel database on an aggregated level. He ap-
plied dea analysis for ten restaurant groups and de-
termined differences in efficiency among them (Hr-
uschka, 1986). In the same year, Banker and Morey
used the same method on a chain of fast-food restau-
rants (60 restaurants). They introduced the idea to use
some uncontrollable inputs (age of the restaurant, lo-
cation, etc.) when determining the efficiency scores.
In their model, they modified the input constraints in
a way to disallow the reduction of uncontrollable in-
puts. The result of their analysis suggested that differ-
ent assumptions about controllable anduncontrollable
inputs have a significant impact on efficiency results.
When all inputs were considered to be controllable,
24 restaurants were efficient, but when some inputs
were considered to be uncontrollable, 32 restaurants
achieved efficiency score 1. The fixed nature of uncon-
trollable inputs allows the identification of opportuni-
ties for targeted savings in all controllable inputs that
are used in the analysis (Banker & Morey, 1986).
Then, after almost two decades, researchers re-
discovered dea in the restaurant industry and, in
the last five years, the number of studies intensified
(see Table 1). Some studies have focused on the effi-
ciency of menu items in order to improve restaurant
firms’ financial performance. Taylor, Reynolds, and
Brown (2009) employed dea as an analytic technique
for analysing menu-item efficiency. They analysed 65
menu items in three full-service restaurants and con-
cluded that menu items that are selected with dea
yield higher gross profit. However, their results were
not validated, since the study was performed as a sim-
ulation. Fang and Hsu (2014) analysed 30 menu items
in two restaurants of the same branded chain and
proved that menu items selected with dea increase
the profitability of both restaurants by more than 15
comparedwith the traditionalmenu-engineeringmeth-
od.
Nevertheless, most studies were concerned with
the operational efficiency of restaurants within the
same franchise or chain. Reynolds (2004) analysed
the efficiency of 38 same-brand restaurants and deter-
mined that seven restaurants achieved an efficiency
score of 1 and that restaurants with the highest sales
were not the most efficient. Reynolds and Thomp-
son (2007) analysed the efficiency of a chain of 60
full-service restaurants; they determined that seven
restaurants operated efficiently, and the average effi-
ciency score for all 60 restaurants was 82. Reynolds
and Biel (2007) analysed the efficiency of a chain of 36
casual-theme restaurants where the average efficiency
score was 86, and eight restaurants achieved an ef-
ficiency score of 1. They determined that with more
efficient use of inputs, the restaurants’ income could
be increased by 13.4. Hadad, Friedman, and Hanani
(2007) analysed data from 30 restaurants. They used
136 | Academica Turistica, Year 12, No. 2, December 2019
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
various dea models and consequently obtained two
different sets of efficiency scores. According to the first
scenario, seven restaurantswere fully efficient; accord-
ing to the second scenario, 11 restaurants achieved an
efficiency score of 1. The emphasis of their research is
on the comparison of different ranking methods (e.g.,
restaurant rankings in restaurant guides) with the re-
sults of dea.
Giménez-García,Martínez-Parra, andBuffa (2007)
performed a dea analysis with the data of 54 Spanish
fast-food restaurants. According to the analysis, due
to input reallocation, sales and service quality can be
increased on average by 4.20. Roh and Choi (2010)
employed dea analysis to evaluate the efficiency of
three brandswithin the same restaurant franchise. The
sample consisted of 136 restaurants, and the efficiency
was assessed based on interviews with managers. The
analysis revealed that the average efficiency score is
73 and that the efficiency results of each brand differ
significantly from the others. In some cases, it was de-
termined that the restaurant size and managers’ expe-
rience have a positive and statistically significant im-
pact on efficiency scores, meaning that larger restau-
rants with more experienced managers achieve higher
efficiency scores (Assaf et al., 2011). Kukanja and Plan-
inc (2018a) used secondary financial data to analyse
the efficiency of 142 restaurants. The average efficiency
score was 85, and 23 restaurants were fully efficient.
Labour costs and depreciation proved to be the main
areas for efficiency improvement.
Some researchers used panel data in determining
the efficiency over a specific time period. For exam-
ple, Giokas, Eriotis, and Dokas (2015) analysed the
efficiency of 21 Greek restaurant companies in pre-
recession and recession and recovery periods (2006–
2012). The results reveal that the average efficiency
scores are 0.85, 0.80, and 0.80 and that most com-
panies had no significant change in their efficiency
while three companies had a significant efficiency de-
crease. Mhlanga (2018) analysed the efficiency of 16
South African restaurants in a four year period (2012–
2016). Four restaurants achieved an efficiency score of
1 at some point in the four-year period. Full-service
restaurants have higher efficiency scores in compari-
son to fast food and casual restaurants.
In addition, the location and revenue per available
seat have a statistically significant positive impact on
restaurants’ efficiency. Parte and Alberca (2019) ex-
amined the efficiency of 1,071 Spanish bar companies
for the 2005–2014 period. The mean efficiency scores
ranged from 0.673 in 2005 to 0.711 in 2014. Companies
improved their efficiency through reducing inputs (the
number of employees, labour costs and operational
costs). The results also revealed the levels of employ-
ees’ education and employment rate are significantly
and positively correlated with efficiency. In contrast,
low wages and long working hours are significantly
and negatively correlated with efficiency.
Efficiency studies in the period from 1986 to 2019
are presented in Table 1. Most studies used sales rev-
enues as an output in the analyses. However, there
is much more inconsistency when it comes to the
selection of inputs. Consequently, the efficiency re-
sults of the presented studies are not fully compara-
ble. This would not be the case if there were a stan-
dardised selection of inputs and outputs, as in some
other business sectors, such as banking. Assaf and
Josiassen (2016) also concluded that the selection of
inputs and outputs is driven mainly by data availabil-
ity rather than theoretical arguments. Efficiency scores
of restaurant firms from previous studies vary from
46.17 (Assaf et al., 2011) up to 86 (Reynolds & Biel,
2007). Efficiency scores vary because of differences
in the variables, different characteristics of restaurant
firms, and because researchers used different models
of dea. Specifically, several authors (Reynolds, 2003;
Roh & Choi, 2010; Assaf et al., 2011) emphasised the
importance of correlation analyses between inputs
and outputs before performing dea. Interestingly, the
analysis of studies presented in Table 1 reveals that
most studies do not provide any evidence of corre-
lation analyses between inputs and outputs. The only
exceptions are the studies of Reynolds and Biel (2007),
Reynolds and Taylor (2011), Roh andChoi (2010), Tay-
lor et al. (2009), and Kukanja and Planinc (2018a). In
addition, researchers use different dea models within
the same research without proper theoretical justifica-
tion for such action.
Therefore, we decided to use requisite assets as
variables in our research, since no business entity can
Academica Turistica, Year 12, No. 2, December 2019 | 137
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
Table 1 Efficiency Studies in the Restaurant Industry
Authors Inputs Outputs
Hruschka (1986) No. of seats, labour costs, costs of goods sold, other
operating expenses
Sales
Banker and Morey (1986) Costs of goods sold, labour costs, age of the restaurant,
advertising expenditures, location, existence of a ‘drive-in’
window
Sales
Reynolds (2003) No. of labour hours Sales
Reynolds (2004) No. of labour hours, average salary, no. of seats, no. of
competitors
Sales, tips
Reynolds and Thompson
(2007)
Average salary, no. of seats Sales, tips
Reynolds and Biel (2007) Costs of goods sold, labour costs, employee satisfaction, no.
of seats, taxes and insurance
Profit, retention equity
Hadad, Friedman and Hanani
(2007)
No. of seats, no. of all employees, no. of employees in a
shift, total size
Average no. of guests per day,
average selling price
Giménez-García,
Martínez-Parra and Buffa
(2007)
No. of all employees, seats and server, no. of competitors,
average spending per guest
Sales, service quality
Taylor, Reynolds, and Brown
(2009)
Meal method preparation, no. of purveyors, no. of stations Gross profit, meal popularity
Roh and Choi (2010) Total size, hall size, kitchen size, no. of seats, no. of tables,
no. of all employees, no. of kitchen and hall, monthly salary
and rent, overhead expenses
Sales, net income
Assaf, Deery, and Jago (2011) No. of seats, no. of employees, food and beverage costs Sales from food, sales from
beverages
Fang and Hsu (2014) Labour costs, costs of goods sold, no. of purveyors Gross profit, meal popularity
Giokas, Eriotis and Dokas
(2015)
Operating expenses (without costs of goods sold), assets
value
Sales
Mhlanga (2018) No. of employees, no. of seats, labour costs, other operating
expenses
Sales, no. of covers
Kukanja and Planinc (2018a) Labour cost, depreciation, costs of goods sold, costs of
services
Sales
Alberca and Parte (2018) Labour costs, other operating expenses, assets value Sales
Parte and Alberca (2019) No. of employees, labour costs, other operating expenses,
assets value
Sales
operate without them. It is also necessary formanagers
to know how efficiently they are using them in achiev-
ing operational efficiency. In this view, the requisite
assets present a solid starting point for evaluating a
firm’s operational efficiency.
Research Methodology
In order to acquire primary data, a questionnaire was
developed based on previous studies on efficiency
measurement in the restaurant industry (presented in
Table 1). The questionnaire consisted of two parts; the
138 | Academica Turistica, Year 12, No. 2, December 2019
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
first was concerned with the socio-demographic char-
acteristics of managers (gender, age, education, work-
ing experience), while the second dealt with the char-
acteristics of restaurants (size, number of seats, em-
ployees, competitors, etc.). Secondary data consisted
of financial data for the year 2018 (net sales revenues;
acquisition cost of goods and material sold and costs
of material; costs of services; labour costs; deprecia-
tion). Financial data were obtained by the Agency of
the Republic of Slovenia for public legal records and
related services (http://www.ajpes.si). The availability
of official financial data is a significant advantage of
our research since we can avoid the subjective opin-
ions of restaurant managers on financial matters.
In order to achieve the main objective of this re-
search, data were gathered from 52 restaurant facil-
ities, located in rural areas in three municipalities in
the countrywith the highest number of overnight stays
(Ljubljana; Piran; Bled). The oecd definition classi-
fication of Slovenian rural areas was not useful for
defining the sample for our research. Therefore, we
used the definition from theGeographical Terminolog-
ical Dictionary (Geografski terminološki slovar, 2013),
as already explained in the Introductory chapter.
Barrows,Vieira, andDiPietro (2015) recommended
being cautious in identifying the competitive set for
the benchmarking process. Therefore, we included
restaurant facilities which are similar according to
their operating variables. Restaurant facilities had to
be officially classified as restaurants and inns; had to
be run independently, and the restaurant activity had
to be the only source of restaurants’ operating rev-
enues. Based on the convenience sampling method,
we selected 250 business entities. All of them were
prechecked in extensive field research. Fieldwork was
conducted by ten interviewers during the summer and
autumn of 2018. Only those facilities that met all the
above-mentioned criteria were included in the study.
According to interviewers’ feedback, many managers
refused to participate in our research for a variety of
reasons. In the end, the final sample consisted of 52
independently-run restaurant facilities.
The demographic data of managers and physi-
cal characteristics of restaurants were analysed with
the spss 24 software. In order to determine the effi-
Table 2 Correlation Coefficients between Inputs
and Output
() () () () ()
Pearson Corr. .** .** .** .**
Sig. (-tailed) .** .** .** .**
Notes ** Correlation is significant at the 0.01 level (2-
tailed). Column headings are as follows: (1) net sales rev-
enues, (2) acquisition cost of goods and material sold and
costs of material, (3) costs of services, (4) labour costs, (5)
depreciation.
ciency levels, we conducted a dea analysis with the
software deap version 2.1. We opted for an input-
oriented dea model since restaurant managers have
a much higher influence on the inputs than on the
outputs (Mhlanga, 2018) and, in such a robust com-
petitive environment, firms are usually input-oriented
(Barros, 2005). In addition, input-oriented models are
a measure of competitiveness (Oliveira et al., 2013).
In the input-oriented model, we want to determine
by how much the input(s) can be reduced without
changing the output(s) (Coelli, Rao,O’Donnell, & Bat-
tese, 2005). We also used the constant returns to scale
(crs) option, since in the restaurant industry there is
a strong monopolistic competition and all firms have
the possibility of operating at an optimal and similar
scale (Coelli et al., 2005). The efficiency scores were
calculated based on one output (net sales revenues)
and three inputs (acquisition cost of goods and mate-
rial sold and costs of material, costs of services, labour
costs, and depreciation). Prior to calculating the effi-
ciency scores, it is necessary to verify that all inputs
were correlated with the output. The results are pre-
sented in Table 2.
Results and Discussion
Firstly, we analysed the demographic data ofmanagers
and the physical characteristic of restaurant facilities.
The results are presented in Table 3. The analysis of
the data revealed that there were slightly more male
respondents (51.9) than females (48.1) and the av-
erage age of the respondents was 45.15 years. Most
(80.8) respondents had a high-school education, and
all the rest (19.2) achieved a college or faculty degree.
Academica Turistica, Year 12, No. 2, December 2019 | 139
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
Table 3 Socio-Demographic Data of Managers
Variable Item f 
Gender Female  .
Male  .
Age –  .
–  .
–  .
–  .
More than   .
Level of
education
Elementary school  .
Vocational or secondary school  .
College/faculty degree  .
Master’s degree or PhD  .
Working
experience
–  .
–  .
–  .
–  .
More than   .
Ownership
structure
Owner and manager  .
Manager  .
Table 4 Restaurants’ Characteristics
Variable () ()
Size of the restaurant (m2) . .
Number of seats . .
Number of employees . .
Age of the restaurant . .
Number of competitors . .
Average spending per guest in eur . .
Notes Column headings are as follows: (1) mean, (2) stan-
dard deviation.
As far as working experience in the restaurant indus-
try is concerned, managers had, on average, almost 23
years of working experience. The majority of respon-
dents (82.7) owned the restaurant facility and were
also employed asmanagers, while the rest (17.3) were
only managers.
In the next step, we analysed the characteristics of
restaurants facilities. The results are presented in Table
Table 5 Financial Data in Euros
Variable () ()
Acquisition cost of goods
and material sold and costs
of material
,. ,.
Costs of services ,. ,.
Labour costs ,. ,.
Depreciation ,. ,.
Net sales revenues ,. ,.
Notes Column headings are as follows: (1) mean, (2) stan-
dard deviation.
4. The analysis of restaurant facilities characteristic in-
dicated that the average size of a restaurant was 248.5
square metres and the average number of seats was
136. In terms of the number of employees, more than
half of all restaurants included in the sample (51.92
) employed up to 5 employees, 32.70 employed
from 6 to 10 employees, and only three restaurants
(5.77) employed more than 15 employees. On aver-
age, the restaurants had been in business for 38 years,
and 78.8 of them were run as family businesses.
As suggested by Reynolds (2004), we also gath-
ered information on the number of competitors in
the vicinity of a restaurant facility. Almost half of the
restaurants (46.15 ) had 0 or 1 competitors, while all
the rest (53.84) had 2 or 3 competitors within a 1 km
radius. The average spending per guest (person – asp)
in all restaurants was almost 14 euros.
Next, we analysed the financial data of all 52 restau-
rant facilities for the year 2018. As already mentioned,
the financial data form statements of income were ob-
tained from national authorities. The results are pre-
sented in Table 5.
According to the results presented in Figure 1, only
nine restaurant firms achieved a score of 1, which in-
dicates that they are fully efficient (100). The aver-
age value of efficiency of all 52 firms under observa-
tion is 67. Based on research results, we can conclude
that on average, the evaluated firms could reduce their
inputs by 33 and simultaneously maintain the same
level of total sales revenues (the output). Detailed re-
sults on efficiency scores are presented in Figure 1.
Twenty-nine restaurants achieved and efficiency
140 | Academica Turistica, Year 12, No. 2, December 2019
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants











                 
Effi
ce
nc
y
sc
or
es
Restaurants
          




     


     
  
    
 
    
    
  
Figure 1 Efficiency Scores
score below the average efficiency score, while 23 res-
taurants achieved above the average efficiency. De-
tailed analysis of restaurants that had achieved the ef-
ficiency score below the average revealed that when
it comes to the input ‘costs of labour,’ there is much
room for optimisation. This is also true for other in-
puts but to a lesser extent. According to the analysis,
the costs of labour could be, on average, lower bymore
than 60.Although all requisite assets are controllable
inputs, managers cannot lower them to the recom-
mended extent. Because of already low salaries in the
restaurant industry (see explanation below) and also
due to the Slovenian tax legislation, managers can-
not afford such a drastic cost reduction. The average
monthly gross salary in the restaurant industry for the
year 2018 in Slovenia was €1,056.22, while the average
gross salary for all business subjects was €1,681.55 (see
https://pxweb.stat.si). The actual net salaries in Slove-
nia are lower by more than 30 due to relatively high
tax burdens. Consequently, restaurant managers have
little or no space in terms of labour cost reductions.
Another significant issue is concerned with the
lack of people willing to work in the restaurant indus-
try. The Slovenian government addressed this issue
by changing the legislation in 2005 by eliminating the
condition of mandatory education for professions in
the tourism and restaurant industry (waiters, cooks,
receptionists, etc.) (Zakon o spremembah in dopol-
nitvah Zakona o gostinstvu, 2005). The change in leg-
islation did not bring the desired effect. According to
Table 6 Correlation Coefficients between Efficiency
Scores and Restaurants’ Physical Characteristics
() () () () ()
Pearson Corr. –. –. –. –.*
Sig. (-tailed) . . . .
Notes * Correlation is significant at the 0.05 level (2-tailed).
Column headings are as follows: (1) efficiency scores, (2)
restaurant size, (3) no. of seats, (4) average spending per per-
son, (5) age of a restaurant.
Zupančič (2019), the government should take some
steps in reducing the tax burden on salaries which
would result in higher net salaries.
Regarding the cost of goods and material sold and
costs of material, managers should consider optimis-
ing costs through inter-firm networking. According
to Sedmak et al. (2011), there are still unexploited po-
tentials in networking between firms in the Slove-
nian hospitality sector, as research results revealed that
managers see the inter-firmnetworking as a possibility
to gain access to more reliable and favourable suppli-
ers.
Next, we verifiedwhether there are correlations be-
tween efficiency scores and restaurants’ physical char-
acteristics, which would help us to better understand
the efficiency level in rural restaurant facilities. We
considered the size of a restaurant, the number of seats,
the average spending per guest, and the age of restau-
rants. The results are presented in Table 6.
The results of the correlation analysis showed that
only the age of restaurants has a weak negative statis-
tically significant correlation with the efficiency scores
(r = –0.279, 2-tailed Sig. = 0.045). Although the cor-
relation coefficients do not indicate the direction of
causality, we might assume that restaurants operat-
ing for a more extended period of time are becoming
less efficient. One possible explanation could be that
restaurants in their early life cycle stages put more ef-
fort inmarketing actions in order to become recognis-
able than in their maturity and decline phase, which
results in higher operational efficiency (on average
restaurants have 38 years of business activity). Nev-
ertheless, further research should be undertaken to
investigate the influence of different life cycle stages
Academica Turistica, Year 12, No. 2, December 2019 | 141
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
and years of business activity on restaurants’ opera-
tional efficiency.
Conclusion
The primary goal of this paper was to determine the
efficiency of selected restaurants operating in rural ar-
eas. We decided to use the requisite assets as inputs
since no business can operate without them. Accord-
ing to the analysis, the selected restaurants achieved
an average efficiency score of 67. This result is not
in line in comparison with other studies of restaurant
efficiency, where the identified efficiency results were
higher (Banker & Morey, 1986; Choi, Roh, & Yoon,
2007; Giménez-García et al., 2007; Reynolds, 2004;
Reynolds & Biel, 2007; Reynolds & Thompson, 2007;
Giokas et al., 2015; Kukanja & Planinc, 2018a; Parte &
Alberca, 2019). On the contrary, only in a few studies
were the identified average scores below our results
(Alberca & Parte, 2018; Assaf et al., 2011; Hadad et al.,
2007).
The comparison of results between our study and
previous studies is difficult if not impossible since dif-
ferent inputs and outputs had been used in different
dea studies (see also Table 1). The direct comparison
is possible only with the results of Kukanja and Plan-
inc’s (2018a) study; the authors had used the same in-
puts and output as they were used in our study and
calculated the average efficiency of 142 restaurants at
85. In their study, restaurants were located predom-
inantly in Slovenian urban areas, while in our case,
restaurant facilities were located exclusively in rural
areas. We can assume that restaurants in urban areas
can generate higher revenues since urban areas offer a
higher number and frequency of guests. Restaurants in
urban areas are also more often characterised as more
luxurious and can consequently charge higher prices
for their offerings.
This research also has some other limitations that
must be considered. dea analysis is a deterministic
method and, consequently, every observed unit that
does not lie on the efficiency frontier is characterised
as an inefficient unit (Fried, Knox Lovell, & Schmidt,
1993). The financial data used in our study presented
only one business year (2018). Therefore, it would be
of great interest to use the panel data. Another limita-
tion is concerned with the sample size and geographi-
cal distribution of restaurant facilities. It would be nec-
essary to broaden the research in order to get more
conclusive results of the analysis.
Regarding the practical implications of this study,
we provided some valuable information for restaurant
managers and decision-makers in terms of identify-
ing areas where further optimisations are possible and
necessary in order to improve restaurant firms’ effi-
ciency. The analysis revealed that managers should
primarily focus on optimising labour costs. Unfortu-
nately, we also determined that managers have little or
no space for such optimisation because of the already
low salaries in the restaurant industry and because of
the strict labour and tax legislation. Therefore, an es-
sential area for restaurants’ operational efficiency im-
provement might be inter-firm networking, especially
in terms of optimising the costs of goods and material
sold and costs of material.
In order to ensure comparable benchmarking anal-
yses, we should also emphasise the importance of the
usage of objective and reliable information in deter-
mining the efficiency levels. Objective and reliable
data is a prerequisite for conducting effective efficiency
analyses.
For an in-depth understanding of restaurants’ effi-
ciency, further research is needed. Researchers should
focus on analysing management practices of the best-
performing restaurants. In addition, the element of
quality of input and output variables is also worth fur-
ther investigation, since the use of appropriate vari-
ables is imperative in understanding the achieved effi-
ciency levels.
Further research should consider incorporating
firms’ non-financial indicators into the efficiency anal-
ysis. Non-financial indicators are becoming increas-
ingly important in evaluating the business perfor-
mance and, in combination with financial indicators,
represent a balanced scorecard consisting of four per-
formance elements: financial, customer service, inter-
nal processes, and the learning and growth aspect (Ka-
plan & Norton, 1992). The fact is that relying solely on
financial analysis is not advisable since companies are
often able to tailor business results in one way or an-
other (Atkinson & Brown, 2001; Hansen, Otley, & Van
142 | Academica Turistica, Year 12, No. 2, December 2019
Tanja Planinc and Marko Kukanja Efficiency Analysis of Restaurants
der Stede, 2003). The combination of financial and
non-financial indicators is imperative in today’s com-
petitive environment. Many firms already use non-
financial indicators related to customer satisfaction,
product quality, achieved market share, corporate so-
cial responsibility, environmental indicators (e.g., car-
bon footprint, green practices, etc.), firms’ organisa-
tional climate, etc. (Kaplan & Norton, 1996; Tarigan
& Widjaja, 2012; Banker, Potter, & Srinivasan, 2005;
Sainaghi et al., 2017).
Since the restaurant industry is a vital part of the
tourism industry, there is a need to establish a stan-
dardised selection of inputs and outputs. Ensuring an
appropriate system for standardised efficiency analy-
ses would significantly contribute to accurate bench-
marking of the restaurant industry worldwide.
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