Segmenting Agritourism Visitors: Moving Beyond the General Market

Abstract

Agritourism has emerged as a dynamic and rapidly expanding sector, drawing a diverse range of visitors. Despite its increasing popularity, many agritourism enterprises continue to adopt a generalized marketing approach that assumes a homogeneous audience. This underscores the critical need for visitor segmentation, and to address this gap, the present study segments agritourism visitors based on their enduring involvement, with an emphasis on intrinsic motivations and environmental behaviors. We collected data through an online survey of 550 agritourism visitors, and statistical analysis identified three distinct segments: Agritourism Lovers, Greenies, and Neophytes. “Agritourism Lovers” represent devoted advocates who seek meaningful connections with agriculture, natural attractions, and local foods. “Greenies” exhibit strong environmental consciousness, emphasizing agritourism’s role in promoting sustainable agricultural practices. “Neophytes”, on the other hand, are newcomers who value the learning opportunities and recreational experiences associated with agritourism. The findings of this study provide valuable insights for agritourism operators, enabling the development of tailored experiences and marketing strategies aimed at maximizing visitor satisfaction and enhancing the overall value of agritourism.

1. Introduction

In the last few decades, agricultural operations have undergone significant transformations around the world driven by factors such as farm sustainability, the development of value-added farming operations, and motivations to generate additional revenue through farm establishments [1,2,3]. Farm diversification, which includes the incorporation of recreational and leisure activities into farming operations, offers both intrinsic and economic benefits to farmers/ranchers, visitors, and rural communities [4]. The instability of farming incomes and the need to diversify revenue sources have highlighted the growing importance of agritourism as an alternative economic opportunity for farmers and ranchers. Agritourism, a dynamic and multifaceted segment of the tourism industry, has garnered significant attention in recent years due to its potential to foster sustainable development, stimulate local economies, and promote cultural exchange [1,2]. As an attractive blend of agriculture and tourism, agritourism offers visitors the opportunity to engage in authentic, hands-on experiences that connect them with rural life, agricultural practices, and the natural environment. As a widely recognized developmental strategy for rural communities and minority farmers, agritourism has garnered attention from farmers’ organizations, policymakers, and state governments [5].

Agritourism, defined as the diversification of farms or ranches to accommodate visitors’ recreational activities, has gained widespread adoption due to its direct and indirect economic benefits for farm or ranch owners, their families, and rural communities [6]. Traditionally, agritourism has been understood as the act of touring or visiting a farm or ranch to gain insights into farming operations and participate in farm-related activities [7]. In modern terms, agritourism encompasses visiting a working farm or any agricultural setting to experience farm life, entertainment, education, socialization, and the purchase of farm products, as well as engage in various other activities [8]. Numerous studies have investigated the various dimensions of agritourism [9]. For visitors, farm visits offer a unique opportunity to gain a deeper understanding of farming and related activities, often reshaping their perceptions of farms and farming operations. Such visits highlight the enjoyable aspects of farm life, contributing to the rising popularity of the “farms and fun” concept as a key demand driver [10]. Notably, the demand for agritourism and related activities has been growing among suburban and urban populations.

In the past decade, agritourism has experienced significant growth among farmers and ranch owners in the United States. Agritourism has gained widespread acceptance and provides farm and ranch owners with an additional income source through the incorporation of agritourism services [11,12]. Small-sized and mid-sized countries have explored agritourism opportunities to remain competitive in the agricultural sector. In addition to traditional agritourism operations, community-based agritourism operations are becoming increasingly important, as they create more employment opportunities for residents. The development of rural areas positively impacts employment opportunities, standards of living, and community networks. According to the USDA Census of Agriculture, the economic impact of agritourism has shown a substantial increase, with total agritourism revenue rising from USD 567 million in 2007 to USD 1.260 billion in 2022, and revenue per farm increasing from USD 24,276 in 2007 to USD 44,004 in 2022.

1.1. Segmenting Agritourism Visitors

Segmentation involves partitioning heterogeneous markets into smaller, more homogeneous market segments with interesting needs, characteristics, or behavior [13]. In recreation and tourism research, segmentation of the potential market is an effective method for understanding tourism markets, especially emerging markets on which little knowledge has been developed [14,15,16]. While reviewing previous studies and literature associated with recreation and tourism, it was identified that posterior data-driven segmentation and priori common-sense-based segmentation are commonly used. However, it is not uncommon to see studies incorporate both approaches in their analyses, typically using a data-driven approach to identify segments first and then using priori criteria (e.g., demographics, trip characteristics) to describe the segments more clearly [14,16,17].

Considering the agritourism sector, agritourism visitors constitute the potential market from which the sector generates demand [18]. Although the segmentation of agritourism visitors offers an enlightened way to better comprehend this significant tourism market and acknowledge its heterogeneous nature, only a few studies have resorted to visitor segmentation to understand agritourism visitors’ behavior. In consideration of this, we initiated this study to segment agritourism visitors based on their enduring involvement and to suggest both theoretical and practical ideas to enhance the profitability of the agritourism sector. The motive behind the segmentation of agritourism visitors is to better understand their motivations and behavior, enabling various agritourism organizations and DMOs to frame promotions and marketing policies that would help in the sustainable development of the sector.

1.2. Enduring Involvement and Agritourism Segmentation

Enduring involvement has been a focus of tourism research in recent years. Enduring involvement can be defined as an individual difference variable representing the arousal potential of a place, service, or activity that fosters personal relevance [19,20]. It represents an individual’s persistent attitude toward the object, which is relatively durable over time [21]. The concept of enduring involvement has tremendous significance when applied to niche market segments like agritourism, which has a high level of participation-based activities for visitors relating to their enduring involvement. While there are no known studies that have examined agritourism visitors’ enduring involvement, it can be argued that enduring involvement would be highly relevant to the DMOs and policymakers related to agritourism destinations, as this concept determines a tourist’s preferences, motivations, supportive behavior, and potential future intentions, which facilitates marketing and managing agritourism tourism offerings. The current study will segment agritourism visitors based on their enduring involvement aligned with intrinsic motivation and environmental behavior.

Intrinsic motivational factors play a significant role in driving visitors to agritourism destinations. Previous research has identified several common motivational factors, including the desire to have farm experiences, participate in farm activities, seek entertainment, spend family time, gain education, purchase farm products, and socialize [1,6,7,8,22,23]. Agritourism visitors are drawn to farms or ranches to gain farm experiences, which include engaging in farm work, exploring various production facilities, understanding different types of operating farms, and learning about the seasonality of operations. These experiences are not only enjoyable but also educational [24], as they allow visitors to understand the significance of agritourism for farmers, its role in rural community development, and the connection between agriculture and tourism in the respective locality or state. These intrinsic motivations serve as the foundation for enduring involvement [25]. When individuals engage in agritourism activities that they find inherently enjoyable and fulfilling, they are more likely to develop a long-term interest and commitment to agritourism. This intrinsic enjoyment fosters a sense of personal relevance and connection, leading to enduring involvement.

Environmental behavior encompasses the actions and measures taken to avoid causing harm to the environment [26] and reflects a consistent commitment to environmental protection (e.g., purchasing local food, conserving water and energy, constructing off-grid homes). These environmentally friendly behaviors are often associated with visiting agritourism destinations and supporting agritourism services. Agritourism destinations increasingly attract visitors who prioritize environmental protection, possess environmentally friendly attitudes, and demonstrate positive environmental behaviors, which are crucial for promoting sustainable tourism [27,28]. Individuals who are intrinsically motivated to engage in environmentally friendly behavior often find personal fulfillment and satisfaction in these actions. They derive a sense of purpose and meaning from contributing to environmental conservation and sustainability.

2. Methods

The primary objective of this study was to segment agritourism visitors based on their enduring involvement, with a particular emphasis on intrinsic motivations and environmental behavior. The study was conducted in Missouri due to the state’s robust, heterogeneous agricultural sector and thriving tourism industry. In 2021, Missouri’s agricultural sector generated an economic impact of USD 93.7B, and in 2023, the tourism industry contributed an economic impact of USD 19B. Agritourism in Missouri synergistically combines the state’s two leading industries. Missouri agritourism serves as a vital complementary business for farmers, offering additional income and alleviating the financial uncertainties associated with traditional farming enterprises. Missouri stands as a leading state in the United States in successfully adopting the agritourism concept in conjunction with agricultural production [6].

To achieve this, the study employed an online survey method, utilizing a five-point Likert scale for each construct, ranging from “strongly disagree” to “strongly agree”. A pilot study was conducted to refine the research instrument, ensuring the validity and reliability of the measurement items prior to finalizing the questionnaire. The survey instrument was designed specifically to address the study’s objectives. The construct of agritourism visitors’ intrinsic motivations (IMs) was measured using seven items, adapted from previous studies examining the motivations of rural and agritourism travelers [9,18,29,30,31]. The items used to measure intrinsic motivation included the following: I visited the agritourism destination to rest and relax (IM1); my family wanted to see it (IM2); to spend time with my family (IM3); to share my experience with others (IM4); for a novel experience (IM5); to gain knowledge (IM6); and to have fun/enjoy/playfulness (IM7). The construct of agritourism visitors’ environmental behavior (EB) was assessed using five measurement items, which were adapted from studies that examined rural travelers’ environmental behavior and attitudes [32,33,34,35,36]. The items used to measure environmental behavior included the following: supports and helps local farmers (EB1), environmentally friendly (EB2), supports the local economy (EB3), brings the community together (EB4), and supports sustainable agriculture (EB5).

The population for this study was defined as individuals aged 18 years and older who had visited agritourism destinations in Missouri since 2020. To collect data, the study employed the online survey company Qualtrics, which partnered with Survey Sampling International (SSI) to obtain representative samples. SSI recruits’ participants from multiple panels using various sourcing methods and channels, thereby employing a broad sample frame to minimize coverage bias and mitigate the limitations associated with convenience sampling from pre-existing online surveys. This approach ensures a more accurate representation of the population under study. To ensure eligibility, screening questions were included at the beginning of the questionnaire. The required minimum sample size for this study was determined using the a priori sample size calculator [37], which recommended a minimum of 425 samples for a 95% confidence level and a 5% margin of error with a population of 10 million. 690 online responses were recorded in the primary stage of data collection. After applying the necessary screening conditions, eligibility requirements, and response rate checks, a total of 550 samples were selected for the segmentation analysis. These 550 samples exhibited the most accurate representation of the targeted population cluster, meeting the established standard levels. Data analysis was conducted in multiple stages, beginning with frequency analysis, followed by factor analysis, cluster analysis, discriminant analysis, and chi-square analysis. The reliability of the constructs used in the study was assessed, with the overall reliability exceeding the accepted threshold of 0.70.

3. Results

3.1. Demographics Profile

The demographic characteristics of the respondents are presented in

Table 1. Respondents’ socio-demographic profile.

Variable		Frequency	Percentage
Gender (n = 550)
	Male	261	47.5%
	Female	289	52.5%
Annual Income (n = 550)
	Less than USD 20,000	66	12.0%
	USD 20,000–USD 34,999	79	14.4%
	USD 35,000–USD 49,999	59	10.7%
	USD 50,000–USD 74,999	92	16.7%
	USD 75,000–USD 99,999	77	14.0%
	USD 100,000–USD 149,999	96	17.5%
	USD 150,000 or more	81	14.7%
Education (n = 550)
	High School Graduate	114	20.7%
	Some College, No Degree	119	21.6%
	Associate Degree	63	11.5%
	Bachelor’s Degree	150	27.3%
	Graduate or Professional Degree	104	18.9%
Race/Ethnicity (n = 550)
	African American or Black	65	11.8%
	Caucasian or White	427	77.6%
	Asian or Pacific Islander	24	4.2%
	Others	34	6.2%

. More than half of the respondents were female (52.5%), while the remaining respondents were male (47.5%). Regarding annual income, 37.1% of the respondents earned USD 49,999 or less, and 32.2% of the respondents earned USD 100,000 or more. In terms of education level, 42.3% of the respondents had a high school diploma or some college experience. On the other hand, 57.7% of the respondents held an associate’s, bachelor’s, or graduate degree. Regarding the race/ethnicity profile of the respondents, 77.6% identified as Caucasian or White, and 11.8% identified as African American or Black.

3.2. Factor Analysis

Exploratory factor analysis (EFA) using principal component analysis as the extraction method and varimax with Kaiser normalization as the rotation method was conducted to determine the underlying determinants of enduring involvement as perceived by agritourism visitors. The Kaiser–Meyer–Olkin measure of sampling adequacy was 0.869, indicating that the sample size was adequate to proceed with the factor analysis results. Bartlett’s test of sphericity indicated that the chi square test was significant at the 0.1% level (χ² (66) = 4269.36, p < 0.001), which indicated that the factors extracted from the factor analysis were not uncorrelated. Two factors were extracted from the EFA conducted on the items of enduring involvement, which are included in

Table 2. Exploratory factor analysis of enduring involvement.

Items	Mean	Standard Deviation	Factor Loading	Eigenvalue	Variance Explained (%)
Intrinsic Motivation (Cronbach alpha = 0.903)				5.74	47.85
IM1	3.94	0.9758	0.85
IM2	4.03	0.9831	0.77
IM3	4.05	0.9462	0.80
IM4	3.92	0.9313	0.73
IM5	3.86	0.9692	0.75
IM6	3.87	1.0683	0.81
IM7	4.14	0.9288	0.71
Environmental Behavior (Cronbach alpha = 0.890)				2.19	18.26
EB1	4.20	0.8254	0.82
EB2	4.17	0.8246	0.80
EB3	4.27	0.8082	0.83
EB4	4.11	0.8819	0.77
EB5	4.16	0.8535	0.84

Mean values measured on a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree).

. The first factor, named intrinsic motivation (IM), had seven items, with an eigenvalue of 5.74 and 47.85% variance explained in enduring involvement. The second factor, environmental behavior (EB), had five items, with an eigenvalue of 2.14 and 18.26% variance explained in enduring involvement. The two factors, IM and EB, explained 66.11% of the variance in enduring involvement. The factor loading of all items in both factors were higher than the acceptable threshold of 0.70, which indicated that all items fell optimally under the right factor. Reliability was also tested using Cronbach’s alpha; IM had a Cronbach’s alpha value of 0.903, indicating excellent reliability, while the factor EB had a Cronbach’s alpha value of 0.890, indicating good reliability. The standard deviation values indicated that variability in responses or variances was higher in IM than in EB.

3.3. Cluster Analysis

Two-step cluster analysis with Schwarz’s Bayesian criterion (BIC) was conducted on enduring involvement, since the number of clusters to be determined was unknown in this scenario. Three clusters were determined (

Table 3. Results of cluster analysis based on enduring involvement.

Items	Cluster 1: Agritourism Lovers (n = 220)	Cluster 2: Greenies (n = 225)	Cluster 3: Neophytes (n = 105)	F-Ratio	p-Value
IM1	4.49	4.11	2.89	204.66	<0.001
IM2	4.55	4.08	2.93	146.14	<0.001
IM3	4.37	3.94	2.90	168.26	<0.001
IM4	4.35	3.86	2.80	130.02	<0.001
IM5	4.35	4.04	2.49	136.62	<0.001
IM6	4.60	4.12	3.29	193.17	<0.001
IM7	4.80	3.96	3.44	96.54	<0.001
EB1	4.76	3.92	3.46	191.12	<0.001
EB2	4.89	4.04	3.47	170.36	<0.001
EB3	4.73	3.90	3.28	227.33	<0.001
EB4	4.80	3.88	3.41	174.86	<0.001
EB5	4.49	4.11	2.89	194.76	<0.001

Note: Mean values on a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree).

), which had a sample size of 220, 225, and 105. The ratio of the largest cluster size to the smallest cluster size was computed to be 2.14, which was less than the threshold level of 3. The silhouette measure of cohesion and separation was calculated and indicated “fair” cluster quality. Cluster 1, named Agritourism Lovers, comprised 220 respondents and proportioned around 40% of the total sample size. This cluster included all respondents who strongly agreed with the IM and EB statements, as all mean scores ranged between 4.35 and 4.89 points. This group of visitors are hardcore followers and supporters of the agriculture sector and would always love to have a relationship with agriculture, natural attractions, and local foods [38,39]. Cluster 2, named Greenies, comprised 225 respondents and proportioned around 41% of the total sample size. This cluster included all respondents who somehow agreed with the IM and EB statements, as all mean scores ranged between 3.86 and 4.11 points. This group of visitors is more environmentally supportive and believes that agritourism is environmentally friendly and helps to promote sustainable agriculture [33,40,41].

Lastly, cluster 3, named Neophytes, comprised 105 respondents and proportioned around 19% of the total sample size. This cluster included all respondents who showed disagreement or were neutral with respect to the statements of IM and EB, as the mean scores ranged between 2.49 and 3.47 points. This group of visitors are beginners, always give importance to the learning experiences associated with the sector and are interested in agritourism and related recreational tourism experiences [42,43]. Interestingly, if collectively reviewed, Cluster 1—Agritourism Lovers—had the highest scores (4.35–4.89), Cluster 2—Greenies—had mediocre scores (3.86–4.11), and Cluster 3—Neophytes—had the lowest scores (2.49–3.47) on a 5-point Likert scale for all statements of IM and EB. One-way ANOVA was conducted to analyze the differences in the scores of the IM and EB statements across the three clusters. The results indicated that all statements scored significantly differently among the three clusters, which indicated that all three clusters have distinctive characteristics regarding enduring involvement.

3.4. Discriminant Analysis

Discriminant analysis was conducted to validate the significant differences between the clusters included in

Table 4. Discriminant analysis.

Function	Eigenvalue	Variance Explained	Canonical Correlation
1	3.090	96.2	0.869
2	0.121	3.8	0.328
Function	Wilks’ Lambda	χ2	p-value
1 through 2	0.218	831.952	<0.001
2	0.892	62.244	<0.001
Discriminant Loading		Function 1	Function 2
IM		0.759	0.676
EB		0.800	−0.627

Results: 92.2% of original grouped cases correctly classified; 90.9% of cross-validated grouped cases correctly classified.

. The first two discriminant functions were used in this analysis, and the results indicated that the first discriminant function explained 96.2% of the variances, with a very high canonical correlation of 0.869, and the second discriminant function explained 3.8% of the variances, with an acceptable canonical correlation of 0.328 (i.e., higher than the threshold level of 0.30). Moreover, both discriminant functions were significant at the 0.1% level (function 1 through 2: χ² (4) = 831, p < 0.001; function 2: χ² (1) = 62.244, p < 0.001). Furthermore, the classification matrix identified that 92.2% of the originally grouped cases were correctly classified, while 90.9% of the cross-validated cases were classified at a 0.1% significance level. Moreover, the structure matrix indicated that both IM and EB had the highest correlation (0.787 and −0.747) with the discriminant function 2, which is included in

Table 5. Summary of structure matrix.

	Function 1	Function 2
IM	0.617	0.787 a
EB	0.665	−0.747 a

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant function. Variables ordered by the absolute size of correlation within the function. ^a Largest absolute correlation between each variable and any discriminant function.

. Furthermore, the unstandardized canonical discriminant functions evaluated at group means were different across all functions, which further indicated that sufficient reliability and validity of the clusters were achieved, as shown in

Table 6. Functions at group centroids.

	Function 1	Function 2
Cluster 1	1.789	−0.235
Cluster 2	−0.337	0.411
Cluster 3	−3.025	−0.389

Note: Unstandardized canonical discriminant functions evaluated at group means.

.

3.5. Examining Differences in Demographics Between the Three Clusters

A chi-square test of independence was conducted to examine whether the three clusters were statistically significantly different in their demographic characteristics, which is included in

Table 7. Chi-square test results with demographics.

	Cluster 1: Agritourism Lovers (n = 220)	Cluster 2: Greenies (n = 225)	Cluster 3: Neophytes (n = 105)	χ2	p
Gender (n = 550)
Male	106 (48.2%)	111 (49.3%)	44 (41.9%)	1.66	0.436
Female	114 (51.8%)	114 (50.7%)	61 (58.1%)
Age (n = 550)
18–24 years	12 (5.5%)	20 (8.9%)	21 (20.0%)	37.98 ***	<0.001
25–34 years	37 (16.8%)	35 (15.6%)	22 (21.0%)
35–44 years	62 (28.2%)	62 (27.6%)	24 (22.9%)
45–54 years	31 (14.1%)	22 (9.8%)	5 (4.8%)
55–64 years	56 (25.5%)	38 (16.9%)	19 (18.1%)
65+ years	22 (10.0%)	48 (21.3%)	14 (13.2%)
Race/Ethnicity (n = 550)
African American or Black	23 (10.5%)	23 (10.2%)	19 (18.1%)	9.42	0.151
Caucasian or White	179 (81.4%)	174 (77.3%)	74 (70.5%)
Asian or Pacific Islander	9 (4.1%)	9 (4.0%)	6 (5.7%)
Others	9 (4.1%)	19 (8.4%)	6 (5.7%)
Education (n = 550)
High school graduate	38 (17.3%)	52 (23.1%)	24 (22.9%)	8.36	0.399
Some college, no degree	44 (20.0%)	48 (21.3%)	27 (25.7%)
Associate degree	25 (11.4%)	23 (10.2%)	15 (14.3%)
Bachelor’s degree	63 (28.6%)	63 (28.0%)	24 (22.9%)
Graduate or professional	50 (22.7%)	39 (17.3%)	15 (14.3%)
Annual Income (n = 550)
Less than USD 20,000	26 (11.8%)	23 (10.2%)	17 (16.2%)	25.83 *	0.011
USD 20,000–USD 34,999	28 (12.7%)	30 (13.3%)	21 (20.0%)
USD 35,000–USD 49,999	21 (9.5%)	24 (10.7%)	14 (13.3%)
USD 50,000–USD 74,999	29 (13.2%)	49 (21.8%)	14 (13.3%)
USD 75,000–USD 99,999	31 (14.1%)	33 (14.7%)	13 (12.4%)
USD 100,000–USD 149,999	37 (16.8%)	40 (17.8%)	19 (18.1%)
USD 150,000 or more	48 (21.8%)	26 (11.6%)	7 (6.7%)

Note: *** p < 0.001, ** p < 0.01, * p < 0.05.

. The percentage distribution of the respondents within each cluster is also presented. The results indicated that only age and income were significantly different across the three clusters (age: χ² (10) = 37.98, p < 0.001; annual income: χ² (12) = 25.83, p < 0.05). From the distribution of the respondents within categories and clusters, it can be observed that around 50% of the respondents in the first cluster had an age of 35–44 years and 55–64 years; in the second cluster, around 50% of the respondents had an age of 35–44 years and above 65 years; while in the third cluster, around 64% of the respondents had an age of fewer than 44 years. In terms of income, around 40% of the respondents had an annual income higher than USD 100,000 in the first cluster; in the second cluster, around 40% of the respondents had an annual income of either USD 50,000 to USD 74,999 or USD 100,000 to USD 149,999; while in the third cluster, around 50% the respondents had an annual income of less than USD 49,999. Interestingly, the three clusters were not significantly different in terms of gender (χ² (2) = 1.66, p = 0.436), race and ethnicity (χ² (6) = 9.42, p = 0.15), and education (χ² (8) = 8.36, p = 0.399). Hence, gender, race, ethnicity, and education could not be included in cluster profiling and segmentation.

4. Discussions

The segmentation of agritourism visitors plays a crucial role in enhancing the understanding of visitors’ preferences, enabling tailored experiences that meet the diverse needs of different visitor types. This study segmented visitors based on enduring involvement, with consideration for IM and EB. First, some interesting conclusions can be drawn concerning the differences between the three identified segments. The levels of enduring involvement regarding IM and EB demonstrated both differences and similarities across the segments. For instance, with respect to IM, the most preferred motivation for both Agritourism Lovers and Neophytes appeared to be to visit agritourism destinations for the purposes of fun, enjoyment, and playfulness. These findings are consistent with previous research and align with the results of prior studies [12]. In contrast, the primary motivation for Greenies in visiting agritourism destinations was found to be to gain knowledge, particularly with a focus on sustainable practices related to agriculture and tourism [44]. Additionally, differences and similarities were observed among the segments concerning EB. Agritourism Lovers and Neophytes exhibited a preference for agritourism destinations because they perceive them as being more environmentally friendly. However, Neophytes specifically favor destinations that are more sustainable. These findings corroborate previous research and are consistent with the conclusions drawn in earlier studies [45].

Although the segments did not differ in aspects like gender, race, and education, significant differences were found regarding age and income. Agritourism Lovers, who are devoted supporters of agritourism, seeking ongoing connections with agriculture, natural attractions, and local foods, were primarily aged between 35 and 44 years and between 55 and 64 years, with an annual income exceeding USD 100,000. Most Greenies, who are characterized by strong environmental support and view agritourism as a vehicle for promoting sustainable agricultural practices and environmental friendliness, were in the age brackets of 35–44 years and above 65 years, with annual incomes ranging from USD 50,000 to USD 74,999 or from USD 100,000 to USD 149,999. Finally, Neophytes, who represent newcomers to agritourism and place a high value on the learning experiences offered by agritourism and related recreational activities, were typically younger than 44, with annual incomes of less than USD 49,999.

The major theoretical implication of this study lies in its contribution to the agritourism literature through the segmentation of visitors based on enduring involvement, specifically focusing on IM and EB. The utilization of this segmentation approach aligns with the theoretical direction in tourism research, whereby employing more complex segmentation criteria can significantly expand the existing body of literature in tourism research. This contribution is particularly noteworthy as it addresses a previously unexplored area, offering a deeper understanding through multifaceted analysis and providing valuable insights into the field [46]. By segmenting agritourism visitors, this study identified distinct groups with specific needs and motivations, thereby enhancing our comprehension of the heterogeneity within the market. The findings confirm the results of other studies, with a distinct pattern, further substantiating the theoretical relevance of this research.

As agritourism emerges as a prominent niche within the tourism sector, the findings of this study have significant managerial implications. The primary aspect of an effective marketing and promotion strategy involves accurately identifying the target market. The empirical results of this study substantiate the rationale for distinguishing between three segments based on enduring involvement. One key benefit of segmenting and profiling agritourism visitors lies in identifying visitor groups that offer higher value to destinations. A concentrated and careful segmentation, coupled with deep market knowledge, will enhance the effectiveness of advertising and promotional efforts for agritourism destinations. We recommend that agritourism destination owners, promoters, and marketers meticulously analyze visitor profiles to determine which segments best align with their strategic marketing objectives. This approach ensures that promotional efforts resonate with the target audience, leading to higher engagement and conversion rates.

Additionally, we advocate for the development of a positioning strategy that addresses the motivations and environmental preferences of the three segments. Agritourism should prioritize opportunities to build and strengthen relationships with visitors, which necessitates the design of an appropriate marketing mix and the formulation of relevant policies. For example, promoting agritourism destinations as family- and group-oriented venues for fun and enjoyment (e.g., highlighting farm tours, on-farm recreational activities) can effectively target the Agritourism Lovers and Neophytes segments. Furthermore, enhancing visitor learning experiences (e.g., cheese pairing, winemaking) can attract visitors from the Greenies segment. We also suggest employing appropriate media and personalized communication messages for each targeted segment. For instance, Neophytes, typically under 44 years of age, can be reached through social media promotions (e.g., Facebook, Instagram) that emphasize fun and enjoyable activities at agritourism destinations.

The present study has several limitations yet offers valuable guidance for future research initiatives. Firstly, this study focused on agritourism visitors in Missouri, raising questions about whether similar segmentation results would be obtained if the same type of studies were conducted in other states or regions of the U.S. We recommend that future segmentation studies include a representative sampling from all regions to achieve a more accurate and comprehensive segmentation and profiling of agritourism visitors. It would be particularly interesting to investigate whether replicating this study in states where agriculture and tourism are the primary economic sectors, such as Florida, would yield similar results or exhibit notable variations. Additionally, we propose extending this research to countries and regions in Europe and Asia. Such an expansion could provide valuable insights into the similarities and differences across diverse cultural, economic, and geographical contexts, thereby enriching the broader understanding of agritourism dynamics.

Another limitation pertains to the sample size. Although we adhered to the priori sample size calculator in [37] and collected responses exceeding the suggested level, we propose that future research employs a larger sample, ideally surpassing 1000 responses. In conclusion, we assert that segmenting visitors through the lens of enduring involvement provides a robust basis for segmentation in the agritourism sector. This segmentation approach yields valuable market information for agritourism destinations and can serve as a crucial input for designing their targeting, positioning, and marketing mix strategies.