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Article

Academic–Practical Cooperation: A Case Study of Rural Destination Image

by
Yael Ram
1,*,
Shahar Shilo
2,
Lee Gafter
3 and
Noga Collins-Kreiner
4
1
Department of Tourism Studies, Ashkelon Academic College, Ashkelon 78211, Israel
2
Tourism—Research and Tourism Planning, Department of Tourism Studies, Ashkelon Academic College, Ashkelon 78100, Israel
3
The Interuniversity Institute for Marine Sciences, Technion Israel Institute of Technology, Haifa 3200003, Israel
4
School of Environmental Sciences, University of Haifa, Haifa 3103301, Israel
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5330; https://doi.org/10.3390/su17125330
Submission received: 16 April 2025 / Revised: 28 May 2025 / Accepted: 2 June 2025 / Published: 9 June 2025
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Abstract

The paper makes a novel contribution to bridging the academic–practitioner divide in tourism studies, specifically in the context of the destination image. An advanced, robust estimation modeling approach that analyzed a lagged commercial international survey of potential tourists reveals that academics and practitioners tend to draw different conclusions from the same dataset based on their different hypotheses. These findings suggest that academics and practitioners have limited perspectives of destination image, casting doubt on the relevance of existing destination image models, particularly when applied to individuals who already hold a less-than-positive perception. Hence, this study suggests four steps for enhancing cooperation between academics and practitioners: the use of a mixed team, re-examination of commercial (lagged) datasets, developing a combined set of hypotheses, and conducting rigorous analysis. The findings advance both theoretical understanding and practical strategy by showing that cognitive marketing messages may reinforce existing views but rarely overturn them. To support the market, academics should focus on conative destination image, develop segmentation tools to identify the target groups based on their overall destination image, and build dynamic destination image models that portray the differences between the groups and conditions.

1. Introduction

In its agenda for 2030, the European Council calls for enhancing sustainable cooperation between universities and businesses in national, regional, and local contexts [1]. The present paper follows this call and suggests bridges for spanning the academic–practical divide. This divide has been described as a “rigor versus relevance” argument ([2], p.1) and is one of the most significant limitations of academia in influencing and impacting reality. This means that rigorous articles provide significant, well-cited implications, but their relevance to practitioners is questionable.
Cooperation between academics and stakeholders in sustainable tourism destinations is strongly recommended [3]. However, a recent systematic literature review on sustainable place branding and image [4] did not refer to the unique perceptions of academics and practitioners in developing and promoting place images. Consequently, the present study aims to map the gaps between academics and practitioners regarding destination images and to present bridges for these gaps.
Destination image is the total mental impressions and perceptions tourists hold of a destination [5]. It is crucial for the sustainable development of a destination [6], as it is one of the building blocks of successful tourism marketing [7,8,9]. Furthermore, destination image is also a widely studied concept. The extensive research on destination image, as presented in Figure 1, and the importance of sustainable tourism development suggests that cooperation between academics and practitioners is more relevant than ever.
Using a case study, the benefits of academic–practical cooperation are demonstrated.
The Galilee region (Israel) is a rural destination with nature, heritage, and cultural assets [10]. At the beginning of this study (March 2021), an international commercial research firm conducted a commercial study regarding six potential international target markets in the Galilee region from five countries. In 2024, after the end of the embargo period, this dataset was inspected by academic advisors who analyzed it using theoretical concepts and advanced modeling approaches. The authors of this paper represent the two groups in question: the practitioners, who are part of the commercial study, and the academics.
In terms of structure, the present paper, which aims to bridge the academic–commercial divide in the context of destination image, begins with a literature review, which presents the divide in the tourism domain and its suggested bridges. It then explains the concept of “destination image”, with an emphasis on the conative destination image that comprises action, i.e., the individual’s actual conduct or intention to revisit and recommend the destination to others (or even to spread positive word of mouth) and behavioral intentions [11]. This part also contains three hypotheses that practitioners and researchers set. The Methods Section presents the case study in Israel’s Galilee region and its adaptation to academic and scientific requirements. The Results Section is followed by Discussion and Conclusion Sections, emphasizing the importance of collaboration between academics and practitioners for creating new, adequate knowledge in general and concerning destination image. More specifically, the conclusions indicate that messages and cognitive stimuli have limited influence on changing entrenched perceptions, reinforcing the need for new theoretical models that address stability versus change in tourist decision-making.

2. Literature Review

2.1. The Academic—Practical Divide in Tourism

The term “the great divide” in tourism studies refers to the gap between tourism scholars and practitioners [12]. Although the term has been in use since the late 1990s [13], it still reflects the ongoing concerns regarding the lack of cooperation between research and academia and between practice and industry in tourism [12,14], which probably hinders sustainable development.
The divide also encompasses the rigor vs. relevance issue [15], with relevance expressed from industrial and sometimes instrumental perspectives [16]. Also prominent is the gap between the higher education curriculum and the needs of the tourism industry [17]; this gap is not discussed in the present study.
Knowledge transfer between the industry and research centers is the first suggested bridge between research and practice for sustainable development. Yet, there are possible barriers to implementation from both sides, including a lack of cooperation by researchers and hostile attitudes from the industry [14,18].
The data-sharing solution is the second optional bridge, as the tourism industry possesses data researchers need [19]. However, such cooperation is rare. In big data studies, for example, only six percent of the reviewed tourism studies (2007–2017) used private and public data, arguably for reasons related to privacy [20]. As a response to privacy issues, it is recommended to use non-recent aggregate data [21].
The “big picture” [21] is the third bridge; however, each side argues that the other has a narrow perspective. For example, practitioners are more interested in economic and technological short-term goals overlooked by tourism researchers [13,15,22], and tourism researchers, like other academics, are motivated by academic publications, which are irrelevant for practitioners [16]. Critical thinking as a means for a “big picture” creation was also suggested as a bridge between academics and tourism firms and between the authorities and NGOs [22]. Another direction involved sustainability, with the incorporation of measures of community involvement into tourism development projects [13]. The present paper employs these bridges—knowledge transfer [14,18], data sharing [19], and the big-picture perspective [13,15,16,22]—to study, define, and analyze the concept of destination image in rigorous, sustainable, and relevant ways.

2.2. Destination Image and Intentions—Review and Hypotheses

Studies have represented destination image in four combined or separate constructs: “overall destination image”, “cognitive image”, “affective image”, and “conative image”. Overall image refers to the general assessment; cognitive image relates to knowledge and beliefs regarding attributes of the destination; affective image refers to emotional responses; and conative image refers to the possibility of or tendency for behavior vis-à-vis the destination [11].
Surrounding these constructs of destination image, we can identify at least three unclear issues: their structural order (hierarchical or flat structure), how the destination image is associated with intentions, and how stable these intentions are.
The first issue is purely theoretical and addresses the structural considerations of the concept of destination image. Is it a flat, single-level construct [5,9] or a multilevel concept, with three constructs (affective, cognitive, and conative) as lower-order elements and the overall destination image as a higher-order element [11,23]?
Other researchers arranged the three constructs according to internal hierarchy or order, in which affective destination image is fully or partially mediated by the relationship between cognitive and conative image [24,25,26,27], with or without other factors.
Given that the first issue is purely theoretical, the hypothesis was set by the academics, who adopted the mediated model of destination image as follows:
H1 (academics).
The cognitive image of the rural destination of Galilee held by potential tourists fully mediates the overall image of Israel with the conative image of Israel. In this hypothesis, the overall orientation toward visiting Israel was incorporated as an independent variable, the cognitive image as a mediator, and the conative image as a dependent variable.
The second issue concerns the association between destination image and intentions, which is characterized by three approaches. The first examines how cognitive and emotional destination image directly or indirectly impacts behavioral intentions such as willingness to pay, visit, revisit, and recommend [28,29,30]. This approach does not refer to the construction of a conative destination image. The second approach in the research relates to the conative image and behavioral intentions [11,23] and examines how the conative image directly or indirectly impacts behavioral intentions. According to the third approach [24,25,26], the conative image encompasses intentions to revisit and recommend and is affected by cognitive and emotional destination image. This approach does not refer to behavioral intentions on their own.
The timing of the examination (before, during, or after the visit) shapes the content of the conation and intentions. While a study on potential studies [28] focuses on the visitation and recommendation intentions of potential tourists, other studies have targeted actual tourists and therefore concentrated on revisitation and recommendation [23,24,25,26,30,31] with interaction with various sociodemographic variables [32].
Given that the practitioners were interested in potential tourists, their hypothesis, based on the knowledge above, associated with potential tourists and was set as follows:
H2a (practitioners).
The knowledge of potential tourists regarding the touristic attributes of Galilee (=cognitive destination image) is associated with intentions to visit (=conative image).
H2b (practitioners).
Sociodemographic segments (age, religion, and gender) are associated with a conative image among potential tourists.
In these hypotheses, the representative of the practitioners wanted to validate the results they obtained from the commercial survey firm during the original submission (2021).
The third issue focuses on the stability and change in behavioral intention and the conative image. Destination image, as defined by affective, cognitive, and overall dimensions, is perceived as a relative dynamic construct that improves as familiarity with the destination grows [33] but decays over time [34]. However, these studies did not refer to the conative dimension and behavioral intentions. In their review regarding destination image, Chu et al. indicated a lack of studies regarding the role of behavioral intentions before travel. They noted the need to explore the factors that shape them [35]. This gap in theory also has practical implications, as behavioral intentions are considered a key driver for actual behavior [36]. Following this theoretical and practical gap, an additional hypothesis was set as follows:
H3 (practitioners and academics).
After learning the attributes of Galilee (=cognitive image), potential tourists change their position toward Israel (=overall image) to a more positive behavioral intention (=conative image).
This study’s rationale and hypotheses are presented in Figure 2.

3. Methods

The present study aims to bridge the academic–practical divide regarding the destination image of potential tourists [28]. Using practitioners’ data is an implementation of the data-sharing bridge [19], and building a mixed project team composed of practitioners and academics reflects the use of the knowledge-transfer bridge [14,18]. And lastly, the setting of two research hypotheses—by practitioners and researchers—is oriented toward the big-picture perspective [13,15,16,22].

3.1. The Case Study—A Rural Destination

Galilee is a region in northern Israel (See Figure 3) known for its natural beauty, historical significance, cultural richness, and Christian religious significance [10]. Although it is a popular destination for domestic tourists, the Free Independent International Tourist Market (FIT) is less familiar with the area, as 95% of the international tourists who visit the region belong to the organized market [37].
Tiberias, located on the Sea of Galilee, attracted 34% of the international tourists to Israel. Since Galilee does not have an airport, visiting it requires all tourists from abroad to enter Israel through the country’s airports or land border crossings [38]. In the Winter of 2021, the tourism authorities in Galilee commissioned a commercial customer survey to examine the region’s relevance as a destination for incoming FIT and organized tourists to Israel.

3.2. Participants and Procedure

The research employed a representative sample of the adult population in Italy, Germany, France, the UK, and the United States. A market research company recruited participants in March 2021. In each country, a local firm was recruited by the representatives of the Galilee authorities. These international target populations were chosen based on their importance to tourism in Israel [39].
A total of 1662 participants took part in the survey, of which 83 (5%) stated that Israel was not at all appealing to them as a tourist destination, and 142 (8.5%) indicated that they had never traveled outside their own country. These participants were screened out of the survey. The recruitment process guaranteed an equal quota of 200 participants from each country-based group, except for the American participants, which saw 200 recruited from Evangelical parishes and another 200 recruited from within the Jewish community. Respondents’ gender was balanced between men and women, and their ages ranged from 20 to 70 (M = 45.6, SD = 13.4). Most of the sample reported belonging to the Christian faith (61.6%), having an average or above-average income (82.9%), and having traveled outside the US or Europe at least once in the last five years (86.6%).
Two-hundred and fifty-four (21.1%) participants had visited Israel at least once in the past 10 years. Of them, more than half (N = 137, 54%) had visited Galilee during their last visit. Descriptive statistics for each country are presented in Table 1. Due to the variability in the motivations to visit Israel, prior beliefs regarding the country, and its general touristic appeal, our study used the data collected from the 800 respondents from Italy, Germany, France, and the UK and disregarded the responses of American Jewish and Evangelical respondents.

3.3. Measures

3.3.1. Background Variables

Participants completed a brief questionnaire assessing demographic characteristics, including age, gender, faith/religion, and income. The questionnaire also evaluated their traveling patterns and preferences, the number of trips they took outside their own country in the past few years, and the destinations to which they traveled.

3.3.2. Cognitive Image of Tourist Sustainable Attributes of Galilee

The respondents were given four short descriptions of attractions and activities that Galilee offers tourists and were asked to rank their impression (from 1 = Not interested at all to 7 = Very much interested). The questions highlighted the variety of rural attractions in the region, the opportunity to participate in tours following the footsteps of Jesus in the area, the connection to European heritage and history, and the abundance of themed trails available in the region.

3.3.3. Overall Image of Israel as a Travel Destination

Respondents were asked this question at the beginning of the questionnaire, assessing Israel’s appeal as a tourism destination (from 1 = Not appealing at all to 7 = Very appealing).

3.3.4. Israel’s Conative Image as a Travel Destination

At the end of the questionnaire, respondents were asked about their willingness to visit Israel in the future (from 1 = Definitely not to 4 = Definitely yes).

3.3.5. Sociodemographic Variables

The variables of age, gender, and whether the respondent identified as Christian were used in the association matrix and as control variables in our study. The effect of the respondents’ past visits to Israel was also tested as a covariate; however, it did not contribute to the model’s explanatory power, nor did it affect the significance of other explanatory variables in our model and therefore excluded from the analysis presented here.

3.4. Analytic Strategy

The analysis in the present study was conducted using R software [40]. Of the total sample size of 800 participants, none of the data were missing; thus, no imputation procedures were required for the data in our study. Statistical association analyses were conducted to explore the association between our study variables. The association was calculated using the Pearson correlation coefficient when applicable, Kendall’s τ B and τ C were employed when values were limited to less than five values, and the Point Biserial and φ measures of association were employed when testing for categorical variables [41]. All continuous data were tested for univariate and multivariate normality using the Shapiro–Wilk and Mardia’s tests.
We applied a mediation analysis analytic scheme to test the mediating role of the cognitive image of Galilee in the association between the overall image and the conative image of Israel. Age, gender, and belonging to the Christian faith were used as control variables in the analysis. For each question regarding the cognitive image of Galilee, we screened for multivariate outliers using the Mahalanobis distance measure for our raw data. This was carried out to detect and omit observations that may severely distort parameter estimation by creating inflated point estimates that may lead to false positive results [42]. We applied the common conservative threshold of p < 0.0001 to set a critical χ 2 value and eliminated observations that exceeded this value [43]. After the initial outliers screening procedure, the data were tested for the standard OLS regression assumptions [44]. Since the data violated the assumptions regarding the error distribution, we applied a quasi-parametric mediation analysis scheme, which is more robust in the case of such violations. The mediation procedure in this work combined the MM-estimator approach and the fast-and-robust bootstrap, which offer better control for type I errors and return highly efficient estimators [45]. The MM-regression approach [46] uses a bi-square redescending score function and produces data-dependent and iteratively reweighted least square estimators based on the deviation of each observation. Since our dependent variable of the conative image is ordinal, estimation can lead to a non-differentiable sample objective function and convergence issues regarding the maximum score estimator [47]. To resolve such issues, we applied a Jittering technique by adding uniformly distributed noise to the dependent variable (i.e., − Z = Y + U 0 , 1 ) [48]. As the constructed variable’s quantiles have a one-to-one relationship with those of the original variable, and the noise is averaged out in the estimation process, it can be used as the basis for our inference [49,50]. We measured each model’s goodness-of-fit using the robust R 2 measure, a corrected measure of the standard R 2 value based on a weighted version of the model’s sum of squares. The analysis was performed using the R package robmed [45].
Whereas the analysis produces point estimates for the coefficients using both the full sample and the bootstrap replicates, we followed the suggestion of remaining within the bootstrap framework and reporting the mean estimate of the bootstrap procedure [51]. The statistical significance of the direct and total effects estimates was calculated using the asymptotic bootstrap z-tests, using the first and second central moments of the bootstrap replicates. Confidence intervals for the estimates of the indirect effects were calculated using the bias-corrected accelerated method [52].

3.5. Power Analysis

Although recruitment was based on our initial recruitment effort of 800 participants (after screening out the American samples), we also performed a power analysis to validate the sufficiency of the data gathered. Since the significance of the indirect effect is determined using a bootstrapping procedure, we use a Monte Carlo simulation approach for the analysis [53]. The analysis generated a minimum sample size of 133 observations, for a power of 0.8, in a 5000 replicates procedure, given the estimates of the association coefficients from our data.

4. Results

4.1. Hypothesis 1: Cognitive Images as Mediators Between Overall and Conative Images

Table 2 shows the mediation analysis results on all four questions of the cognitive image of Galilee as a sustainable destination, and Figure 4 is a visual diagram of the mediated relationships. A positive relationship exists between the overall image and the conative image of Israel. The total effect of the overall image on the latter is significant ( β ^ = 0.35 ,   p v < 0.001 ; β ^ = 0.36 , p v < 0.001 ; β ^ = 0.36 , p v < 0.001 ;   β ^ = 0.35 ,   p v < 0.001 , respectively), indicating that a more positive overall image is associated with a better conative image.
The results also confirm that a more positive overall image of Israel as a tourist destination positively and significantly predicts the cognitive images of Galilee’s attractions, following in Jesus’ footsteps, European heritage sites, and themed hiking trails ( β ^ = 0.81 ,   p v < 0.001 ; β ^ = 0.79 ,   p v < 0.001 ; β ^ = 0.68 ,   p v < 0.001 ;   β ^ = 0.72 ,   p v < 0.001 , respectively), and that cognitive images of Galilee positively and significantly predict the conative image of Israel ( β ^ = 0.24 ,   p v < 0.001 : β ^ = 0.14 ,   p v < 0.001 ; β ^ = 0.19 ,   p v < 0.001 ; β ^ = 0.16 ,   p v < 0.001 , respectively).
The indirect effect of the overall image of Israel as a tourist destination is also statistically significant for all four models ( β ^ = 0.20 , with LL > 0 for confidence intervals of 99.9% confidence; β ^ = 0.11 , with LL > 0 for confidence intervals of 99.9% confidence;   β ^ = 0.13 , with LL > 0 for confidence intervals of 99.9% confidence;   β ^ = 0.09 , with LL > 0 for confidence intervals of 99.9% confidence, respectively), confirming that they mediate the relationship between the overall image and the conative image, thus supporting Hypothesis 1.

4.2. Hypotheses 2a and 2b: Associations Between Cognitive and Conative Images and Sociodemographic Variables

As seen in Table 3, which presents associations between the study variables, the conative image of Israel was positively and significantly associated with the overall and cognitive images. Hence, Hypothesis 2a was supported. With regard to Hypothesis 2b, age had a weak, negative significant correlation with the conative image, whereas religion had a weak, positive significant association. Gender had no such influence on the conative image. Hypothesis 2b was partially supported.

4.3. Hypothesis 3: Changing Positions

As reflected in the two images in Figure 5, the link between the overall image of Israel as a tourist destination and the conative image of Israel was supported by the destination’s cognitive image, but primarily for the participants who found Israel appealing from the outset. Ninety percent of the participants with a positive overall image of Israel also held a positive conative image. Seventy-nine percent of the participants with a negative overall image of Israel maintained a negative conative image, though the cognitive sustainable image did not support it. The percentage of participants who changed their minds was therefore low.
Between the defined segments of negative and positive destination images of Israel, the large segment of participants who selected “somewhat appealing” (n = 270) was divided between participants who changed their opinion to a positive conative image (n = 183) and those who were not interested in visiting (n = 87) at a ratio of approximately 2:1.
It is worth noting that the model is based on robust estimation procedures. It gives more weight to matching pairs of observations in calculating the estimators, as they follow a quasi-linear relationship. The reverse effect—when participants who held a positive overall destination image changed their mind to adopt a negative conative image, and vice versa—could not be rigorously investigated within the framework of this article. Although this is not typical, it was not overly uncommon.
Fifteen percent of the participants changed their opinions negatively from a somewhat appealing/very appealing/absolutely appealing overall image to a negative conative image (will probably not/definitely not visit in the future) during the questionnaire. Together with the 13% of the participants who maintained a negative image, the cognitive destination image seems to have had a limited effect. Hence, the hypothesis was not rejected, but it raised serious doubts regarding the impact of the cognitive image and sustainable attributes on potential tourists with a less positive overall destination image of Israel.

5. Discussion

The study showcases how academics and practitioners interpret the same data differently, shaped by their respective goals and perspectives. It focuses on an international sample (n = 800) of potential tourists and their destination image of Israel and a rural sustainable destination—the Galilee region. It incorporates two perspectives—practitioners and researchers—and tries to bridge them using a mixed team, a commercial (lagged) dataset, a combined set of hypotheses, and rigorous analysis.
From an academic perspective, this study supports Hypothesis 1, as the region’s cognitive images fully mediate the country’s overall image and conative image among potential tourists; however, as the study dealt with the expectations of potential tourists, the hierarchical structure of the destination image model differs from those found in previous studies [24,25,26,27], because, without an emotional component, the cognitive image mediates overall and conative images.
This mediated model emphasizes matching pairs of observations and underestimates changes in position, as reflected in the descriptive analysis of Hypothesis 3. The latter finding raises theoretical doubts regarding the applicability of destination image models to tourists who have held a less positive overall destination image from the outset. Furthermore, it raises practical doubts regarding the effectiveness of cognitive images of rural destinations in shaping tourists’ intentions when these tourists have a less positive overall image or change their opinion.
From the perspective of practitioners, this study examines how different types of cognitive images of Galilee (attractions, the footsteps of Jesus, European history, and themed trails) support the conative image of Israel. Hypothesis 2a was supported, as all four types of cognitive images of Galilee reinforced the conative image of Israel. This finding follows previous results [28]. Hypothesis 2b was only partially supported, as age and religion segmentations were marginally significant. Gender was not found to have an effect. In sum, Hypotheses 2a and 2b indicate that more information regarding the attributes of a rural region, as reflected in the cognitive destination image, generally supports the destination’s conative image, regardless of the type of messages conveyed and the sociodemographic segmentation, as opposed to a previous study that highlighted sociodemographic segmentation [32].

6. Conclusions

6.1. Theoretical Contribution—The Stability of Destination Image

This study demonstrates how commercial and academic cooperation explores new knowledge, as destination image was found to be relatively fixed and constant, for better or for worse. Potential tourists tend to stick to their previous opinions, which are reinforced primarily by the mediating effect of the cognitive image. The change in opinion is limited primarily to undecided participants. Overall, pro- and anti-opinions remained relatively stable as messages have limited impacts. This finding challenges the assumption that destination image models are universally applicable and highlights their limited relevance in changing the opinions of skeptical tourists.
In this sense, the present study follows academic studies that indicated the importance of cognitive image to conative image [24,25,26,27,28] but limited it only to tourists who favor the destination. From the practitioners’/commercial perspective, one may conclude, as was found in H2a, that cognitive image always contributes to conative image, but the findings of H3 imply otherwise and indicate a possible change in positions.
A previous study also found changes in destination images to be rare [54]. In this sense, the concepts of “destination image” and “place attachment” are similar. Place attachment represents individuals’ emotional bond to places [55]. This idea is derived from “attachment theory” in psychology, which perceives a person’s attachment style to others as relatively stable across their life span [56]. Similarly, place attachment affects the tourist experience but remains relatively stable in response to ongoing events and experiences [57].
Here, the great divide between practitioners and academics helped overcome the partial knowledge that overlooks the relative stability of destination image and blurred potential similarities between place attachment and destination image concepts. When each group (academics and practitioners) separately tried to portray how the destination image was formed due to persuasive messages or different conditions, they missed the complete picture of stability and change in the process and wholly ignored individuals with negative images. Hence, there is a need for new theoretical models that address stability versus change in tourist decision-making.

6.2. Practical Contribution—Bridges Between Academics and Practitioners

The present case study of destination image applied the three bridges between tourism researchers and practitioners, as suggested by previous studies [13,14,16,18,19]: the transfer of knowledge between the industry and research centers, data-sharing, and a big-picture perspective. Four steps were used to implement these bridges: the use of a mixed team, re-examination of commercial (lagged) datasets, developing a combined set of hypotheses, and conducting rigorous analysis. Without these bridges and steps, the findings would have remained in the separated domains of relevance and rigor, as practitioners focused on cognitive image’s impact on conative image across different segments and messages, and academics studied the hierarchical structure of the concept of destination image. Only cooperation between academics and practitioners can contribute to new knowledge regarding the stability of destination images and the limited coverage of known models, which refer mainly to people with positive images from the beginning. It is significantly important in rural tourism, as understanding its destination image can support a sustainable competitive advantage [31].

6.3. Managerial Implications—Better Delivery of Destination Image

To better incorporate the concept and models of destination image, practitioners are advised to target potential tourists who may change their opinion. Investing in messages to potential tourists with a positive overall image can be a waste of effort, as they already tend to have a positive conative image and are more ready to purchase. Investing in potential tourists with a negative overall image is generally a waste of effort, as they do not tend to change their minds. Focusing on cognitive images and persuasive messages for undecided tourists or tourists with a slightly favorable view would probably be the most cost-efficient approach and support a sustainable competitive advantage.
To support the market, academics should focus on conative destination image, develop segmentation tools to identify the target groups based on their overall destination image, and build dynamic destination image models that portray the differences between the groups and conditions. The models should be accompanied by statistical procedures focusing on changes instead of covariance to better reflect varying tourist perceptions and target messaging.
The most important bridge to the new knowledge regarding destination image was the joint formulation of research hypotheses. Taking this principle beyond the destination-image discussion, tourism practitioners and academics should collaborate in teams. Formulating research questions and hypotheses together is essential for enhancing the relevance and rigor of sustainable tourism studies.

6.4. Limitations

This study has two limitations. The first type stems from its specific location in the Galilee region of Israel. Israel elicits emotional responses from people worldwide [58], and therefore insights regarding its destination image may not be generalized to other places and destinations. This limitation became even more profound during the 2023–2025 Gaza War, even though this study was conducted earlier.
The second limitation stems from the cooperation between practitioners and researchers. Although it is recommended to create new knowledge and to overcome challenges of relevance and rigor, this cooperation also results in compromises and drawbacks. Using an existing lagged database [19] requires employing variables and measures not fully aligned with theories or statistical conventions. More specifically
  • The measurement scales were modified from seven levels to four levels through the commercial survey. In general, academics do not recommend this change.
  • Participants were not asked about their emotional destination image, even if they had reported having visited Israel and Galilee.
  • The questionnaire used for this study was lengthy, and the fatigue effect may have influenced the responses.
  • The first question was used as a filter question; participants who stated that they were absolutely not interested in Israel were removed from the sample, leaving a truncated distribution and a limited span of variance.
  • The overall and conative images refer to Israel, whereas the informative image pertains to the Galilee region.
  • The data are several years old, which may create a relevance problem, especially in light of the 2023–2025 Gaza War.
The rigorous analysis [21] of this study seeks to address all of these challenges except for the last.

6.5. Directions for Future Studies

Future research should overcome the above limitations. Moreover, they can also seek to address the following issues:
  • Actual behavior—Whereas the present study focuses on conative image, the known gap between intentions and behaviors will require future studies to extend their investigation to include purchases and actual behavior [59].
  • Destination image and place attachment—Given the similarity between the concepts, future studies may focus on their convergent validity. Indeed, their interrelations have already been studied [60,61] not as constructs with interfacing aspects but as antecedents of one another.
  • Sustainability and research–practice cooperation—This study sheds light on the relationship between collaboration and sustainability. Future studies may continue in this stream to explore other topics in tourism.

7. Final Remarks

The present study addresses the academic–practitioner divide in tourism. Focusing on cooperation regarding a rural destination image case study, a mixed team enabled the portrayal of practical bridges between researchers and practitioners. This study advances theoretical understanding and practical strategy by showing that cognitive marketing messages may reinforce existing views but rarely overturn them. Notably, the cooperation in this study demonstrated how models of the destination image are aimed at individuals who already hold relatively positive images. Future projects should focus on models that consider changes in opinions and actual behaviors and incorporate researchers and practitioners who share data and develop joint research questions and hypotheses.

Author Contributions

Conceptualization—Y.R. and S.S. Writing—Original Draft—Y.R. Writing—Review and Editing—Y.R., S.S., L.G. and N.C.-K. Visualization—Y.R. and L.G. Data curation—S.S. Formal Analysis—L.G. Methodology—L.G. Resources—N.C.-K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Not applicable. This research did not involve the active participation of human subjects.

Data Availability Statement

The original data presented in the study are openly available at Ram, Yael (2024), “Dataset for Galilee research”, Mendeley Data, V1, doi: 10.17632/xhk4hcpnzh.1. https://data.mendeley.com/datasets/xhk4hcpnzh/1 (accessed on 20 May 2025).

Acknowledgments

Approval of the Ethics Committee for Human Experiments, University of Haifa: (160/25). No AI was used during the preparation of this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 05330 g001
Figure 1. “Destination image” publications. Data source: Web of Science (May 2025).
Figure 1. “Destination image” publications. Data source: Web of Science (May 2025).
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Figure 2. Rationale and hypotheses of the study.
Figure 2. Rationale and hypotheses of the study.
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Figure 3. A map of the Galilee region (source: authors).
Figure 3. A map of the Galilee region (source: authors).
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Figure 4. A visual diagram of the mediated relationships (Hypothesis 1). ** p < 0.001, *** p < 0.0001.
Figure 4. A visual diagram of the mediated relationships (Hypothesis 1). ** p < 0.001, *** p < 0.0001.
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Figure 5. Two demonstrations of changing positions.
Figure 5. Two demonstrations of changing positions.
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Table 1. Descriptive statistics by country of origin.
Table 1. Descriptive statistics by country of origin.
FranceGermanyItalyUKUS 1 (Evangelicals)US 1
(Jews)
N sample200200200200200200
Income
Above average59 (29.5%)61 (30.5%)38 (19%)64 (32%)107 (53.5%)112 (56%)
Average99 (49.5%)107 (53.5%)117 (58.5%)100 (50%)66 (33%)65 (32.5%)
Below average42 (21%)32 (16%)45 (22.5%)36 (18%)27 (13.5%)23 (11.5%)
Gender
Men106 (53%)103 (51.5%)102 (51%)104 (52%)101 (50.5%)95 (47.5%)
Women94 (47%)97 (48.5%)98 (49%)96 (48%)99 (49.5%)105 (52.5%)
Identifies as Christian
No85 (42.5%)68 (34%)41 (20.5%)67 (33.5%)0 (0%)200 (100%)
Yes115 (57.5%)132 (66%)159 (79.5%)133 (66.5%)200 (100%)0 (0%)
Traveled abroad in the past 5 years
No24 (12%)35 (17.5%)31 (15.5%)22 (11%)29 (14.5%)20 (10%)
Yes176 (88%)165 (82.5%)169 (84.5%)178 (89%)171 (85.5%)180 (90%)
Visited Israel in the past 10 years
No168 (84%)156 (78%)177 (88.5%)172 (86%)161 (80.5%)112 (56%)
Yes32 (16%)44 (22%)23 (11.5%)28 (14%)39 (19.5%)88 (44%)
Conative image—Israel’s future visit
Definitely yes40 (20%)37 (18.5%)54 (27%)31 (15.5%)87 (43.5%)89 (44.5%)
Probably yes102 (51%)92 (46%)109 (54.5%)103 (51.5%)79 (39.5%)77 (38.5%)
Probably not48 (24%)57 (28.5%)30 (15%)54 (27%)29 (14.5%)32 (16%)
Definitely not10 (5%)14 (7%)7 (3.5%)12 (6%)5 (2.5%)2 (1%)
Age45.23 (13.72)44.77 (14.13)43.79 (13)46.1 (13.52)46.38 (12.95)47.66 (12.96)
Overall image of Israel as a tourist destination4.86 (1.45)4.8 (1.45)4.96 (1.52)4.47 (1.41)5.78 (1.4)5.92 (1.39)
Cognitive image—Galilee’s attractions4.76 (1.45)4.86 (1.45)5.16 (1.52)4.47 (1.41)5.86 (1.4)5.34 (1.39)
Cognitive image—The Jesus’ Step Trail4.4 (1.8)4.5 (1.82)4.84 (1.7)4.12 (1.79)5.73 (1.51)3.66 (2.1)
Cognitive image—Galilee’s influence on European heritage4.8 (1.54)4.82 (1.63)5.31 (1.43)4.54 (1.58)--
Cognitive image—Hiking trails4.66 (1.56)4.79 (1.57)5.21 (1.5)4.5 (1.62)5.51 (1.5)4.43 (1.88)
1 Disregarded by the analysis.
Table 2. Cognitive images as mediators.
Table 2. Cognitive images as mediators.
Model 1Model 2Model 3Model 4
Cognitive Image—AttractionsConative Image †Cognitive Image—Jesus TrailConative Image † Cognitive Image—European HistoryConative Image †Cognitive Image—Themed TrailsConative Image
Total effects β ^ S E β ^ S E β ^ S E β ^ S E β ^ S E β ^ S E β ^ S E β ^ S E
Overall image 0.35 ***
(0.02)		 0.36 ***
(0.02)		 0.36 ***
(0.02)		 0.35 ***
(0.01)
Direct effects
(Intercept)0.88 ***
(0.24)		1.28 ***
(0.13)		1.13 ***
(0.31)		1.27 ***
(0.14)		1.75 ***
(0.26)		1.1 ***
(0.14)		1.32 ***
(0.26)		1.25 ***
(0.14)
Overall image 0.81 ***
(0.03)		0.15 ***
(0.02)		0.79 ***
(0.04)		0.25 ***
(0.02)		0.68 ***
(0.03)		0.23 ***
(0.02)		0.72 ***
(0.03)		0.24 ***
(0.02)
Age0−0.01 ***
(0.001)		−0.01 **
(0.004)		0.(0)
−0.002
(0.003)		−0.004 **
(0.001)		0.001
(0.003)		−0.01 **
(0.001)
Gender (woman)0.07
(0.08)		−0.06
(0.05)		−0.12
(0.11)		−0.02
(0.05)		0.01
(0.09)		−0.03
(0.05)		0.01
(0.09)		−0.03
(0.05)
Christianity (Yes)0.01
(0.09)		0.04
(0.05)		0.39 **
(0.13)		−0.02
(0.05)		0.12
(0.11)		0.03
(0.05)		0.07
(0.1)		0.03
(0.05)
Cognitive images (models 1, 2, 3, 4)−0.24 ***
(0.02)		 0.14 ***
(0.02)		 0.19 ***
(0.02)		 0.16 ***
(0.02)
R 2   ‡0.610.510.500.480.460.480.480.46
Indirect Effects
overall > cognitive > conative 0.20
[0.16,0.24]		 0.11
[0.08,0.14]		 0.13
[0.10,0.16]		 0.09
[0.06,0.11]
** p < 0.001, *** p < 0.0001; † Jittered values. ‡ Consistency-corrected robust coefficient of determination by Renaud and Victoria-Feser (2010).
Table 3. Association between study variables (n = 800).
Table 3. Association between study variables (n = 800).
12345678
1. Conative image—Future visit to Israel−
2. Overall image of Israel 0.49 ***−
3. Cognitive image—Galilee attractions0.52 ***0.66 ***−
4. Cognitive image—The Jesus Trail0.45 ***0.54 ***0.68 ***−
5. Cognitive image—Galilee’s influence on European heritage0.46 ***0.58 ***0.71 ***0.74 ***−
6. Cognitive image—Themed trails0.45 ***0.57 ***0.7 ***0.72 ***0.76 ***−
7. Gender−0.06−0.08 *−0.02−0.06−0.05−0.05−
8. Age−0.13 ***−0.06−0.050.014 ***−0.06−0.05−0.15 ***−
9. Identifies as belonging to the Christian faith0.08 *0.08 *0.050.19 ***0.1 **0.13 ***0.08 *0.05
* p < 0.05. ** p < 0.01. *** p < 0.001. NOTE: Kendall’s   τ B , Kendall’s τ C , Point Biserial, and φ coefficients served as a measure of association where Pearson correlation was inapplicable.
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Ram, Y.; Shilo, S.; Gafter, L.; Collins-Kreiner, N. Academic–Practical Cooperation: A Case Study of Rural Destination Image. Sustainability 2025, 17, 5330. https://doi.org/10.3390/su17125330

AMA Style

Ram Y, Shilo S, Gafter L, Collins-Kreiner N. Academic–Practical Cooperation: A Case Study of Rural Destination Image. Sustainability. 2025; 17(12):5330. https://doi.org/10.3390/su17125330

Chicago/Turabian Style

Ram, Yael, Shahar Shilo, Lee Gafter, and Noga Collins-Kreiner. 2025. "Academic–Practical Cooperation: A Case Study of Rural Destination Image" Sustainability 17, no. 12: 5330. https://doi.org/10.3390/su17125330

APA Style

Ram, Y., Shilo, S., Gafter, L., & Collins-Kreiner, N. (2025). Academic–Practical Cooperation: A Case Study of Rural Destination Image. Sustainability, 17(12), 5330. https://doi.org/10.3390/su17125330

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