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.

1. Introduction

Globally, the establishment of sustainable intersectoral linkages between tourism and agriculture is increasingly being explored through the application of various models [1,2,3,4,5]. The intersectoral connections between agriculture, hospitality, and tourism have become a growing topic within the scientific community, as confirmed by numerous relevant studies [6,7,8]. 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 [7,11,12,13,14,15]. Notably, hospitality providers, who play a crucial role in distributing agricultural and gastronomic products to tourists, have often been overlooked [16,17,18,19].

It is important to highlight that tourism planners have recognized the significant potential for developing intersectoral cooperation between tourism, hospitality, and agriculture [20,21,22,23]. However, many researchers have pointed to the weak connections among these sectors [24,25,26]. Such cooperation is more successful in microregions where farmers produce authentic food products, which are then distributed to both tourists and hospitality 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 [28,29,30]. 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 Development 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 hospitality and tourism in Vojvodina has revealed that cooperation between hospitality establishments and farmers is not well established and is only partially recognized as a key component of future development [34,35]. Ćirić 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.

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 fundamental 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, hospitality, 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, underdeveloped 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 linkages between agriculture and tourism are realized through the Structure Path Analysis (SPA) approach [26]. The Empowerment Model for Sustainable Tourism Villages in Indonesia 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, incorporating 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 development, 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 agriculture, 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 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 linkages 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 complicates 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 explaining 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 collaboration, 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].

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, representatives 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 linkages [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].

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) 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, 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 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. 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. Differences 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).

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. Eigenvalues and percentages of explained variance in extracted factors.

No. of Factors	Initial Values				After Extraction		λ After 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. 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
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

) 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.

The second factor includes nine items (

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

) and explains 5.68% of the common variance. The factor structure indicates that it is named Education.

The factor structure matrix of the third factor includes four items (

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

) and explains 2.84% of the common variance. The factor structure indicates that it is named Government Policy.

The factor structure matrix of the fourth factor includes five items (

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

) and explains 2.56% of the common variance. The factor structure indicates that it is named Contribution of Farmers and Hospitality Providers.

The factor structure matrix of the fifth factor includes two items (

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

) and explains 2.19% of the common variance. The factor structure indicates that it is named Infrastructure.

The reliability of the retained factors is acceptable to excellent (

Table 8. Descriptive data, reliability, and average inter-item correlation.

Scales of the Questionnaire	AS	SD	α	MIC
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

). The Sustainability 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.

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. 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.

.

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. 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

shows the factor loadings of the CFA model.

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. 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

, the correlations between the factors are presented.

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 correlations 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 cooperation 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 complementary 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 initiatives. 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 regarding 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.

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. Differences in dimensions between farmers, hospitality service providers, and tourism actors.

Dimensions	F (2,586)	p	η2
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. Post hoc Bonferroni test for testing the significance of differences between farmers, hospitality 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

, 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.

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 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 [48,49,50,75]. 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 proposed 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 intersectoral connections [48,49,50,75]. 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 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. Indicators, 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. 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. Inadequate 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 [48,49,50].

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 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 [81,82,83]. 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 methodological 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 decisions and improving intersectoral cooperation. Its application could contribute to strengthening 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.

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.