Intersectoral Linking of Agriculture, Hospitality, and Tourism—A Model for Implementation in AP Vojvodina (Republic of Serbia)

Abstract

This study explores the establishment of intersectoral linkages between agriculture, hospitality, and tourism in the microregion of AP Vojvodina, Serbia, with a focus on developing a model that identifies the key factors for its effective implementation. For research purposes, the Delphi method, pilot testing, and advanced statistical techniques are used to validate the model. The Sustainable Intersectoral Linking Model in Agriculture, Hospitality, and Tourism (SILM-AHT) is developed, encompassing 37 indicators distributed across five factors: Sustainability, Education, Government Policy, Contribution of Farmers and Hospitality Providers, and Infrastructure. The SILM-AHT model can serve as a valuable tool for policymakers, enabling the monitoring and evaluation of sustainable development strategies. Its further practical application is recommended, along with the development of sustainable and well-coordinated activities and programs involving all the relevant stakeholders.

Full text

Academic Editors: Xosé Antón
Armesto-López, Martí Cors-Iglesias
and M. Belén Gómez Martín
Received: 13 February 2025
Revised: 6 March 2025
Accepted: 7 March 2025
Published: 11 March 2025
Citation: Pauni´c, M.; Kalenjuk
Pivarski, B.; Tešanovi´c, D.; Ivanovi´c,
V.; Vujasinovi´c, V.; Gagi´c Jarakovi´c, S.;
Vuli´c, G.; ´
Ciri´c, M. Intersectoral
Linking of Agriculture, Hospitality,
and Tourism—A Model for
Implementation in AP Vojvodina
(Republic of Serbia). Agriculture 2025,
15, 604. https://doi.org/10.3390/
agriculture15060604
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
Intersectoral Linking of Agriculture, Hospitality, and
Tourism—A Model for Implementation in AP Vojvodina
(Republic of Serbia)
Maja Pauni´c 1, Bojana Kalenjuk Pivarski 1, Dragan Tešanovi´c 1, Velibor Ivanovi´c 1, Vesna Vujasinovi´c 1,* ,
Snježana Gagi´c Jarakovi´c 1, Gordana Vuli´c 2,3 and Miloš ´
Ciri´c 4
1Department of Geography, Tourism and Hotel Management, Faculty of Sciences, University of Novi Sad,
21000 Novi Sad, Serbia; maja.banjac@dgt.uns.ac.rs (M.P.); bojana.kalenjuk@dgt.uns.ac.rs (B.K.P.);
dragan.tesanovic@dgt.uns.ac.rs (D.T.); velibor.ivanovic@dgt.uns.ac.rs (V.I.);
snjezana.gagic@dgt.uns.ac.rs (S.G.J.)
2College of Management Bled, 4260 Bled, Slovenia; gordana.vulic@bic-lj.si
3Biotechnical Educational Centre Ljubljana, 1000 Ljubljana, Slovenia
4Vocational School of Hotel and Tourism Management, 11000 Belgrade, Serbia;
milos.ciric.gastronom@gmail.com
*Correspondence: vesna.vujasinovic@dgt.uns.ac.rs
Abstract: This study explores the establishment of intersectoral linkages between agricul-
ture, hospitality, and tourism in the microregion of AP Vojvodina, Serbia, with a focus on
developing a model that identifies the key factors for its effective implementation. For
research purposes, the Delphi method, pilot testing, and advanced statistical techniques
are used to validate the model. The Sustainable Intersectoral Linking Model in Agricul-
ture, Hospitality, and Tourism (SILM-AHT) is developed, encompassing 37 indicators
distributed across five factors: Sustainability, Education, Government Policy, Contribution
of Farmers and Hospitality Providers, and Infrastructure. The SILM-AHT model can serve
as a valuable tool for policymakers, enabling the monitoring and evaluation of sustainable
development strategies. Its further practical application is recommended, along with the
development of sustainable and well-coordinated activities and programs involving all the
relevant stakeholders.
Keywords: intersectoral linking; agriculture; hospitality; tourism
1. Introduction
Globally, the establishment of sustainable intersectoral linkages between tourism and
agriculture is increasingly being explored through the application of various models [
1
–
5
].
The intersectoral connections between agriculture, hospitality, and tourism have become
a growing topic within the scientific community, as confirmed by numerous relevant
studies [
6
–
8
]. Various models in the literature are tailored to the specific characteristics of
microregions, bridging theory and practice for sustainable development [
9
,
10
]. In most
cases, the research has focused on local food and the interconnections between tourists and
farmers [
7
,
11
–
15
]. Notably, hospitality providers, who play a crucial role in distributing
agricultural and gastronomic products to tourists, have often been overlooked [16–19].
It is important to highlight that tourism planners have recognized the significant
potential for developing intersectoral cooperation between tourism, hospitality, and agri-
culture [
20
–
23
]. However, many researchers have pointed to the weak connections among
these sectors [
24
–
26
]. Such cooperation is more successful in microregions where farmers
Agriculture 2025,15, 604 https://doi.org/10.3390/agriculture15060604

Agriculture 2025,15, 604 2 of 22
produce authentic food products, which are then distributed to both tourists and hospital-
ity providers [
24
,
27
]. This system is typical of developed countries that strategically plan
approaches in this field [
23
,
26
]. It is evident that the establishment of intersectoral linkages
depends on a country’s level of economic development [
28
–
30
]. In developed countries,
this process is easier to implement compared to in developing nations, where numerous
barriers hinder the adoption of these concepts [26,27].
The Republic of Serbia has significant potential for developing intersectoral linkages
between agriculture, hospitality, and tourism [
31
]. The northern province of Serbia, AP
Vojvodina, is among the most developed regions in the country. Agricultural production
exceeds domestic demand, with a portion exported [
32
]. As such, this region is ideal
for intersectoral linkage development, as agricultural and gastronomic products can be
produced and consumed within a 100 km radius within the tourism sector. The geographic
proximity of the major urban tourism centers, Belgrade and Novi Sad, further supports this
development [33,34].
Despite the evident potential, several limitations exist [
35
,
36
]. The Tourism Devel-
opment Strategy of the Republic of Serbia [
37
] for the period 2016–2025 has focused on
increasing visitor numbers. However, planning programs for intersectoral cooperation
have been neglected [
33
]. Additionally, the Agriculture and Rural Development Strategy
(2014–2024) [
38
] has not sufficiently addressed the preservation of product authenticity,
processing methods, and preparation techniques [
34
]. This is despite the fact that local and
traditional food in Vojvodina is considered a significant factor for the region’s sustainability
based on tourism [39,40].
The research examining the impact of local agriculture on the development of hos-
pitality and tourism in Vojvodina has revealed that cooperation between hospitality es-
tablishments and farmers is not well established and is only partially recognized as a
key component of future development [
34
,
35
]. ´
Ciri´c et al. (2022) indicated that tourism,
agriculture, and hospitality have the potential to function symbiotically, yet they noted that
the tourism sector does not sufficiently contribute to local community development [41].
Given all the above, this study focuses on developing a model for the intersectoral
linkages between agriculture, hospitality, and tourism. For this reason, the following
research questions are posed in this study:
Q1: what are the key methodological approaches used in the existing models of
intersectoral linkages, and what is the potential for their application in AP Vojvodina?
Q2: what are the main factors to consider when identifying the intersectoral linkages
between agriculture, hospitality, and tourism in AP Vojvodina?
Q3: are there differences in the attitudes of farmers, hospitality providers, and
tourism stakeholders regarding the key indicators for establishing intersectoral linkages in
the region?
The logical coherence of the research questions is reflected in their sequential and
complementary nature, which enables a comprehensive understanding of the intersectoral
linkages between agriculture, hospitality, and tourism. The first research question lays the
foundation of this study through a theoretical analysis of the existing models of intersectoral
linkages, identifying the key methodological approaches and assessing their applicability in
the specific regional context of AP Vojvodina. This question plays a crucial role in defining
the research framework and methodology, as it facilitates the selection of appropriate
approaches for further empirical analysis. The second question builds on the first by
developing a new model and empirically testing it. The third research question follows
on from the previous one, providing a deeper understanding of the perceptions and the
heterogeneity of attitudes among the stakeholders from different sectors regarding the
key indicators.

Agriculture 2025,15, 604 3 of 22
The aim of this study is to propose a model suitable for identifying the intersectoral
linkages between agriculture, hospitality, and tourism in Vojvodina, based on existing
models and the competencies of farmers, hospitality providers, and tourism employees.
The structure of this study consists of five sections. The introduction presents fun-
damental facts about the research topic, setting the framework for further analysis. The
literature review focuses on the key aspects closely related to the research questions and
objectives. The methodological section describes the research context, the approach to
model development, and the statistical methods. The research results present the processed
data obtained through the appropriate statistical analyses. In the concluding section, a
discussion of the findings and final considerations is provided, emphasizing the theoretical
and practical contributions of the research, as well as its limitations and recommendations
for future studies.
2. Literature Review
2.1. Models of Intersectoral Linkages in Tourism
For the analysis of the available literature on intersectoral linkages in tourism, the
SCOPUS database was used to identify the relevant models and approaches. During the
period from 2000 to March 2023, a total of 493 articles in English were selected, in which the
keywords “intersectoral linkages in tourism” and “indicators of sustainable development in
tourism” were used. The review confirmed that numerous models and various approaches
have addressed the intersectoral linkages in tourism, with many authors contributing to a
better understanding of this concept [27,42].
Andersson et al. (2017) [
5
] developed the Synergy Model among agriculture, hospi-
tality, and tourism for Scandinavian regions. A model that has yielded positive results in
China is Community-Based Tourism (CBT). It is based on the capital connection of assets
and local environments for the development of intersectoral linkages in rural, underdevel-
oped areas near World Heritage Sites [43,44].
Development policymakers in Italy believe that multiple models can encourage the
establishment of synergies between agriculture and hospitality. Some of these include the
Rural Renewal Program (RRP) [
45
] and Unlocking Value Creation Using an Agritourism
Business Model [
46
]. In Senegal, the identification and development of intersectoral link-
ages between agriculture and tourism are realized through the Structure Path Analysis
(SPA) approach [
26
]. The Empowerment Model for Sustainable Tourism Villages in Indone-
sia highlights the importance of spatial and sectoral approaches, human resources, and IT
in the development of sustainable tourism [47].
Choya et al. [
48
] developed a model based on supply chain management, incorporat-
ing the factors crucial for establishing intersectoral linkages. Richardson-Ngwenya and
Momsen (2011) identified six factors essential for intersectoral linkages [
49
]. Kock (2013)
emphasized that intersectoral linkages are closely related to sustainable development.
He expanded Moson’s model by incorporating three dimensions of sustainable develop-
ment, and named it the Food Management Model in Tourism, which has been applied in
Aruba [50].
The possibility of applying existing models of intersectoral linkages to AP Vojvodina
arises from the region’s potential for developing synergistic relationships between agri-
culture, hospitality, and tourism [
31
]. Although the analyzed models were developed in
different socio-economic and geographic contexts, their key principles, such as involving
the local community, identifying the attitudes of stakeholders in the chain, valorizing
authentic products, and strengthening local supply chains, can be adapted and applied
to AP Vojvodina [
34
,
35
]. A particular importance is placed on directing development

Agriculture 2025,15, 604 4 of 22
toward sustainable tourism that contributes to preserving local identity and diversifying
the income of rural areas [26,44,47].
Based on the presented research and findings, the first research question is posed:
Q1—what are the key methodological approaches used in existing models of intersectoral
linkages, and what is the potential for their application in AP Vojvodina?
2.2. Development of a Model for Intersectoral Linkages Between Agriculture, Hospitality, and
Tourism in Vojvodina
Regarding their methodological approaches, authors dealing with intersectoral link-
ages have recommend applying a multidisciplinary approach for collecting primary
data [
35
,
43
]. Particular importance has been given to establishing synergy between the
qualitative and quantitative techniques, which include the use of official statistical data; an
analysis of the development strategies and plans of the relevant stakeholders; and collecting
the opinions of the key actors through interviews, surveys, and workshops. Additionally,
secondary data can be useful for further analysis and contextualization [51].
The authors of the presented models, along with the many other researchers studying
the intersectoral linkages in tourism, have emphasized the need for further research aimed
at identifying the key indicators for their successful implementation, while adapting to the
specific characteristics of the microregion in which a study is conducted [5,26,43,45,47,50].
Since Serbia is a developing country, the possibility of applying and forming certain
models is challenging [
35
,
36
]. Although numerous data are available, a multidisciplinary
approach, crucial for developing these models, is not fully applicable. The official statistical
data are often incomplete, and the 50-year gap between agricultural censuses further com-
plicates the analysis. Evaluations of strategic plans in tourism and agriculture are largely
missing, while the current statistical monitoring is not aligned with the key indicators
needed for effective and sustainable sectoral linkages [34].
Due to these limitations, the recommendations from researchers on how to proceed
in such situations have been considered [
43
,
49
,
50
,
52
]. This study focuses on developing a
basic model for the intersectoral linkages between agriculture, hospitality, and tourism in
AP Vojvodina (Republic of Serbia), based on identifying the key factors that can contribute
to the sustainable integration of these sectors.
Based on the presented research, the second research question (Q2) is formulated:
what are the main factors to consider when identifying the intersectoral linkages between
agriculture, hospitality, and tourism in AP Vojvodina?
2.3. Heterogeneity of Stakeholder Attitudes Toward Intersectoral Linkages in Tourism
A study investigating the dynamic model of sustainable tourism suggested that there is
no single intersectoral optimum that would equally satisfy the perception of all the actors in a
chain, as their interests are diverse and heterogeneous [
53
]. The different attitudes of farmers,
hospitality providers, and tourism stakeholders toward the intersectoral linkages in tourism
may result from varying interests, priorities, understandings of cross-sector collaboration, and
specific challenges faced by each of these industries [
18
,
19
,
42
,
44
,
52
]. The main factors explain-
ing these differences in attitudes include different economic goals and interests, disagreements
over the perceived benefits of intersectoral linkages, operational and infrastructural barriers,
differences in cultural and organizational characteristics, lack of trust and experience in col-
laboration, and misaligned regulatory frameworks and policies [
5
,
42
]. In order to achieve
successful intersectoral linkages, it is necessary to overcome these obstacles through the
development of common interests, policy alignment, and the creation of infrastructure that
supports collaboration between sectors while harmonizing the perspectives of all relevant
actors in the chain [26,54].

Agriculture 2025,15, 604 5 of 22
Considering the findings of the available studies, the third research question (Q3) is
posed: are there differences in the attitudes of farmers, hospitality providers, and tourism
stakeholders toward the key indicators for establishing intersectoral linkages in the region?
2.4. Subjects Involved in Intersectoral Linkages in AP Vojvodina
In the process of establishing intersectoral linkages between agriculture, hospitality,
and tourism in Vojvodina, the key subjects are farmers, hospitality providers, represen-
tatives of the tourism sector, local and national authorities, and tourists [
31
,
35
]. Food
producers play a central role as they provide the essential resources for the hospitality and
tourism sector, while service providers enable tourists to experience local gastronomy and
tradition [
18
,
19
]. Tourism agencies and organizations create and promote offers that include
visits to agricultural estates, participation in rural tourism activities, and tastings of local
products [11,14,15]. The national government, in cooperation with local authorities, plays
a crucial role in creating and implementing policies that support intersectoral linkages,
through subsidies, promotions, and infrastructure development [
23
,
44
]. Tourists, through
their interest in and demand for authentic experiences, shape the dynamics of these link-
ages [
11
,
21
]. All these actors must work in synergy, recognizing the mutual benefits and
challenges, to achieve long-term success through sustainable regional development [50].
3. Materials and Methods
3.1. The Researched Area
The research location is the Autonomous Province of Vojvodina, which is located in
the northern part of the Republic of Serbia (Figure 1). It is home to the largest number
of agricultural holdings in the country, with 157,103 recorded in the agricultural census.
Family farms dominate, numbering 156,138, with an average value of EUR 8953 [
55
]. In
Serbia, 512 agricultural holdings are involved in tourism, 107 of which are located in
Vojvodina. One of the limiting factors in the available studies is that agricultural holdings
are considered within the framework of agritourism, and their products’ placement in
the hospitality–tourism market is often neglected [
31
,
35
,
55
]. A key problem for their
involvement in hospitality–tourism is the lack of workforce needed to create tourism
products [
41
,
56
,
57
]. Looking at the statistical results for tourism development, it is observed
that the region has recorded a growing number of domestic and foreign tourists year after
year [
58
]. AP Vojvodina has strategic importance for the development of tourism in Serbia
and the region, as its natural beauty, cultural heritage, and intersectoral linkages can
contribute to strengthening the competitiveness and sustainability of tourism offerings.
By investing in infrastructure, digitalization of tourism services, and promotion of local
products, the region can further solidify its position as a leader in the development of
sustainable forms of tourism in southeastern Europe [
51
]. Previous studies related to food
offerings of hospitality establishments in Vojvodina have highlighted that the offerings
are neither sufficiently local nor authentic [
40
,
59
,
60
]. Tourists who stayed in Novi Sad and
Belgrade recognized the scarce offerings and considered the food to be non-authentic. This
negatively impacted the attractiveness of the destination, as the rating of the food culture is
an important factor of attractiveness [61].

Agriculture 2025,15, 604 6 of 22
Agriculture 2025, 15, x FOR PEER REVIEW 6 of 22
Figure 1. Location of Vojvodina Province (Republic of Serbia) [39].
3.2. Research Model
The Sustainable Intersectoral Linking Model in Agriculture, Hospitality, and Tour-
ism (SILM-AHT) was developed through four phases: (1) detailed literature review; (2)
workshop using the Delphi method; (3) pilot testing; and (4) model development and
validation, as described below.
The first phase involved a detailed literature review using the SCOPUS database to
identify relevant indicators from the fields of tourism, hospitality, and agriculture, lead-
ing to the formation of a list of 75 indicators for the survey questionnaire.
The second phase included a workshop using the Delphi method. This phase
achieved consensus on the most relevant indicators for identifying intersectoral connec-
tions between agriculture, hospitality, and tourism in Vojvodina [62]. The primary goal
of this method is to reach agreement rather than compromise. There is no strict order in
the Delphi procedure. Typically, the research scope and topic are defined first, followed
by the selection of experts who respond to questionnaires over three to four rounds [63].
Accordingly, scientists, government officials, and business professionals from the
public and private sectors in the fields of tourism, hospitality, and agriculture were in-
vited to participate in this study to achieve strategic consensus. The basic contact list was
derived from databases, such as the KFS (Culinary Federation of Serbia), the Tourism
Organization of Vojvodina (TOS), the Agricultural Association of the Vojvodina Cham-
ber of Commerce, and personal contacts of researchers and professors specializing in
gastronomy, tourism, and agricultural management, following guidelines for sample size
(at least 10 to 15 individuals) [64]. Given the multidisciplinary nature of this study, 70
experts were invited, 56 of whom participated in the first round of the survey. In the
subsequent two rounds, 45 participants took part in this study. The average years of
work experience of the participants in their sector in the first round was 26.6 years, in the
second round 22.12 years, and in the third round 18.25 years.
The first round of the Delphi method aimed at identifying stakeholders interested in
participating in the research and completing the survey questionnaire. They were asked
to assess how relevant each of the 75 indicators was for measuring or establishing inter-
Figure 1. Location of Vojvodina Province (Republic of Serbia) [39].
3.2. Research Model
The Sustainable Intersectoral Linking Model in Agriculture, Hospitality, and Tourism
(SILM-AHT) was developed through four phases: (1) detailed literature review; (2) work-
shop using the Delphi method; (3) pilot testing; and (4) model development and validation,
as described below.
The first phase involved a detailed literature review using the SCOPUS database to
identify relevant indicators from the fields of tourism, hospitality, and agriculture, leading
to the formation of a list of 75 indicators for the survey questionnaire.
The second phase included a workshop using the Delphi method. This phase achieved
consensus on the most relevant indicators for identifying intersectoral connections between
agriculture, hospitality, and tourism in Vojvodina [
62
]. The primary goal of this method is
to reach agreement rather than compromise. There is no strict order in the Delphi procedure.
Typically, the research scope and topic are defined first, followed by the selection of experts
who respond to questionnaires over three to four rounds [63].
Accordingly, scientists, government officials, and business professionals from the
public and private sectors in the fields of tourism, hospitality, and agriculture were invited
to participate in this study to achieve strategic consensus. The basic contact list was
derived from databases, such as the KFS (Culinary Federation of Serbia), the Tourism
Organization of Vojvodina (TOS), the Agricultural Association of the Vojvodina Chamber of
Commerce, and personal contacts of researchers and professors specializing in gastronomy,
tourism, and agricultural management, following guidelines for sample size (at least
10 to 15 individuals
) [
64
]. Given the multidisciplinary nature of this study, 70 experts
were invited, 56 of whom participated in the first round of the survey. In the subsequent
two rounds
, 45 participants took part in this study. The average years of work experience
of the participants in their sector in the first round was 26.6 years, in the second round
22.12 years, and in the third round 18.25 years.
The first round of the Delphi method aimed at identifying stakeholders interested in
participating in the research and completing the survey questionnaire. They were asked to

Agriculture 2025,15, 604 7 of 22
assess how relevant each of the 75 indicators was for measuring or establishing intersectoral
connections in Vojvodina. A five-point Likert scale (1—not relevant at all; 5—very relevant)
was used for this purpose. A total of 56 responses were collected, which were analyzed
using mean values and standard deviations. Indicators with a mean value of less than
4 and
a high standard deviation were eliminated from this study. After this step, a list of
61 indicators was formed for the second round.
The second and third rounds of the Delphi method were conducted during a workshop
held in Novi Sad in February 2024. During the workshop, a discussion about the indicators
took place, and participants completed the survey questionnaire twice. The analysis and
elimination procedure for the indicators followed the same method as in the first round.
At the end of the workshop, a total of 39 indicators were identified as relevant for
establishing intersectoral connections in Vojvodina.
The third phase involved pilot testing, conducted in March and April 2024 via email.
Representatives from the hospitality, agriculture, and tourism sectors were randomly
selected to participate. The aim of the pilot study was to eliminate any potential issues or
misunderstandings that might arise in the final research. During this phase, participants
had the opportunity to mark a statement with the number 6 if they believed the statement
was not well formulated.
Pilot testing was conducted on a sample of 60 participants, as Connelly (2008) [
65
]
recommended that the sample size of a pilot study should be at least 10%. Finally, it is
important to note that participants in this study found all the created variables to be clear
and understandable.
The fourth phase involved the development and validation of the model, which was
carried out using statistical methods. Data processing was performed using the SPSS
software program (24.0 for Windows). Data entry into the created matrix was systematic,
followed by testing for validity. An analysis of missing data was conducted using the
Missing Value Analysis module. Little’s test was applied to assess the complete randomness
of the distribution of missing values [66,67].
In order to determine the factor structure of the questionnaire, exploratory factor
analysis (EFA) was applied (method of principal axes). To determine the number of factors,
parallel analysis was used [
68
]. Isolated factors were subjected to oblique promax rotation
and interpreted based on the factor structure matrix. Additionally, an item analysis was
performed, and items with low discriminability (<0.30) were eliminated, as their removal
would contribute to an increase in alpha. The homogeneity index (MIC) was calculated
as the average inter-item correlation within the factor, with values expected to range from
0.20 to 0.50. The confirmation of the factor analysis was conducted through confirmatory
factor analysis (CFA).
For model fit evaluation, the following fit indices were used: chi-square (
χ2
), CFI
(Comparative Fit Index), TLI (Tucker–Lewis Index), RMSEA (Root Mean Square Error of
Approximation), and SRMR (Standardized Root Mean Residual). For model validation,
the sample was split into two parts. Sample 1 (N = 296) was used for EFA, while Sample
2 (N = 294) was used for CFA. The sample size was in accordance with Pallant’s [
69
]
recommendations, which suggested that an acceptable sample size should range from
150 to 300, while MacCallum et al. [
70
] stated that a sample size of 100 to 200 observations
is sufficient for conducting descriptive and factor analysis. The participants and sample
sizes are presented in Table 1.

Agriculture 2025,15, 604 8 of 22
Table 1. Participants in the survey research.
Sample 1
Description of the Sample Agricultural Sector
n = 94
Hospitality Sector
n = 101
Tourism Sector
n = 101
Age
min-max (years old) 21–82 18–72 18–71
AS (SD) 50.12 (13.05) 34.45 (11.55) 37.22 (10.30)
Gender
men 67 (71.3%) 67 (66.3%) 55 (54.5%)
women 27 (28.7%) 34 (33.7%) 46 (45.5%)
Highest level of completed
education
elementary school 15 (16.0%) 0 (0.0%) 0 (0.0%
high school 60 (63.8%) 49 (48.5%) 28 (27.7%)
college 6 (6.4%) 30 (29.7%) 28 (27.7%)
university 13 (13.8%) 22 (21.8%) 45 (44.6%)
Please indicate how many
years you have been in
agriculture, hospitality, and
tourism (years)
1–65 1–40 1–39
AS (SD) 21.98 (14.26) 11.69 (8.19) 18.90 (10.60)
Sample 2
Description of the Sample Agricultural Sector
n = 94
Hospitality Sector
n = 100
Tourism Sector
n = 100
Age
min-max (years old) 20–84 17–73 17–73
AS (SD) 49.53 (13.21) 33.96 (11.70) 36.77 (10.45)
Gender
men 68 (72.3%) 66 (66.0%) 53 (53.0%)
women 26 (27.7%) 34 (34.0%) 47 (47.0%)
Highest level of completed
education
elementary school 15 (16.0%) 0 (0.0%) 0 (0.0%)
high school 61 (64.9%) 49 (49.0%) 28 (28.0%)
college 6 (6.4%) 30 (30.0%) 28 (28.0%)
university 12 (12.7%) 21 (21.0%) 44 (44.0%)
Please indicate how many
years you have been in
agriculture, hospitality, and
tourism (years)
1–65 1–40 1–39
AS (SD) 21.98 (14.26) 11.69 (8.19) 18.90 (10.60)
It is important to note that descriptive statistics were used to present the sample. Dif-
ferences in participants’ perceptions regarding grouped factors were tested using analysis
of variance.
3.3. Research Procedure and Instrument
The survey research was conducted from June to October 2024. The questionnaire
consisted of 39 variables closely related to establishing intersectoral linking in the Vojvodina
region. Respondents indicated their level of agreement on a 5-point Likert scale (1—strongly
disagree; 5—strongly agree).

Agriculture 2025,15, 604 9 of 22
3.4. Participants in the Research
In the final phase of the research, 580 participants took part. The primary contact list
consisted of databases of business entities that were invited to participate in the Delphi
method. The profile of the participants is shown in Table 1.
4. Results
4.1. Exploratory Factor Analysis
Based on the parallel analysis, it was possible to extract five factors that explained
50.33% of the total variance, or 42.02% of the common variance (Table 2).
Table 2. Eigenvalues and percentages of explained variance in extracted factors.
No. of
Factors
Initial Values After Extraction
λAfter
Rotation
Parallel
Analysis Λ% of
Variance Cumulative % % Variances
Cumulative
%
1 1.62 11.815 31.025 31.025 28.732 28.732 10.102
2 1.53 2.745 7.214 38.239 5.689 34.421 6.725
3 1.47 1.682 4.375 42.614 2.845 37.266 6.785
4 1.42 1.542 3.995 46.609 2.562 39.828 5.821
5 1.38 1.421 3.725 50.334 2.194 42.022 1.604
6 1.35 1.262 3.330 53.664
Two items did not achieve significant loadings on any factor (there is a need for
infrastructure projects that support sustainable tourism, such as eco-friendly hotels and
restaurants using local products; and there are adequate legislative frameworks supporting
intersectoral cooperation in tourism). Four items had significant secondary loadings, so
they were included in the factor where they had the primary loading. The exception was
the item there is a need for infrastructure projects that would enable the promotion and
distribution of local products as tourist attractions, which has relatively equal loadings on
the Education and Infrastructure factors. Due to the content and theoretical expectations
about the distinction of the Infrastructure factor, it was retained within it. The extracted
factors were then subjected to a promax rotation and interpreted based on the factor
structure matrix.
The first factor includes 17 items (Table 3) and explains 28.7% of the common variance.
The factor structure indicates that it is named Sustainability. It encompasses the assessment
of establishing intersectoral linkages through sustainability principles.
Table 3. Factor pattern matrix—Sustainability.
Items Loadings
Intersectoral collaboration contributes to increasing revenue from tourism, agriculture, and hospitality. 0.77
Investing in intersectoral cooperation brings economic benefits to the local community. 0.90
Intersectoral collaboration can contribute to improving the standard of living of local communities. 0.46
Tourism and agriculture together contribute to employment in rural areas. 0.74
Sustainable intersectoral cooperation can help preserve traditional skills and cultural values. 0.52
Intersectoral collaboration between tourism, agriculture, and hospitality helps in
preserving natural resources. 0.38
Preserving local customs and traditions should be a priority in tourism development. 0.39
Through intersectoral cooperation, the authenticity of the destination can be preserved. 0.41
Improving infrastructure in tourism and agriculture positively affects intersectoral collaboration. 0.55
Technological innovations can contribute to the sustainable development of tourism in the region. 0.63
Well-established communication between sectors is key to successful intersectoral cooperation. 0.79
Collaboration between farmers, hoteliers, and tourism stakeholders reduces operating costs in all sectors.
0.68

Agriculture 2025,15, 604 10 of 22
Table 3. Cont.
Items Loadings
Investing in transportation and communication networks is essential for the development of
intersectoral cooperation. 0.73
Transparency in operations can improve collaboration among different sectors. 0.67
Lack of flexibility in business models hampers intersectoral cooperation. 0.63
There is a lack of trust between farmers, hoteliers, and tourism stakeholders. 0.55
The use of environmentally friendly methods in production is a necessity in tourism development. 0.57
The second factor includes nine items (Table 4) and explains 5.68% of the common
variance. The factor structure indicates that it is named Education.
Table 4. Factor loading matrix for Education.
Items Loadings
There is a need for organizing educational programs that connect farmers, hoteliers, and
tourism stakeholders. 0.38
There is insufficient education on how intersectoral collaboration can contribute to sustainable tourism
development in our region. 0.56
There is a need for organizing educational programs that connect farmers, hoteliers, and
tourism stakeholders. 0.35
Education on sustainable practices in tourism should be part of training for all sector actors. 0.80
Farmers and hoteliers should be introduced to new technologies and methods that support the
sustainability of tourism. 0.49
Farmers, hoteliers, and tourism stakeholders should participate in joint workshops and seminars that
support intersectoral cooperation. 0.35
Exchange of experiences and best practices among stakeholders from different sectors can improve
intersectoral collaboration. 0.46
Tourism workers need additional training to learn how to communicate better with
farmers and hoteliers. 0.48
Introducing joint educational sessions can contribute to better collaboration between sectors. 0.36
The factor structure matrix of the third factor includes four items (Table 5) and ex-
plains 2.84% of the common variance. The factor structure indicates that it is named
Government Policy.
Table 5. Factor loading matrix of Government Policy.
Items Loadings
Government policies should encourage greater private sector involvement in the development of
tourism, agriculture, and hospitality. 0.38
The government should provide more support to local communities in developing intersectoral
collaboration that includes agriculture, hospitality, and tourism. 0.50
The government should develop specific policies that encourage intersectoral collaboration between
agriculture, hospitality, and tourism. 0.79
Government subsidies and financial incentives should be available to farmers, hoteliers, and tourism
stakeholders to encourage them to engage in intersectoral collaboration. 0.78
The factor structure matrix of the fourth factor includes five items (Table 6) and
explains 2.56% of the common variance. The factor structure indicates that it is named
Contribution of Farmers and Hospitality Providers.

Agriculture 2025,15, 604 11 of 22
Table 6. Factor loading matrix—Contribution of Farmers and Hospitality Providers.
Items Loadings
There is a disagreement between farmers and hoteliers regarding the prices and quality of products
used in tourism. 0.62
Hoteliers believe that intersectoral linking can improve their business model and attract more tourists. 0.51
Hoteliers expect greater consistency in product quality from farmers to enhance the tourism offering. 0.68
There is a disagreement between farmers and hoteliers regarding the constant availability of products on
the hospitality and tourism market. 0.64
Farmers and hoteliers recognize the importance of intersectoral collaboration for the development of
sustainable tourism. 0.39
The factor structure matrix of the fifth factor includes two items (Table 7) and explains
2.19% of the common variance. The factor structure indicates that it is named Infrastructure.
Table 7. Factor loading matrix—Infrastructure.
Items Loadings
Infrastructure for the direct sale of local products is not sufficiently developed to support tourism. 0.50
There is a need for infrastructure projects that would enable the promotion and distribution of local
products as tourist attractions. 0.38
The reliability of the retained factors is acceptable to excellent (Table 8). The Sustain-
ability factor has the highest reliability. Considering that the Infrastructure factor contains
only two items, its reliability is acceptable. The average inter-item correlations (MICs) are
within the recommended range, so it can be concluded that there are no redundant items.
The presented results confirm that the scales can be used in further analyses. Five factors
are identified: Sustainability, Education, Government Policy, Contribution of Hospitality
Providers and Farmers, and Infrastructure.
Table 8. Descriptive data, reliability, and average inter-item correlation.
Scales of the Questionnaire AS SD αMIC
Sustainability 4.09 0.62 0.92 0.41
Education 3.56 0.69 0.79 0.32
Government Policy 3.71 0.75 0.74 0.42
Contribution of Hospitality and Agriculture 3.48 0.72 0.72 0.34
Infrastructure 3.26 1.00 0.61 0.43
4.2. Analysis and Validation of the Model—Confirmatory Factor Analysis
Model Fit Indices
A confirmatory factor analysis (CFA) was conducted to test the factor structure of the
model and confirm its validity. The results of the fit indices are presented in Table 9.
The results of the CFA show that the model has a good fit to the data, as all the fit
indices are within the recommended limits. The values of the CFI (0.93) and TLI (0.91)
confirm that the model has a high degree of alignment with the data [
71
], while the RMSEA
(0.05) and SRMR (0.06) suggest that the residual errors are minimal [
72
]. Since the chi-
square test is not significant (p= 0.06), it can be concluded that there are no significant
deviations between the model and the data, indicating its validity and stability [73].
Table 10 shows the factor loadings of the CFA model.

Agriculture 2025,15, 604 12 of 22
Table 9. CFA model fit indices.
Fit Index Value Recommended Range Interpretation
Chi-square (χ2,p-value) 312.45 (p= 0.06) p> 0.05 (ideal) The model fits the data well.
CFI (Comparative Fit Index) 0.93 ≥0.90 (good), ≥0.95 (excellent) The model shows a high degree
of alignment.
TLI (Tucker–Lewis Index) 0.91 ≥0.90 (good), ≥0.95 (excellent)
The model has an adequate structure.
RMSEA (Root Mean Square
Error of Approximation) 0.05 ≤0.08 (good), ≤0.05 (excellent) The model shows minimal error.
SRMR (Standardized Root
Mean Residual) 0.06 ≤0.08 (good)
The model is consistent with the data.
Table 10. The factor loadings of the CFA model.
Item
Sustainability
Education Government Policy
Contribution
Infrastructure
s1 0.79 - - - -
s2 0.77 - - - -
s3 0.74 - - - -
s4 0.71 - - - -
s5 0.78 - - - -
s6 0.76 - - - -
s7 0.80 - - - -
s8 0.74 - - - -
s9 0.73 - - - -
s10 0.79 - - - -
s11 0.81 - - - -
s12 0.77 - - - -
s13 0.76 - - - -
s14 0.78 - - - -
s15 0.75 - - - -
s16 0.80 - - - -
s17 0.77 - - - -
s18 - 0.82 - - -
s19 - 0.79 - - -
s20 - 0.76 - - -
s21 - 0.75 - - -
s22 - 0.78 - - -
s23 - 0.79 - - -
s24 - 0.81 - - -
s25 - 0.77 - - -
s26 - 0.75 - - -
s27 - - 0.70 - -
s28 - - 0.72 - -
s29 - - 0.75 - -
s30 - - 0.76 - -
s31 - - - 0.74 -
s32 - - - 0.78 -
s33 - - - 0.80 -
s34 - - - 0.77 -
s35 - - - 0.75 -
s36 - - - - 0.71
s37 - - - - 0.69
All the factor loadings are above the recommended threshold of 0.70, which indicates
the relationship between the items and the latent constructs is good. This confirms the
convergent validity of the model, as each item reliably measures the construct to which it
belongs [
74
]. The lowest loading (S37 = 0.69) is on the borderline of the acceptable level,
but it does not require the elimination of the item.
In Table 11, the correlations between the factors are presented.

Agriculture 2025,15, 604 13 of 22
Table 11. Correlations between the factors.
Factor Sustainability Education Government Policy Contribution Infrastructure
Sustainability 1.00 0.58 0.50 0.53 0.47
Education 0.58 1.00 0.54 0.48 0.45
Government Policy 0.50 0.54 1.00 0.49 0.46
Contribution of Hospitality
and Agriculture 0.53 0.48 0.49 1.00 0.50
Infrastructure 0.47 0.45 0.46 0.50 1.00
All the factors are moderately correlated with each other, confirming the discriminant
validity of the model. There are no high correlations (>0.80), which means that each factor
measures a different aspect of intersectoral cooperation [74].
The CFA results confirm that the model has good structural stability, high reliability,
and adequate validity. All the fit indices are within the recommended ranges and the factor
loadings show a strong connection between the items and the factors, while the correla-
tions between the factors indicate a clear distinction between the constructs. The results,
based on the exploratory factor analysis (EFA) and confirmed by the confirmatory factor
analysis (CFA), point to a clearly defined model structure that describes intersectoral coop-
eration in tourism, agriculture, and hospitality. The five identified
factors—Sustainability
,
Education, Government Policy, Contribution of Farmers and Hospitality Providers, and
Infrastructure—demonstrate
satisfactory reliability and validity. The Sustainability factor
plays a dominant role in the model. The factor structure shows that the items within this
factor are related to intersectoral cooperation through the economic, ecological, and cultural
dimensions of sustainability. The factor loadings in the CFA model (above 0.70) confirm
its convergent validity, while its moderate correlations with other factors suggest its com-
plementary role within the model. The Education factor points to the recognized need for
training and raising awareness about the importance of intersectoral cooperation. The EFA
shows that the items related to organizing educational programs and knowledge exchange
are highly loaded on this factor, which is further confirmed by the CFA analysis (factor
loadings above 0.75). The moderate connection with the Sustainability factor (
r = 0.58
)
suggests that education is a key element in implementing sustainable intersectoral initia-
tives. The Government Policy factor reflects the regulatory and institutional framework for
intersectoral cooperation. The factor analysis confirms that the items related to government
incentives and support for local communities have significant factor loadings. The CFA
analysis further confirms the structural stability of the factor, with its correlations with other
factors being moderate (most notably with Education,
r = 0.54
), indicating the synergistic
effect of regulatory support and educational initiatives. The fourth factor, Contribution of
Farmers and Hospitality Providers, encompasses attitudes about the roles of these actors
in intersectoral cooperation. The items with the highest loadings relate to challenges re-
garding the quality, availability, and price of local products. The factor structure shows
that this factor is clearly separated and is confirmed by the CFA analysis (factor loadings
from 0.74 to 0.80). Its connection with the Sustainability factor (r = 0.53) suggests that
the active involvement of farmers and hospitality providers is crucial for achieving the
long-term sustainability of tourism initiatives. The final factor, Infrastructure, includes
items indicating the need for improving logistics and the physical connections between
sectors. The EFA shows that two items with similar meanings are extracted as a separate
dimension, while the CFA analysis confirms that their loadings are satisfactory (above 0.69).
The correlation with other factors is moderate but significant, indicating that infrastructure
is recognized as a foundation for the successful implementation of intersectoral cooperation.
These analyses highlight the main factors to consider when identifying the intersectoral
linkages between agriculture, hospitality, and tourism in AP Vojvodina.

Agriculture 2025,15, 604 14 of 22
4.3. Analysis of Respondents’ Attitude Heterogeneity Toward Intersectoral Connection
Establishing intersectoral connections between agriculture, hospitality, and tourism
will not be easy because the subjects have heterogeneous attitudes toward and different
interests for achieving it. The differences in perception in this study were examined through
the identified factors. The variance analysis revealed significant differences between
farmers, hoteliers, and tourism actors regarding the identified factors (Table 12).
Table 12. Differences in dimensions between farmers, hospitality service providers, and tourism actors.
Dimensions F (2,586) pη2
Sustainability 18.68 0.000 0.06
Education 34.46 0.000 0.11
Government Policy 8.00 0.000 0.03
Contribution of Hospitality and Agriculture 17.27 0.000 0.06
Infrastructure 4.51 0.011 0.02
Based on a post hoc Bonferroni test, results were obtained that answer the third
research question (Table 13, Scheme 1). It can be concluded that tourism planners scored
higher on the dimensions of Sustainability and the Contribution of Hospitality Providers
and Farmers compared to farmers and hoteliers, as well as higher on the Infrastructure
factor compared to hoteliers. Hoteliers scored lower on the dimensions of Education and
Government Policies compared to farmers and tourism stakeholders.
Table 13. Post hoc Bonferroni test for testing the significance of differences between farmers, hospital-
ity providers, and tourism actors regarding intersectoral linkage.
Dependent Variable (I) Group (J) Group Mean
Difference (I-J) Std. Error Sig.
Sustainability
1.00 farmers 2.00 hospitality providers 0.13073 0.06086 0.096
3.00 tourism stakeholders −0.23011 0.06086 0.001
2.00 hospitality providers 1.00 farmers −0.13073 0.06086 0.096
3.00 tourism stakeholders −0.36084 0.05975 0.000
3.00 tourism stakeholders 1.00 farmers 0.23011 0.06086 0.001
2.00 hospitality providers 0.36084 0.05975 0.000
Education
1.00 farmers 2.00 hospitality providers 0.41511 0.06620 0.000
3.00 tourism stakeholders −0.09298 0.06620 0.482
2.00 hospitality providers 1.00 farmers −0.41511 0.06620 0.000
3.00 tourism stakeholders −0.50808 0.06499 0.000
3.00 tourism stakeholders 1.00 farmers 0.09298 0.06620 0.482
2.00 hospitality providers 0.50808 0.06499 0.000
Government Policy
1.00 farmers 2.00 hospitality providers 0.19108 0.07498 0.033
3.00 tourism stakeholders −0.09872 0.07498 0.565
2.00 hospitality providers 1.00 farmers −0.19108 0.07498 0.033
3.00 tourism stakeholders −0.28980 0.07362 0.000
3.00 tourism stakeholders 1.00 farmers 0.09872 0.07498 0.565
2.00 hospitality providers 0.28980 0.07362 0.000
Contribution of Hospitality
and Agriculture
1.00 farmers 2.00 hospitality providers 0.14129 0.07082 0.140
3.00 tourism stakeholders −0.26170 0.07082 0.001
2.00 hospitality providers 1.00 farmers −0.14129 0.07082 0.140
3.00 tourism stakeholders −0.40299 0.06953 0.000
3.00 tourism stakeholders 1.00 farmers 0.26170 0.07082 0.001
2.00 hospitality providers 0.40299 0.06953 0.000
Infrastructure
1.00 farmers 2.00 hospitality providers 0.15258 0.10128 0.397
3.00 tourism stakeholders −0.14593 0.10128 0.450
2.00 hospitality providers 1.00 farmers −0.15258 0.10128 0.397
3.00 tourism stakeholders −0.29851 0.09944 0.008
3.00 tourism stakeholders 1.00 farmers 0.14593 0.10128 0.450
2.00 hospitality providers 0.29851 0.09944 0.008

Agriculture 2025,15, 604 15 of 22
Agriculture 2025, 15, x FOR PEER REVIEW 15 of 22
3.00 tourism stakeholders −0.29851 0.09944 0.008
3.00 tourism stakeholders 1.00 farmers 0.14593 0.10128 0.450
2.00 hospitality providers 0.29851 0.09944 0.008
Scheme 1. Expressed heterogeneity of views of farmers, hospitality providers, and tourism actors.
5. Discussion
This study clearly outlines the process for creating a model for the intersectoral
linking of agriculture, hospitality, and tourism, tailored to the specific characteristics of
microregions. The Delphi method was crucial for achieving consensus among all the
participants in this study regarding the relevant indicators that should be included in the
research. In addition to quantitative analyses, the qualitative data obtained through
stakeholder discussions contributed to beer clarity and a more precise definition of
certain indicators. The pilot testing identified and addressed all shortcomings and mis-
understandings, resulting in a well-balanced model that was approved by experts. Af-
terward, the model was tested and proved to be a valid and reliable tool.
A detailed review of the literature led to the answer to the first research question,
Q1. It was established that the issue of intersectoral linkages in tourism is complex, and
that various methodological approaches are the key to understanding it. All the analyzed
models were adapted to specific regional conditions, including socio-economic and geo-
graphical specifics.
In regions where the state has not provided long-term and planned support for in-
tersectoral linking with tourism, the developed models were based on identifying the key
factors through the aitudes of stakeholders from the different sectors involved in the
chain [48–50,75]. On the other hand, the effects of long-term investments with planned
and continuous monitoring were highlighted in the context of the CBT model. A 2014
evaluation showed that tourism had become a key development factor, with a revenue of
USD 47 billion and leading to a 10% reduction in poverty [43,44].
The development of a multidisciplinary sustainable model for AP Vojvodina re-
quires institutional support, agricultural production diversification, stakeholder educa-
tion, and the creation of a favorable business environment [41]. In line with the current
limitations, the most acceptable approach to model development is based on the identi-
fication of the factors for establishing intersectoral linkages between agriculture, hospi-
tality, and tourism.
The second research question (Q2) aimed to identify the main findings of the pro-
posed model. The Sustainable Intersectoral Linking Model in Agriculture, Hospitality,
Scheme 1. Expressed heterogeneity of views of farmers, hospitality providers, and tourism actors.
5. Discussion
This study clearly outlines the process for creating a model for the intersectoral linking
of agriculture, hospitality, and tourism, tailored to the specific characteristics of microre-
gions. The Delphi method was crucial for achieving consensus among all the participants
in this study regarding the relevant indicators that should be included in the research. In
addition to quantitative analyses, the qualitative data obtained through stakeholder discus-
sions contributed to better clarity and a more precise definition of certain indicators. The
pilot testing identified and addressed all shortcomings and misunderstandings, resulting
in a well-balanced model that was approved by experts. Afterward, the model was tested
and proved to be a valid and reliable tool.
A detailed review of the literature led to the answer to the first research question,
Q1. It was established that the issue of intersectoral linkages in tourism is complex,
and that various methodological approaches are the key to understanding it. All the
analyzed models were adapted to specific regional conditions, including socio-economic
and geographical specifics.
In regions where the state has not provided long-term and planned support for
intersectoral linking with tourism, the developed models were based on identifying the
key factors through the attitudes of stakeholders from the different sectors involved in the
chain [
48
–
50
,
75
]. On the other hand, the effects of long-term investments with planned
and continuous monitoring were highlighted in the context of the CBT model. A 2014
evaluation showed that tourism had become a key development factor, with a revenue of
USD 47 billion and leading to a 10% reduction in poverty [43,44].
The development of a multidisciplinary sustainable model for AP Vojvodina requires
institutional support, agricultural production diversification, stakeholder education, and
the creation of a favorable business environment [
41
]. In line with the current limitations,
the most acceptable approach to model development is based on the identification of the
factors for establishing intersectoral linkages between agriculture, hospitality, and tourism.
The second research question (Q2) aimed to identify the main findings of the pro-
posed model. The Sustainable Intersectoral Linking Model in Agriculture, Hospitality, and
Tourism (SILM-AHT) was created. It consists of 37 indicators, which are grouped into
five key factors: Sustainability, Education, Government Policy, Contribution of Hospitality
Providers and Farmers, and Infrastructure. These factors, along with their variables, are
aligned with the existing models that focus on identifying the factors for establishing inter-
sectoral connections [
48
–
50
,
75
]. The Sustainability factor indicates the economic, ecological,

Agriculture 2025,15, 604 16 of 22
and social sustainability of intersectoral links. Indicators, such as “Investing in intersectoral
cooperation brings economic benefits to the local community” and “Technological innova-
tions can contribute to the sustainable development of tourism in the region”, demonstrate
how the linking of agriculture, hospitality, and tourism can contribute to the long-term
sustainability of a region (Anderson et al., 2018) [
24
]. The Education factor emphasizes
the need for educating all actors in a system. Indicators, such as “There is insufficient
education on how intersectoral cooperation can contribute to the sustainable development
of tourism”, point to barriers in understanding the benefits of cooperation. In Vojvodina,
there is currently no systematic training program linking farmers and hoteliers, which
hinders the adoption of new technologies and sustainability practices. The Government
Policy factor represents a key element in regulating and supporting intersectoral links.
The indicators suggest that subsidies and financial incentives are necessary to strengthen
cooperation between the sectors, and that such support is currently significantly lacking.
The Contribution of Food Producers and Hoteliers factor sheds light on the challenges to
intersectoral coordination, especially regarding the availability and quality of products. In-
dicators, such as “There is a disagreement between food producers and hoteliers regarding
the constant availability of products”, suggest the need for more stable supply channels
and clearer quality standards. For example, seasonal variations in product availability often
make it difficult for hoteliers to maintain a consistent menu based on local ingredients.
Infrastructure is highlighted as a critical factor. A key indicator is that infrastructure for
the direct sale of local products is not sufficiently developed. Djuri´c (2018) emphasized
that infrastructure for the direct sale of local products was not well developed enough to
support tourism [
76
]. This is further supported by the fact that family farms in Vojvodina
have an average value of EUR 8953 [77]. Figure 2presents the research results.
Agriculture 2025, 15, x FOR PEER REVIEW 16 of 22
and Tourism (SILM-AHT) was created. It consists of 37 indicators, which are grouped
into five key factors: Sustainability, Education, Government Policy, Contribution of
Hospitality Providers and Farmers, and Infrastructure. These factors, along with their
variables, are aligned with the existing models that focus on identifying the factors for
establishing intersectoral connections [48–50,75]. The Sustainability factor indicates the
economic, ecological, and social sustainability of intersectoral links. Indicators, such as
“Investing in intersectoral cooperation brings economic benefits to the local community”
and “Technological innovations can contribute to the sustainable development of tour-
ism in the region”, demonstrate how the linking of agriculture, hospitality, and tourism
can contribute to the long-term sustainability of a region (Anderson et al., 2018) [24]. The
Education factor emphasizes the need for educating all actors in a system. Indicators,
such as “There is insufficient education on how intersectoral cooperation can contribute
to the sustainable development of tourism”, point to barriers in understanding the ben-
efits of cooperation. In Vojvodina, there is currently no systematic training program
linking farmers and hoteliers, which hinders the adoption of new technologies and sus-
tainability practices. The Government Policy factor represents a key element in regulat-
ing and supporting intersectoral links. The indicators suggest that subsidies and financial
incentives are necessary to strengthen cooperation between the sectors, and that such
support is currently significantly lacking. The Contribution of Food Producers and Ho-
teliers factor sheds light on the challenges to intersectoral coordination, especially re-
garding the availability and quality of products. Indicators, such as “There is a disa-
greement between food producers and hoteliers regarding the constant availability of
products”, suggest the need for more stable supply channels and clearer quality stand-
ards. For example, seasonal variations in product availability often make it difficult for
hoteliers to maintain a consistent menu based on local ingredients. Infrastructure is
highlighted as a critical factor. A key indicator is that infrastructure for the direct sale of
local products is not sufficiently developed. Djurić (2018) emphasized that infrastructure
for the direct sale of local products was not well developed enough to support tourism
[76]. This is further supported by the fact that family farms in Vojvodina have an average
value of EUR 8953 [77]. Figure 2 presents the research results.
It is important to note that the interaction between the identified factors has a key
impact on intersectoral linkages. Infrastructure, Education, and Government Policies are
interdependent elements that directly affect the efficiency of intersectoral linking. Inad-
equate infrastructure hinders stable supply chains, while disagreements about the qual-
ity and availability of products further complicate cooperation and limits the contribu-
tions of farmers and hoteliers. Infrastructure without education and political support
cannot ensure long-term sustainability. The success of intersectoral cooperation depends
on the synergy of all the aforementioned factors [48–50].
Figure 2. Research results.
It is important to note that the interaction between the identified factors has a key
impact on intersectoral linkages. Infrastructure, Education, and Government Policies are
interdependent elements that directly affect the efficiency of intersectoral linking. Inade-
quate infrastructure hinders stable supply chains, while disagreements about the quality
and availability of products further complicate cooperation and limits the contributions
of farmers and hoteliers. Infrastructure without education and political support cannot
ensure long-term sustainability. The success of intersectoral cooperation depends on the
synergy of all the aforementioned factors [48–50].
The development of the SILM-AHT model is a significant resource for policymakers,
researchers, and other stakeholders who are practically and theoretically working on
establishing connections between agriculture, hospitality, and tourism [
5
,
78
]. This study
also makes a significant contribution to the knowledge of the development of intersectoral

Agriculture 2025,15, 604 17 of 22
linking models that are tailored to the characteristics of microregions, with the goal of
sustainable development in local communities [5,24,26,27,47,71,72,78,79].
The SILM-AHT model can also be used in the formulation of development plans and
for evaluating strategies [
29
,
30
]. With statistical results, the created model can greatly
contribute to the development of sustainable practices [
43
,
44
]. In order for the model to
be successfully implemented, it is necessary to create a more sustainable and coordinated
program of regular activities (from the surveys among the target groups) [
51
]. So far, in
Serbia, no scientific model has been created that has all the prerequisites to be implemented
in practice for the intersectoral linking of agriculture, hospitality, and tourism [80].
The improvement of the SILM-AHT model in future applications should be based on
an integrated approach that includes the following: improving the indicators and factors
of the model, developing digital platforms, strengthening institutional support, creating
innovative educational programs, supporting the development of short supply chains for
local products, introducing pilot projects, and applying a participatory approach [43,75].
The third research question aimed to identify whether there are differences in the
attitudes of farmers, hospitality providers, and tourism actors toward the key indicators
for establishing intersectoral linking (Q3).
The expressed heterogeneity of views was expected and is consistent with all the
available studies [
5
,
26
,
42
]. The higher scores among tourism planners on the dimensions
of Sustainability and the Contribution of Farmers and Hospitality Providers reflect the
fact that they have the broadest perspective in relation to all the subjects in the chain
with regard to establishing intersectoral linking [
5
,
74
]. In order for farmers to have more
positive views, it is necessary for them to be educated and diversify their production [
81
–
83
].
Hospitality providers need to focus on tourists as one of the target groups who will visit
their establishments [
18
,
19
]. In the future, it will be important to aim for reducing the
differences in attitudes among farmers, hospitality providers, and tourism actors [
24
,
35
,
50
].
6. Conclusions
This study contributes to the development of theoretical and practical frameworks for
intersectoral linking between agriculture, hospitality, and tourism through the creation and
validation of the SILM-AHT model. The SILM-AHT model addresses previous methodolog-
ical limitations by incorporating relevant indicators that enable a more realistic assessment
and planning of intersectoral relations.
Using combined methodological approaches, the key factors shaping sustainable
cooperation are identified: Sustainability, Education, Government Policy, Contributions of
Farmers and Hospitality Providers, and Infrastructure. One of the significant findings of
this research is the pronounced heterogeneity of stakeholder attitudes toward establishing
intersectoral connections, highlighting the need for additional education and policy creation
to enhance coordination and understanding between sectors.
The SILM-AHT model represents a valuable resource for making development deci-
sions and improving intersectoral cooperation. Its application could contribute to strength-
ening local economies, increasing tourism attractiveness, and promoting the sustainable
development of microregions.
Theoretical contribution of this paper: The theoretical contribution of this paper lies
in laying the foundation for the practical application of the proposed model, not only in
Serbia but also in similarly developing countries. This study highlights the importance of
models adapted to microregions, with the potential for integration into development plans.
It is expected that in the future, this model will encourage researchers to further use and
improve it.

Agriculture 2025,15, 604 18 of 22
Practical contribution: The practical contribution of this scientific paper lies in the
development of a model and a set of indicators that can serve as a practical tool for
policymakers and actors from different sectors, helping them make decisions based on
precise and validated indicators. The SILM-AHT model could be useful for creating
and evaluating development plans, enabling the accurate monitoring of the effects of
implemented activities and policies. Furthermore, the implementation of this model in
practice could contribute to the diversification of agricultural production by improving the
alignment of supply chains with the needs of the hospitality and tourism sectors.
To successfully apply the model in practice, it is recommended that a more coordinated
program of regular activities be created, including the continuous involvement of all
actors; training and capacity building; pilot project implementation and effect monitoring;
integration with political frameworks; and the advancement of digitization with the aim of
developing online platforms and data-sharing systems, which would enable the real-time
monitoring of intersectoral links.
Limitations of this study: This research did not cover all the possible factors that may
affect the success of intersectoral linking, such as economic changes, changes in legislation,
or unforeseen external factors. Furthermore, this study did not examine the long-term
effects of implementing the model, which would be useful for further understanding its
significance in practice. For this reason, future research that covers the long-term results of
the model’s application would be of great importance.
Recommendations for Future Research
Further research on intersectoral linking through the use of the SILM-AHT model
could proceed in several directions. These are as follows: the further examination and
improvement of the proposed model in the Republic of Serbia and other developing
countries; the individual identification of attitudes toward the proposed model among
farmers, hospitality providers, and tourism actors; the importance of product health safety
through the implementation of the model; and the diversification of agricultural production
in the implementation of the SILM-AHT model.
The research gaps in the context of intersectoral links in Vojvodina are not only related
to theoretical shortcomings but also to practical challenges. To fill these gaps, it will be
important to clarify which key factors are preventing the successful connection of sectors
and which deficiencies in the existing research are hindering the implementation of policies
that could improve these links.
Author Contributions: Conceptualization, M.P., B.K.P. and D.T.; methodology, M.P., B.K.P., D.T. and
V.V.; data curation, V.I. and V.V.; writing—review and editing, M.P., B.K.P., D.T., V.I., V.V., S.G.J., G.V.
and M. ´
C.; visualization, V.I.; supervision, M.P. and B.K.P. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the Ministry of Science, Technological Development and
Innovation of the Republic of Serbia (Grant Nos. 451-03-137/2025-03/200125 and 451-03-136/2025-
03/200125).
Institutional Review Board Statement: Our research involves humans but not as experimental
research but as a part of survey research which is anonymous and does not involve collecting any
personal data of respondents. As such, this kind of research does not require special Ethical committee
approval in Serbia where the research was conducted, as it is in line with the national Law on Personal
Data Protection (The Official Gazzette of the Republic of Serbia, number 97/08; further: The Law).
The national Law on Personal Data Protection is aligned with the current standards of the relevant
European documents, and in particular with the EU General Data Protection Regulation (GDPR). The
Law applies to the processing of personal data in the context of the activities of an establishment of a
controller or a processor in the Republic of Serbia, regardless of whether the processing takes place in
the Republic of Serbia or not.

Agriculture 2025,15, 604 19 of 22
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: The authors gratefully acknowledge the financial support of the Ministry of
Science, Technological Development and Innovation of the Republic of Serbia (Grant Nos. 451-03-
137/2025-03/200125 and 451-03-136/2025-03/200125).
Conflicts of Interest: The authors declare no conflicts of interests.
References
1.
Socci, P.; Errico, A.; Castelli, G.; Penna, D.; Preti, F. Terracing: From Agriculture to Multiple Ecosystem Services. Oxf. Res. Encycl.
Environ. Sci. 2019. [CrossRef]
2.
Santoro, A.; Venturi, M.; Agnoletti, M. Agricultural Heritage Systems and Landscape Perception among Tourists. The Case of
Lamole, Chianti (Italy). Sustainability 2020,12, 3509. [CrossRef]
3.
Garcia, M.A.; Yagüe, J.L.; De Nicolás, V.L.; Díaz-Puente, J.M. Characterization of Globally Important Agricultural Heritage
Systems (GIAHS) in Europe. Sustainability 2020,12, 1611. [CrossRef]
4.
Kumar, S.; Valeri, M.; Shekhar. Understanding the relationship among factors influencing rural tourism: A hierarchical approach.
J. Organ. Change Manag. 2022,35, 385–407. [CrossRef]
5.
Andersson, T.D.; Mossberg, L.; Therkelsen, A. Food and tourism synergies: Perspectives on consumption, production and
destination development. Scand. J. Hosp. Tour. 2017,17, 1–8. [CrossRef]
6.
Koseoglu, M.A.; Law, R.; Cagri Dogan, I. Exploring the social structure of strategic management research with a hospitality
industry focus. Int. J. Contemp. Hosp. Manag. 2018,1, 436–488. [CrossRef]
7.
Rachao, S.; Breda, Z.; Fernandes, C.; Joukes, V. Food tourism and regional development: A systematic literature review. Eur. J.
Tour. Res. 2019,21, 33–49. [CrossRef]
8.
Zhang, H.; Xiao, Y. Planning island sustainable development policy based on the theory of ecosystem services: A case study of
Zhoushan Archipelago, East China. Isl. Stud. J. 2020,15, 237–252. [CrossRef]
9.
Santeramon, F.G.; Seccia, A.; Nardone, G. The synergies of the Italian wine and tourism sectors. Wine Econ. Policy 2016,6, 71–74.
[CrossRef]
10.
Polese, F.; Botti, A.; Grimaldi, M.; Monda, A.; Vesci, M. Social innovation in smart tourism ecosystems: How technology and
institutions shape sustainable value co-creation. Sustainability 2018,10, 140. [CrossRef]
11.
Adongo, R.; Choe, J.Y.; Han, H. Tourism in Hoi An, Vietnam: Impacts, perceived benefits, community attachment and support for
tourism development. Int. J. Tour. Sci. 2017,17, 86–106. [CrossRef]
12.
Johnson, A.G.; Neuhofer, B. Airbnb—An exploration of value co-creation experiences in Jamaica. Int. J. Contemp. Hosp. Manag.
2017,29, 2361–2376. [CrossRef]
13.
Paulauskaite, D.; Powell, R.; Coca-Stefaniak, A.; Morrison, A. Living like a local: Authentic tourism experiences and the sharing
economy. Int. J. Tour. Res. 2017,19, 619–628. [CrossRef]
14.
Schuckert, M.; Peters, M.; Pilz, G. The co-creation of host–guest relationships via couchsurfing: A qualitative study. Tour. Recreat.
Res. 2018,43, 220–234. [CrossRef]
15.
Campos, A.C.; Mendes, J.; Do Valle, P.O.; Scott, N. Co-creation of tourist experiences: A literature review. Curr. Issues Tour. 2018,
21, 369–400. [CrossRef]
16.
Tru
t
,
escu, M.N.; Rotarus, A. Approach on sustainable development through the involvement of local community in tourism food
industry: A case study of Azuga resort, Romania. J. Environ. Tour. 2018,8, 12–23. [CrossRef]
17.
Robinson, R.; Getz, D.; Dolnicar, S. Food tourism subsegments: A data-driven analysis. Int. J. Tour. Res. 2018,20, 367–377.
[CrossRef]
18.
Lugosi, P. Developing and Publishing Interdisciplinary Research: Creating Dialogue, Taking Risks. Hosp. Soc. 2020,10, 217–230.
[CrossRef]
19.
Lugosi, P. Exploring the hospitalitytourism nexus: Directions and questions for past and future research. Tour. Stud. 2021,
21, 24–35. [CrossRef]
20.
Islahuddin, I.; Akib, H.; Eppang, B.M.; Salim, M.A.M.; Darmayasa, D. Reconstruction of the actor collaboration model in the
development of marine tourism destinations in the new normal local economy. Linguist. Cult. Rev. 2021,5, 1505–1520. [CrossRef]
21.
Bhatta, K.B.; Joshi, B.R. Community Collaboration with Tourism Stakeholders: Issues and Challenges to Promote Sustainable
Community Development in Annapurna Sanctuary Trail, Nepal. Saudi J. Eng. Technol. 2023,8, 146–154. [CrossRef]
22.
Weidenfeld, A.; Hall, C.M. Tourism and innovation: A cross sectoral and interregional perspective. In The Wiley Blackwell
Companion to Tourism; Wiley-Blackwell: Hoboken, NJ, USA, 2024; pp. 652–664.
23.
Chen, Y.; Lin, Y.; Su, X.; Chen, P.; Song, H. Multiple Effects of Agricultural Cultural Heritage Identity on Residents’ Value
Co-Creation—A Host–Guest Interaction Perspective on Tea Culture Tourism in China. Agriculture 2025,15, 57. [CrossRef]

Agriculture 2025,15, 604 20 of 22
24.
Anderson, W. Linkages between tourism and agriculture for inclusive development in Tanzania: A value chain perspective.
J. Hosp. Tour. Insights 2018,1, 168–184. [CrossRef]
25.
Dipokusumo, B.; Sjah, T. Key sectors and inter-sectoral linkages in economic development in East Lombok Regency, West Nusa
Tenggara Province. IOP Conf. Ser. Earth Environ. Sci. 2024,681, 012062.
26.
Tchouamou Njoya, E.; Nikitas, A. Assessing agriculture–tourism linkages in Senegal: A structure path analysis. GeoJournal 2020,
85, 1469–1486. [CrossRef]
27.
Salihoglu, G.; Gezici, F. Linkages Between Tourism and Agriculture: The Case of Turkey. In Regional Science Perspectives on Tourism
and Hospitality; Ferrante, M., Fritz, O., Öner, Ö., Eds.; Advances in Spatial Science; Springer: Cham, Switzerland, 2021. [CrossRef]
28.
Loizou, E.; Karelakis, C.; Galanopoulos, K.; Mattas, K. The role of agriculture as a development tool for a regional economy.
Agric. Syst. 2020,173, 482–490. [CrossRef]
29.
Sych, V.; Yavorska, V.; Kolomiyets, K. Formation of regional inter-sectoral complexes of recreation and tourism activity as a sign
of structural reorganization of the economy. J. Educ. Health Sport 2021,11, 237–248. [CrossRef]
30.
Li, F.; Sastri, R.; Edziah, B.K.; Setiyawan, A. Exploring the inter-sectoral and inter-regional effect of tourism industry in Indonesia
based on input-output framework. Kybernetes 2024,ahead of printing. [CrossRef]
31.
Banjac, M. Sinergija poljoprivrede i ugostiteljstva u funkciji razvoja turizma u Vojvodini. In Faculty of Sciences; University of Novi Sad:
Novi Sad, Serbia, 2022. Available online: https://nardus.mpn.gov.rs/handle/123456789/20574 (accessed on 1 September 2022).
32.
Novkovi´c, N.; Vukeli´c, N.; Mutavdži´c, B.; Teki´c, D. Development Opportunities of Agribusiness in the AP Vojvodina.
Contemp. Agric. 2023,72, 14–21. [CrossRef]
33.
Tešanovi´c, D.; Banjac, M.; Radivojevi´c, G.; Stankovi´c, A. The influence of local agriculture in tourism development. In Proceedings
of the Contemporary Trends in Tourism and Hospitality 2019, Get Ready for iGeneration, Novi Sad, Serbia,
12–13 September 2019
;
pp. 62–63, ISBN 978-86-7031-519-8.
34.
Banjac, M.; Tešanovi´c, D.; Devi´c Blanuša, J. Importance of connection between agricultural holding and catering facilities in
development of tourism in Vojvodina. In Proceedings of the Me ¯
dunarodni Nauˇcni Skup “Nauka i Praksa Poslovnih Studija”,
Banja Luka, Bosnia and Herzegovina, 15 September 2017.
35.
Tešanovi´c, D.; Kalenjuk, B.; Banjac, M. Food and tourism synergy-inpact on sustainable development of the region. In Proceedings
of the Jahorina Business Forum 2018, Sustainable Tourism and Institutional Environment, Jahorina, Bosnia and Herzegovina,
22–24 March 2017; pp. 209–213, ISSN 2303–8969.
36.
Novakovi´c, T.; Mutavdži´c, B.; Mili´c, D.; Teki´c, D. Analysis of the subsidy structure in agriculture of Vojvodina. J. Process. Energy
Agric. 2019,23, 142–146. [CrossRef]
37.
Strategija Razvoja Turizma R. Srbije 2016–2025. Available online: https://mto.gov.rs/extfile/sr/207/strategija.pdf (accessed on
20 September 2024).
38.
Strategija Razvoja Poljoprivrede i Ruralnih Podruˇcja 2014–2024. Available online: https://pravno-informacioni-sistem.rs/eli/
rep/sgrs/vlada/strategija/2014/85/1 (accessed on 20 September 2024).
39.
Kalenjuk Pivarski, B.; Grubor, B.; Banjac, M.; Ðerˇcan, B.; Tešanovi´c, D.; Šmugovi´c, S.; Radivojevi´c, G.; Ivanovi´c, V.; Vujasinovi´c,
V.; Stoši´c, T. The Sustainability of Gastronomic Heritage and Its Significance for Regional Tourism Development. Heritage 2023,
6, 3402–3417. [CrossRef]
40.
Šmugovi´c, S.; Kalenjuk Pivarski, B.; Ivanovi´c, V.; Tešanovi´c, D.; Novakovi´c, D.; Mari´c, A.; Lazarevi´c, J.; Pauni´c, M. The Effects of
the Characteristics of Catering Establishments and Their Managers on the Offering of Dishes Prepared with Traditional Food
Products in Baˇcka Region, Serbia. Sustainability 2024,16, 7450. [CrossRef]
41.
´
Ciri´c, M.; Tešanovi´c, D.; Kalenjuk Pivarski, B.; ´
Ciri´c, I.; Banjac, M.; Radivojevi´c, G.; Grubor, B.; Toši´c, P.; Simovi´c, O.; Šmugovi´c, S.
Analyses of the Attitudes of Agricultural Holdings on the Development of Agritourism and the Impacts on the Economy, Society
and Environment of Serbia. Sustainability 2021,13, 13729. [CrossRef]
42. Birch, D.; Memery, J. Tourists, local food and the intention-behaviour gap. J. Hosp. Tour. Manag. 2020,43, 53–61. [CrossRef]
43.
Qian, C.; Sasaki, N.; Shivakoti, G.; Zhang, Y. Effective governance in tourism development—An analysis of local perception in the
Huangshan mountain area. Tour. Manag. Perspect. 2016,20, 112–133. [CrossRef]
44.
Qian, C.; Sasaki, N.; Jourdain, D.; Kim, S.M.; Shivakoti, P.G. Local livelihood under different governances of tourism development
in China: A case study of Huangshan mountain area. Tour. Manag. 2017,61, 221–233. [CrossRef]
45.
Calabrò, G.; Vieri, S. Cultural and rural tourism: Potential synergies for a new economic development pattern. The Italian case.
Cactus Tour. J. 2018,18, 27–35.
46.
Broccardo, L.; Culasso, F.; Truant, E. Unlocking Value Creation Using an Agritourism Business Model. Sustainability 2017,9, 1618.
[CrossRef]
47.
Purnomo, S.; Rahayu, E.S.; Riani, A.L.; Suminah, S.; Udin, U.D.I.N. Empowerment model for sustainable tourism village in an
emerging country. J. Asian Financ. Econ. Bus. 2020,7, 261–270. [CrossRef]
48.
Choya, K.L.; Leea, W.B.; Lob, V. Design of an intelligent supplier relationship management system: A hybrid case based neural
network approach. Expert Syst. Appl. 2003,24, 225–237. [CrossRef]

Agriculture 2025,15, 604 21 of 22
49.
Richardson-Ngwenya, P.; Momsen, J. Tourism and agriculture in Barbados: Changing relationships. In Tourism and Agriculture;
Routledge: London, UK, 2011; pp. 139–148.
50.
Kock, M. The Development of an E Elopment of an Eco-Gastro-Gastronomic Tourism (egt) Supply Tourism (egt) Supply Chain-Analyzing
Linkages Between Farmer, Restaurants, and Tourists in Aruba; University of Central Florida: Orlando, FL, USA, 2013.
51.
Pavlukovi´c, V.; Kovaˇci´c, S.; Pavlovi´c, D.; Stankov, U.; Cimbaljevi´c, M.; Pani´c, A.; Radojevi´c, T.; Pivac, T. Tourism destination
competitiveness: An application model for Serbia. J. Vac. Mark. 2024. [CrossRef]
52.
Boesen, M.; Sundbo, D.; Sundbo, J. Local food and tourism: An entrepreneurial network approach. Scand. J. Hosp. Tour. 2017,
17, 76–91. [CrossRef]
53. Johnston, R.J. A dynamic model of sustainable tourism. J. Travel Res. 2005,44, 124–134. [CrossRef]
54.
Sidali, K.L.; Hemmerling, S. Developing an authenticity model of traditional food specialties: Does the self-concept of consumers
matter? Br. Food J. 2014,116, 44–60. [CrossRef]
55.
Bogdanov, N.; Babovi´c, M. Radna Snaga i Aktivnosti Poljoprivrednih Gazdinstava [Labour Force and Activities of Agricultural Holdings];
Statistical Office of the Republic of Serbia: Belgrade, Serbia, 2014.
56.
Bubalo-Živkovi´c, M.; Kalenjuk, B.; Luki´c, T.; Ðerˇcan, B. Who Is Still Engaged in Agriculture in Vojvodina? Eur. Geogr. Stud. 2018,
5, 32–41.
57.
Ðerˇcan, B.; Gatari´c, D.; Bubalo-Živkovi´c, M.; Belij Radin, M.; Vukoiˇci´c, D.; Kalenjuk Pivarski, B.; Luki´c, T.; Vasi´c, P.; Nikoli´c, M.;
Lutovac, M.; et al. Evaluating farm tourism development for sustainability: A case study of farms in the peri-urban area of Novi
Sad (Serbia). Sustainability 2023,15, 12952. [CrossRef]
58. Republiˇcki Zavod za Statistiku. Available online: https://www.stat.gov.rs/ (accessed on 15 November 2024).
59.
Kalenjuk Pivarski, B.; Šmugovi´c, S.; Teki´c, D.; Ivanovi´c, V.; Novakovi´c, A.; Tešanovi´c, D.; Banjac, M.; Ðerˇcan, B.; Peuli´c, T.;
Mutavdži´c, B.; et al. Characteristics of traditional food products as a segment of sustainable consumption in Vojvodina’s
hospitality industry. Sustainability 2022,14, 13553. [CrossRef]
60.
Grubor, B.; Kalenjuk Pivarski, B.; Ðerˇcan, B.; Tešanovi´c, D.; Banjac, M.; Luki´c, T.; Živkovi´c, M.B.; Udoviˇci´c, D.I.; Šmugovi´c, S.;
Ivanovi´c, V.; et al. Traditional and authentic food of ethnic groups of Vojvodina (Northern Serbia)—Preservation and potential for
tourism development. Sustainability 2022,14, 1805. [CrossRef]
61.
Vuksanovi´c, N.; Tešanovi´c, D.; Kalenjuk, B.; Porti´c, M. Gender, age and education differences in food consumption within a
region: Case studies of Belgrade and Novi Sad (Serbia). Acta Geogr. Slov. 2019,59, 72–80. [CrossRef]
62.
Aichholzer, G. The Delphi method: Eliciting experts’ knowledge in technology foresight. In Interviewing Experts; Bogner, A.,
Littig, B., Menz, W., Eds.; Palgrave Macmillan: London, UK, 2009; pp. 252–274. [CrossRef]
63.
Doke, E.R.; Swanson, N.E. Decision variables for selecting prototyping in information systems development: A Delphi study of
MIS managers. Inf. Manag. 1995,29, 173–182. [CrossRef]
64.
Skulmoski, G.J.; Hartman, F.T.; Krahn, J. The Delphi method for graduate research. J. Inf. Technol. Educ. Res. 2007,6, 1–21.
[CrossRef]
65. Connelly, L.M. Pilot studies. Medsurg. Nurs. 2008,17, 411–412. [PubMed]
66. Little, R.J.A.; Rubin, D.B. Statistical Analysis with Missing Data; John Wiley & Sons: New York, NY, USA, 1987.
67. Howell, D. Statistical Methods for Psychology, 8th ed.; Wadsworth: Belmont, CA, USA, 2013.
68.
O’Connor, B.P. SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP
test. Behav. Res. Methods Instrum. Comput. 2000,32, 396–402. [CrossRef] [PubMed]
69. Pallant, J. SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS; Routledge: London, UK, 2020.
70.
MacCallum, R.C.; Widaman, K.F.; Preacher, K.J.; Hong, S. Sample size in factor analysis: The role of model error. Multivar. Behav.
Res. 2001,36, 611–637. [CrossRef]
71.
Hoyle, R.H. The Structural Equation Modeling Approach: Basic Concepts and Fundamental Issues. In Structural Equation
Modeling: Concepts, Issues, and Applications, 1st ed.; Hoyle, R.H., Ed.; Sage Publications, Inc.: London, UK, 1995; pp. 1–15.
72. Browne, M.W.; Cudeck, R. Alternative ways of assessing model fit. Sociol. Methods Res. 1992,21, 230–258. [CrossRef]
73. Kline, R.B. Principles and Practice of Structural Equation Modeling, 4th ed.; Guilford Publications: New York, NY, USA, 2016.
74.
Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark.
Res. 1981,18, 39–50. [CrossRef]
75.
Sanches-Pereira, A.; Onguglo, B.; Pacini, H.; Gómez, M.F.; Coelho, S.T.; Muwanga, M.K. Fostering local sustainable development
in Tanzania by enhancing linkages between tourism and small-scale agriculture. J. Clean. Prod. 2017,162, 1567–1581. [CrossRef]
76. Djuri´c, K. Poljoprivreda i Ruralni Razvoj Srbije u Procesu Evropskih Integracija; Poljoprivredni Fakultet, Univerzitet u Novom Sadu:
Novi Sad, Serbia, 2018.
77.
Cvijanovi´c, D.; Subi´c, J.; Parauši´c, V. Poljoprivredna Gazdinstva Prema Ekonomskoj Veliˇcini i Tipu Proizvodnje u Republici Srbiji;
Republiˇcki Zavod za Statistiku: Belgrade, Serbia, 2014.
78.
Mei, X.Y. Networking and collaboration between tourism and agriculture: Food tourism experiences along the National Tourist
Routes of Norway. Scand. J. Hosp. Tour. 2017,17, 59–75. [CrossRef]

Agriculture 2025,15, 604 22 of 22
79.
Qiu, P.; Zhou, Z.; Kim, D.-J. A new path of sustainable development in traditional agricultural areas from the perspective of open
innovation—A coupling and coordination study on the agricultural industry and the tourism industry. J. Open Innov. Technol.
Mark. Complex. 2021,7, 16. [CrossRef]
80.
Stanojevi´c, J.; Krsti´c, B. Intersectoral linkages and their contribution to economic growth in the Republic of Serbia. Facta Univ.
2019,16, 197–213. [CrossRef]
81.
Koutsouris, A.; Gidarakou, I.; Grava, F.; Michailidis, A. The phantom of (agri)tourism and agriculture symbiosis? A Greek case
study. Tour. Manag. Perspect. 2014,12, 94–103. [CrossRef]
82.
Ohe, Y. Assessing managerial efficiency of educational tourism in agriculture: Case of dairy farms in Japan. Sustainability 2017,
9, 1931. [CrossRef]
83.
Castellano-Álvarez, F.J.; del Río-Rama, M.d.l.C.; Álvarez-García, J.; Durán-Sánchez, A. Limitations of Rural Tourism as an
Economic Diversification and Regional Development Instrument. The Case Study of the Region of La Vera. Sustainability 2019,
11, 3309. [CrossRef]
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