Immersive Storytelling Content and Innovation Resistance in Agritourism Marketing Context: Impact on Traveler Post-Experience Behavior

Abstract

Immersive technologies (IMTs) have significantly impacted the tourism sector by offering experiences that enhance engagement with destinations. Although previous research confirms that IMT affects travelers’ behavioral intentions, there is a lack of studies specifically focusing on the post-experience context of agritourism and the factors contributing to technological resistance. This study introduces a conceptual model that combines the Diffusion of Innovation framework, the technology acceptance model, and the psychological factors related to innovation resistance to examine the decision-making processes of IMT users in the post-experience context of agritourism. The research model is evaluated through partial least squares structural equation modeling (PLS-SEM) techniques involving 400 users who engaged with IMT for a duration not exceeding 3 months. The findings indicate that the amount of storytelling content, which enhances engagement in agritourism, significantly affects users’ perceptions of IMT and their intentions to revisit and continue using IMT. Additionally, factors related to compatibility, along with privacy and security risks, influence the reluctance or readiness to adopt IMT and the decision to revisit a destination. These findings contribute to the understanding necessary to develop content and apply IMT in the agritourism sector to promote long-term sustainability.

1. Introduction

Agritourism has emerged as a significant strategy for rural economic development and fostering sustainability within communities. A defining characteristic of agritourism is the amalgamation of conventional agricultural practices with tourism, enabling visitors to immerse themselves in rural life through diverse activities such as education, recreation, community interaction, and the acquisition of locally produced goods [1,2]. Agritourism activities are distinctive in that they must account for the agricultural season of each region, as the season directly influences the availability of diverse activities. Consequently, the design of tourism activities must be adaptable and aligned with the cadence of local production to suitably accommodate tourists. The involvement of diverse community sectors is a crucial component in the design of such activities. Local leaders, farmers, and agencies often help shape tourism activities to make sure that they do not disrupt the community. At the same time, these activities offer a way to share authentic cultural knowledge. Simultaneously, they can authentically communicate local cultural values and knowledge. This form of participation fosters a sense of ownership among residents and increases the potential for sustainable agritourism development [3].

Although agritourism has the potential to generate educational, recreational, and economic benefits, operators encounter numerous challenges in practice—specifically regarding the attraction of contemporary tourists, who possess elevated expectations concerning the nature of their experiences. Contemporary tourists increasingly desire interactive experiences that require active participation rather than mere observation and anticipate the integration of technology [4,5,6,7]. The design of tourism activities must evolve following shifting trends in tourist behavior to foster differentiation and sustained interest [8]. Consequently, it is essential to devise activities that foster engagement while utilizing contextually appropriate technology to augment economic value and enrich the experiences of tourists participating in agritourism.

The change in tourist behavior towards digital communication has created new opportunities for the agritourism sector to engage a broader audience by providing unique and valuable experiences in an interactive format [9]. IMTs have become essential to the digital transformation of the hospitality and tourism industry within agritourism, altering how individuals engage with both real and virtual environments [10,11]. Given the aforementioned advantages, an increasing number of destinations are integrating IMT within tourism sectors, including hotels, cultural heritage sites, and museums, as components of their marketing and promotional strategies [2,12,13,14,15]. IMT serves as a crucial marketing instrument for tourist experiences while also addressing users’ psychological, emotional, behavioral, sensory, and social responses throughout the decision-making process [16,17,18,19,20].

The tourism experience includes the activities conducted during a tourist’s journey, which can be classified into three phases: pre-trip, during-trip, and post-trip [21]. Previous studies have established that IMT enhances tourists’ experiences at every phase of their journey. The post-trip phase is characterized by IMT’s significant enhancement of their experiences, the promotion of repeat travel intentions, and motivation towards positive word-of-mouth about a destination [22,23,24]. Alongside the utilization of technology to develop immersive experiences, storytelling serves as a significant communication tool in the digital era. Destinations can enhance their content through narrative techniques and the integration of IMT, thereby creating an emotional connection between tourists and destinations. The findings indicate a beneficial impact on destinations’ image and personality [25,26,27]. While the findings indicate the potential of IMT in the tourism sector, notable limitations remain from the viewpoint of service providers. Access-related issues, such as investment costs and perceptions of technology’s potential, remain as barriers to its widespread adoption [28]. The findings indicate a notable deficiency in understanding the use of IMT within sustainable agricultural tourism, affecting both service users (tourists) and service providers (farmers or local communities). This gap requires thorough research to link technology, consumer behavior, and practical usability within the agritourism sector.

This study utilized the innovation decision process framework proposed by Rogers et al. [29] to examine the factors influencing tourists’ acceptance or rejection of IMT in agritourism. Numerous theories have been developed to explain the behavior of users of new technology. Particularly, the technology acceptance model (TAM) examines the intention to accept and utilize new technologies [30]. Additionally, the Stimulus–Organism–Response (SOR) theory, defined by Mehrabian and Russell [31], describes individual behavior in reaction to external stimuli, integrating cognitive and emotional processes. An in-depth understanding of individual behavior, particularly regarding the decision to adopt an innovation, necessitates an examination of the sequential decision-making process. The DOI theory offers a comprehensive, systemic perspective on the knowledge, persuasion, and decision-making of innovation users. Consequently, the DOI and TAM theories are suitable for this research. This study analyzes the psychological factors influencing the decision to adopt IMT in the agritourism sector post-experience. The research questions (RQs) designed for investigating the implementation of IMT in the context of agritourism are as follows:

RQ 1. 

How does immersive storytelling content affect user behaviors following an agritourism experience?

RQ 2. 

What characteristics of IMT correlate with technology resistance behaviors following an agritourism experience?

RQ 3. 

How do psychological traits affect the intention to revisit a destination and the intention to continue utilizing IMT?

This article includes a literature review and the theoretical background in Section 2, the conceptual model and hypothesis development in Section 3, and the research methodology in Section 4. Section 5 presents this study’s results, whereas Section 6 discusses the implications of this study. Section 7 presents the conclusion of this research.

2. The Literature Review and Theoretical Background

2.1. Agritourism Experience

The increasing popularity of agritourism reflects rising demand among tourists for authentic and immersive experiences that connect with local culture and lifestyle. This includes activities such as exploring community environments, sampling local cuisine, participating in do-it-yourself (DIY) craft activities, and harvesting crops [6]. Martinus, Boruff, and Picado [3] articulated that the experiential aspect of agritourism involves tourists engaging in agricultural activities in both agricultural and non-agricultural settings. Agritourism encompasses four experiential dimensions: location (the site of the activity), interaction (the nature of visitor engagement), authenticity (the degree of involvement of agricultural producers/products in the experience), and learning (educational exchanges).

Table 1 provides a summary of previous studies concerning the experiential dimensions of agritourism across each category. Dimension 1, location: Previous research has concentrated on the implementation of agritourism activities within the context of location, particularly in rural highland areas characterized by significant natural and cultural resources. The development of agritourism in this context focuses on the sustainable use of existing resources to promote low-carbon tourism and create community advantages [32].

Dimension 2, interaction: Previous studies indicate that the organization of diverse agritourism activities significantly contributes to the creation of memorable experiences for tourists at a destination. This directly affects tourists’ intentions to support local products and their probability of returning to the area in the future. Agritourism experiences are structured based on varying levels of tourist participation: passive participation involves observing agricultural activities or community lifestyles without direct involvement; participatory experience includes engaging in certain activities with interaction; and commercial participation entails supporting the local economy by purchasing community-produced goods [6,33,34].

Dimension 3, authenticity: Previous research indicates that most experiential activities in agritourism focus on commercial participation and passive engagement. While these activities do not prioritize extensive involvement, they can effectively communicate the community’s identity and traditional agricultural practices. Engagement with local products that reflect narratives or community knowledge promotes tourist contributions to the local economy through the purchase of goods and services, thereby enhancing the probability of destination revisitation [35,36].

Dimension 4, learning: Previous research has focused on structuring educational activities that integrate agricultural knowledge with indigenous lifestyles and cultures, allowing local individuals to share their expertise and firsthand experiences with tourists. The educational framework in agritourism can be organized as instructional sessions that occur during community interactions [37].

Table 1. Summary of research regarding experiential aspects of agricultural tourism.

Authors (Year)	Dimension Type				Interaction Type	Traveler Behavior	Example Activities
	Location	Interaction	Authenticity	Learning
Bhaktikul, Aroonsrimorakot, Laiphrakpam, and Paisantanakij [32]	√				Cultural immersion	Minimizing energy consumption, reducing CO2 emissions, and decreasing pollution	Development of low-carbon tourism via a resource analysis
Liang, Hsiao, Chen, and Lin [6]		√			Passive participation and participatory experience	Intention to revisit	Do-it-yourself (DIY), animal feeding or interaction, ecological guiding, and crop picking
Esau and Senese [33]		√			Passive participation	Facilitating memorable experiences between travelers and destinations	The sensory experiences associated with wine tourism
Rezaei, Kim, Alizadeh, and Rokni [34]		√			Participatory experience	Mental health advantages related to agritourism activities	Agritourism activities that enhance mood and mental well-being
Pehin Dato Musa and Chin [35]		√	√		Commercial and passive participation	Purchase behavior for fresh food ingredients without external intervention	Agritourism activities related to farm-to-table (FTT)
Brune, Knollenberg, Stevenson, Barbieri, and Schroeder-Moreno [36]			√		Commercial participation	Behavior of purchasing food locally	Consumer intent to purchase or support local food
Chen, Lee, Kabre, and Hsieh [37]				√	Cultural immersion	Students’ future career intentions	Experiential benefits, career identity, career choices, and support for tourism

Table 1. Summary of research regarding experiential aspects of agricultural tourism.

Authors (Year)	Dimension Type				Interaction Type	Traveler Behavior	Example Activities
	Location	Interaction	Authenticity	Learning
Bhaktikul, Aroonsrimorakot, Laiphrakpam, and Paisantanakij [32]	√				Cultural immersion	Minimizing energy consumption, reducing CO2 emissions, and decreasing pollution	Development of low-carbon tourism via a resource analysis
Liang, Hsiao, Chen, and Lin [6]		√			Passive participation and participatory experience	Intention to revisit	Do-it-yourself (DIY), animal feeding or interaction, ecological guiding, and crop picking
Esau and Senese [33]		√			Passive participation	Facilitating memorable experiences between travelers and destinations	The sensory experiences associated with wine tourism
Rezaei, Kim, Alizadeh, and Rokni [34]		√			Participatory experience	Mental health advantages related to agritourism activities	Agritourism activities that enhance mood and mental well-being
Pehin Dato Musa and Chin [35]		√	√		Commercial and passive participation	Purchase behavior for fresh food ingredients without external intervention	Agritourism activities related to farm-to-table (FTT)
Brune, Knollenberg, Stevenson, Barbieri, and Schroeder-Moreno [36]			√		Commercial participation	Behavior of purchasing food locally	Consumer intent to purchase or support local food
Chen, Lee, Kabre, and Hsieh [37]				√	Cultural immersion	Students’ future career intentions	Experiential benefits, career identity, career choices, and support for tourism

The experiential dimension has become an essential component and an effective marketing strategy in agritourism. Modern tourists increasingly pursue meaningful experiences in rural destinations, focusing on simplicity, a deeper connection to nature, and interaction with the local culture [38]. Creating a tourism experience that promotes awareness of community lifestyles enhances tourist satisfaction and is essential for building trust in service providers and agricultural products [39]. This approach cultivates relationships between tourists and local communities, promoting sustainability in the economic, social, and cultural aspects of agricultural areas.

2.2. Immersive Technology in Tourism

The COVID-19 pandemic significantly impacted the global tourism industry, prompting the rapid integration of digital technologies to improve the travel experience. Key innovations include IMT such as virtual reality (VR) and augmented reality (AR) [7,40], artificial intelligence (AI) [41], big data analytics [42], and the Internet of Things (IoT) [43]. These technologies collectively enhance the creation of safe, realistic, and behaviorally adaptive travel experiences for tourists in the post-pandemic context. IMTs are notably significant as they improve the travel experience throughout all stages: pre-trip, during the trip, and post-trip [44].

IMT obscures the distinctions between the physical and virtual realms, allowing users to experience a sense of presence in the real world [45]. IMT includes various tools such as augmented reality (AR), which allows for interaction with virtual content overlaid onto the physical world; virtual reality (VR), which creates interactive environments that mimic real-life experiences (non-immersive VR displays content through a computer screen or traditional media without specialized equipment, while immersive VR necessitates a head-mounted display for complete engagement); and mixed reality (MR), which combines virtual and real objects in an able environment, enabling fluid interaction.

Milgram and Kishino [46] proposed the concept of IMTs and defined the range of these technologies through the virtual reality continuum. Augmented virtuality (AV) and virtual reality (VR) are often used interchangeably, as both involve the integration of real objects into a virtual environment [47]. Figure 1 illustrates the spectrum of IMT. AR and VR technologies facilitate a mixed-reality experience, enabling users to engage with an environment that integrates physical and virtual objects.

Lemon and Verhoef (2016) [48] defined the travel experience as the sum of customer travel activities which includes the stages before, during, and after the purchase. Kim, Ritchie, and McCormick [21] identified three stages of experience: pre-experiences, during, and post-experiences. IMTs enhance the travel experience at various stages, including pre-trip, on site, and post-trip [22]. Previous studies have examined the traveler behaviors at each stage to identify factors that influence the enhancement of the travel experience (refer to Table 2), which can be categorized as follows:

Pre-experiences: Previous research has concentrated on investigating the influence of IMT on travelers’ behavior, specifically the incorporation of the metaverse, a virtual reality tool, into tourism marketing to enhance engagement, satisfaction, and intention to visit destinations [16,18,19,44]. The application of VR technology to enhancing wine tourists’ intention to visit wine production sites was explored in [17]. Furthermore, virtual reality (VR) and augmented reality (AR) applications and games have been utilized to enhance tourism marketing and stimulate travel intentions [16]. The aforementioned studies indicate that the use of IMT during the pre-trip phase significantly enhances a destination’s image and influences tourists’ cognitive and emotional processes, resulting in satisfaction, travel intention, and the propensity to recommend the destination.

During experiences: Currently, much of the research focuses on the impact of IMT on enhancing the tourist experience. Augmented reality (AR) and virtual reality (VR) technologies are employed to offer insights into tourist attractions, including the presentation of museum or landscape information via AR applications on smartphones or wearable devices. Besides augmenting tourist experiences, IMTs also enhance the emotional and cognitive capacities of travelers [49,50], enabling them to interact with locations and content that are otherwise unattainable in the physical realm, resulting in the formation of significant and memorable learning experiences.

Post-experiences: During this phase, IMTs persist in facilitating museums’ objectives of enhancing value and education and promoting immersive experiences and behaviors [51]. IMTs facilitate the establishment of emotional connections between travelers and destinations, thereby impacting brand affinity, engagement, and loyalty [23,24,44]. IMTs currently enhance post-experiences, fostering repeat travel intentions and generating positive word-of-mouth for destinations.

Table 2. An overview of previous studies regarding the experiential aspects of travel in tourism utilizing IMT.

Authors (Year)	Experience Stage			Theories	Input Factors	Behavioral	Limitations
	Pre	During	Post
Shamim, Gupta, and Shin [18]	√			The technology acceptance model (TAM)	Immersive experiences and user perceptions	User engagement	User-generated content, the metaverse platform, and typology of destinations
Casais, Coelho, and Escadas [16]	√			Stimulus–Organism–Response (SOR) framework	Vision, hearing, and haptics in tourism metaverse previews	Intention to visit	Typology of destinations, attitudes towards the metaverse, awareness of physiological and psychological consequences, security and privacy
Jafar and Ahmad [19]	√			SOR	Immersion, escapism and enjoyment, and cognitive processing	Tourist satisfaction and loyalty for metaverse experiences	Opinions of tourists: consider suggestions and revisit metaverse destinations
Atzeni, et al. [52]	√			SOR	Object-based authenticity, existential authenticity, affective response, satisfaction, and VR attachment	Intention to visit	Other types of authenticity, geographical distance, the use of non-immersive technology, motivations and willingness to pay
Di Dalmazi, et al. [53]	√			SOR	Effectiveness of immersive VR, cognition (presence), and affection (arousal)	Intention to visit and recommend a destination	Only focuses on one emotional dimension and typology of destination
An, et al. [54]	√			Flow theory and SOR	The psychological process through which the VR travel evokes flow	Satisfaction and intention to visit	Individual-centric variables influence VR travel experiences and the typology of destinations
Kieanwatana and Vongvit [55]	√			TAM	Destination image, virtual experiences, information access, content quality, perceived usefulness, perceived ease of use, novelty, stimulation, recognition, safety, and independent travel	Intention to visit	Cybersecurity, sustainability, travel experience, personality traits, and typology of destinations
Hui, et al. [56]	√			Pro-environmental theory and media richness theory	Journalism, metaverse-based regenerative tourism promotion, eco-literacy, dispositional empathy, and pro-environmental behavior	Regenerative tourism intention	Economic and socio-cultural factors in regenerative tourism intentions, health awareness, and climate change
Hao, Liu, Zhang and Chon [40]		√		Embodied social presence theory with social identity theory	Technological attributes, user attributes, social presence, and tourist satisfaction	Word-of-mouth in sustainable tourism	Avatar traits; used a video-based survey; typology of destinations
Balakrishnan, et al. [57]		√		Cognitive embodiment theory and metacognitive theory	VR-based interactions (ergonomics and embodiment)	Memorable experiences and revisit intention	The role of embodiment and self-concept in the metaverse, the role of ergonomics, and the typology of destinations
Robaina-Calderín, Martín-Santana and Munoz-Leiva [50]		√		-	Stimulus at the level of immersion of the experience (head-mounted display (HDM), mobile devices and VR glasses, and computer screens)	Experience immersion levels, affective and cognitive performance, and intention to visit	Cultural intensity and profile, previous experience, past visits, intellectual curiosity, technological profile, sociodemographic characteristics, and neuromarketing
Abou-Shouk, Zouair, Abdelhakim, Roshdy and Abdel-Jalil [44]	√		√	Theory of planned behavior (TPB), TAM, the value-based adoption model (VAM), and the hedonic-motivation system adoption model (HMSAM)	Perceived ease of use, enjoyment, immersion, usefulness, attitude towards immersive technology adoption, and perceived value and engagement	Loyalty	Specific tourism settings or comparison of perceptions of adoption within different sectors of tourism
Luo and Xia [24]			√	Consistency and place attachment theory	Virtual tourism experiences during the post-trip stage	Place attachment	Consumers’ sensory perceptions, the impact of the metaverse, behavioral intentions, and typology of destinations
Le, Tran and Le [23]			√	Self-congruence theory and the psychology of flow theory	Self-congruence and destination brand immersion	Destination brand love	The role of influencers, social media, and all other forms of media and typology of destinations

Table 2. An overview of previous studies regarding the experiential aspects of travel in tourism utilizing IMT.

Authors (Year)	Experience Stage			Theories	Input Factors	Behavioral	Limitations
	Pre	During	Post
Shamim, Gupta, and Shin [18]	√			The technology acceptance model (TAM)	Immersive experiences and user perceptions	User engagement	User-generated content, the metaverse platform, and typology of destinations
Casais, Coelho, and Escadas [16]	√			Stimulus–Organism–Response (SOR) framework	Vision, hearing, and haptics in tourism metaverse previews	Intention to visit	Typology of destinations, attitudes towards the metaverse, awareness of physiological and psychological consequences, security and privacy
Jafar and Ahmad [19]	√			SOR	Immersion, escapism and enjoyment, and cognitive processing	Tourist satisfaction and loyalty for metaverse experiences	Opinions of tourists: consider suggestions and revisit metaverse destinations
Atzeni, et al. [52]	√			SOR	Object-based authenticity, existential authenticity, affective response, satisfaction, and VR attachment	Intention to visit	Other types of authenticity, geographical distance, the use of non-immersive technology, motivations and willingness to pay
Di Dalmazi, et al. [53]	√			SOR	Effectiveness of immersive VR, cognition (presence), and affection (arousal)	Intention to visit and recommend a destination	Only focuses on one emotional dimension and typology of destination
An, et al. [54]	√			Flow theory and SOR	The psychological process through which the VR travel evokes flow	Satisfaction and intention to visit	Individual-centric variables influence VR travel experiences and the typology of destinations
Kieanwatana and Vongvit [55]	√			TAM	Destination image, virtual experiences, information access, content quality, perceived usefulness, perceived ease of use, novelty, stimulation, recognition, safety, and independent travel	Intention to visit	Cybersecurity, sustainability, travel experience, personality traits, and typology of destinations
Hui, et al. [56]	√			Pro-environmental theory and media richness theory	Journalism, metaverse-based regenerative tourism promotion, eco-literacy, dispositional empathy, and pro-environmental behavior	Regenerative tourism intention	Economic and socio-cultural factors in regenerative tourism intentions, health awareness, and climate change
Hao, Liu, Zhang and Chon [40]		√		Embodied social presence theory with social identity theory	Technological attributes, user attributes, social presence, and tourist satisfaction	Word-of-mouth in sustainable tourism	Avatar traits; used a video-based survey; typology of destinations
Balakrishnan, et al. [57]		√		Cognitive embodiment theory and metacognitive theory	VR-based interactions (ergonomics and embodiment)	Memorable experiences and revisit intention	The role of embodiment and self-concept in the metaverse, the role of ergonomics, and the typology of destinations
Robaina-Calderín, Martín-Santana and Munoz-Leiva [50]		√		-	Stimulus at the level of immersion of the experience (head-mounted display (HDM), mobile devices and VR glasses, and computer screens)	Experience immersion levels, affective and cognitive performance, and intention to visit	Cultural intensity and profile, previous experience, past visits, intellectual curiosity, technological profile, sociodemographic characteristics, and neuromarketing
Abou-Shouk, Zouair, Abdelhakim, Roshdy and Abdel-Jalil [44]	√		√	Theory of planned behavior (TPB), TAM, the value-based adoption model (VAM), and the hedonic-motivation system adoption model (HMSAM)	Perceived ease of use, enjoyment, immersion, usefulness, attitude towards immersive technology adoption, and perceived value and engagement	Loyalty	Specific tourism settings or comparison of perceptions of adoption within different sectors of tourism
Luo and Xia [24]			√	Consistency and place attachment theory	Virtual tourism experiences during the post-trip stage	Place attachment	Consumers’ sensory perceptions, the impact of the metaverse, behavioral intentions, and typology of destinations
Le, Tran and Le [23]			√	Self-congruence theory and the psychology of flow theory	Self-congruence and destination brand immersion	Destination brand love	The role of influencers, social media, and all other forms of media and typology of destinations

Table 2 illustrates that studies on the application of IMT in tourism have employed diverse theories to explain tourists’ behavior across various stages of the travel experience, particularly concerning acceptance, interaction, and behavioral outcomes. The TAM is a widely recognized theory that elucidates users’ intentions to adopt and utilize technology, emphasizing the roles of perceived usefulness and perceived ease of use [30]. Nonetheless, one limitation of the TAM is its inability to thoroughly address psychological factors or the social context.

Stimulus–Organism–Response (SOR) theory serves as a significant framework for understanding immersive experiences. It explains the impact of environmental stimuli on individuals’ cognitive and emotional processes, resulting in varied response behaviors, both positive and negative [31]. This theory is especially relevant in the context of IMTs that engage users’ senses and emotions. The SOR model offers a dimensional explanation of affective behaviors; however, its complexity in psychological measurement often necessitates its use alongside other models for a structural analysis.

Some research studies have utilized flow theory to explain user experiences in computer-mediated environments. The computer-mediated environment (CME) highlights that intense engagement and concentration on immersive experiences can notably influence psychological and behavioral outcomes [23,58]. This approach facilitates the assessment of user engagement and satisfaction with technology. However, one limitation is that it does not address external driving factors. In addition, media richness theory has been applied to explaining how the characteristics of digital media, especially the ability to convey complex information and create immersive interactions, can influence tourists’ perceptions and travel intentions [56]. This limitation arises from its primary application in organizational management, rather than at the individual level, and it does not directly address user behavior.

The theories mentioned above provide partial explanations for the acceptance and response behaviors associated with immersive technology; however, a comparative analysis reveals that each theory possesses distinct strengths and limitations. The TAM is appropriate for examining the initial phase of usage. The SOR and flow theories are effective frameworks for assessing emotional impact and user satisfaction. Media richness theory emphasizes communication via technology. To comprehend user behavior, particularly regarding emerging innovations, it is essential for studies to examine the innovation–decision process. This dimension is crucial for understanding how individuals perceive, evaluate, and ultimately decide to accept or reject new technologies.

2.3. Diffusion of Innovation (DOI) Theory

Innovation is a critical factor in the success and sustainable development of a company [59,60]. The adoption of innovations is frequently context-dependent, prompting an examination of the factors influencing it from multiple viewpoints. DOI theory is a widely accepted framework for explaining the adoption of technological innovations. Rogers, Singhal, and Quinlan [29] developed the DOI theory as a framework for analyzing the acceptance or rejection of innovations by users. The process comprises five steps: (1) knowledge (fundamental information regarding the innovation), (2) persuasion (favorable or unfavorable attitudes towards the innovation), (3) decision (the choice to accept or reject the innovation based on its merits and drawbacks), (4) implementation (the execution of the innovation), and (5) confirmation (validation that the innovation is a beneficial option) (refer to Figure 2). These processes serve as a framework for analyzing user behavior in response to new technologies, particularly within the tourism sector, where IMTs are prevalent.

Rogers, Singhal, and Quinlan [29] identified the psychological factors that reduce uncertainty and enhance adoption rates: relative advantage (the innovation is viewed as superior to the existing methods), compatibility (the innovation aligns with prior experiences), complexity (the user-friendliness of the innovation), trialability (the ability to test the innovation), and observability (the benefits users gain from the innovation). This model is valuable for innovation research aiming to understand complex user behaviors through the identification of additional psychological factors [61,62,63]. The reasons for tourists’ approval or rejection of new technologies, particularly the implementation of IMTs within the tourism sector, are clarified by this five-factor analysis.

The DOI theory provides a foundational framework for examining motivation and the adoption of new technologies in the tourism sector. DOI has provided a framework for analyzing the impact of intellectual capital (IC) on organizational resilience (OR) in the tourism and service sectors, emphasizing the roles of organizational agility (OA) and innovation. Alnasser, et al. [64] have demonstrated that IC has a beneficial impact on OR, OA, and innovation. In this study, the factors that influence the net benefits of smart tourism technologies for tourists are investigated. The model is based on the theory of DOI and includes use intention, perceived value, and net benefits for tourists. The research indicates that technology functions, both directly and indirectly, affect the net benefits via perceived value and usage intention [65].

Building on this, DOI theory presents a framework for explaining the process of blockchain adoption in the tourism sector [66]. The integration of DOI and Technology–Organization–Environment (TOE) frameworks was employed to examine the diffusion of intelligence within the hospitality industry and the adoption of smart technologies. This study’s results offer insights into overcoming barriers to the diffusion of intelligence and integrating smart technologies into hotel operations [67]. The determinants influencing the adoption of AI chatbots within the tourism and travel sector have been analyzed. The findings indicate that both advantages and trialability positively influence the adoption of AI chatbots. The factors hindering their adoption include compatibility, complexity, and observability. Trust notably diminishes the correlation between adoption intention and actual usage [68].

Mahmoud, et al. [69] conducted a study that combined the DOI theory with the TAM to create a metaverse adoption model specifically for the tourism sector, leveraging big data. The findings indicate that the metaverse fosters complex positive beliefs and emotions, such as excitement, appreciation, and substantial immersion, underscoring the technology’s potential to enhance the tourism experience, as illustrated in

Table 3. Utilization of DOI theory in the context of tourism.

Authors (Year)	Context Studied	Theories	Findings
Alnasser, Alkhozaim, Alshiha, Al-Romeedy and Khairy [64]	Five-star hotels and travel agencies	DOI	Intellectual capital enhances organizational resilience by fostering agility and innovation.
Yi Wang, Wenyuan, Kumari, Tian and Zhang [65]	Smart tourism technology	DOI and perceived value	Technology exerts both direct and indirect influences on net benefits.
Erol, Neuhofer, Dogru, Oztel, Searcy and Yorulmaz [66]	Blockchain	DOI	Elucidates the integration procedure for blockchain technology.
Stylos, Fotiadis, Shin and Huan [67]	Smart systems	DOI + TOE	Proposes strategies to surmount the challenges in the implementation of smart technology.
Alam, et al. [70]	Augmented reality (AR)	DOI + TOE	Exhibits the technological and environmental influences on the application of augmented reality in tourism and tour operations.
Hanji, Hungund, Blagov, Desai and Hanji [68]	AI chatbot	DOI	Complexity and observability impede adoption, whereas trust alleviates adverse effects.
Mahmoud, Fuxman, Asaad, and Solakis [68]	VR (metaverse)	DOI, the TAM, and big data	The metaverse fosters positive emotions and profound engagement.

. Previous studies indicate that the DOI theory has significant applications in the tourism sector, particularly in the analysis of innovation adoption behavior, emerging technologies, and organizational adaptation. This expansion enables the application of DOI in both individual and organizational contexts.

The DOI theory presents a theoretical framework that clarifies the decision-making processes of innovation users within the tourism sector; nonetheless, its application to IMT is limited. Recent studies have focused on synthesizing user opinions about tourism in the metaverse, primarily through the use of big data [69]. This approach lacks an in-depth understanding of users’ decision-making processes, especially concerning agricultural tourism combined with IMT, a subject that has been largely overlooked in the current literature. Understanding innovation acceptance behavior requires the consideration of multiple factors, including rational, emotional, and psychological elements that may affect the acceptance or rejection of innovation.

3. The Conceptual Model and Hypothesis Development

In the context of agritourism, this study is the first to employ the DOI theory framework to comprehend the decision-making process of users in the post-experience phase with respect to IMT. Psychological factors are our primary focus in gaining a comprehensive understanding of the intricacies of user behavior. This research framework is based on the three-stage decision-making process in Rogers, Singhal, and Quinlan [29]’s theory, as follows:

The knowledge process involves users perceiving and understanding essential information related to innovation. This step examines the narrative content of IMT and the user characteristics that affect their understanding and perception of the innovation.

The persuasion process involves users evaluating the innovation from both emotional and attitudinal perspectives. This step employs elements from the TAM to clarify users’ emotions and responses to IMT.

The decision process involves users evaluating the acceptance or rejection of an innovation. This phase focuses on analyzing the intentions to return to the original destination and the continued use of IMT among users with previous experiences. This illustrates the sustainable development strategy for IMT during the post-experience phase of agritourism. Figure 3 illustrates the conceptual model of this study.

3.1. Immersive Storytelling Content

Storytelling content serves as a form of emotional communication that enhances audience engagement. Storytelling activities are prevalent in human life [71]. Currently, storytelling content is utilized as a digital marketing tool, integrating narrative techniques with IMT to influence audiences [72]. Consequently, numerous businesses are employing storytelling content across diverse processes, including advertising, customer service, brand marketing, and product sponsorship. Storytelling serves as an effective marketing strategy by successfully capturing consumers’ attention [73]. Well-structured storytelling content can enhance brand image, boost customer engagement, increase emotional value, influence perceptions of a product or brand value, and affect consumer purchasing behavior based on their needs and attitudes. As a result, storytelling is increasingly employed in various contexts [26].

The narrative of locations is crucial in tourism within the digital era as destinations compete for tourist attention [74]. Travelers can share their experiences by employing storytelling techniques related to attractions, integrating virtual characters, soundscape design, and advanced audio interactions. IMT provides entertainment, education, and interactive experiences for visitors and tourists in virtual museums [75,76]. Previous studies have concentrated on creating applications and tools utilizing immersive storytelling content to enhance the user experience [77,78,79]. Further research on immersive storytelling content formats within agritourism remains necessary.

This study utilized the framework proposed by Peters, et al. [80], integrating theories from marketing, psychology, and sociology to categorize social media content into three distinct types: (1) Content passive (CPS) refers to the clarity of the presented content. This study examines the clarity of agritourism storytelling content delivered through various IMTs, including images and videos. Content valence (CVL) refers to the narrative elements that communicate emotions and feelings. This study examines the elements of storytelling history, community environment, and activities across multiple dimensions as presented through IMT. Content volume (CVO) refers to the frequency and amount of content produced. This study analyzes the prevalence of storytelling content designed to motivate travelers to participate in agritourism activities post-experience. The hypotheses are outlined as follows:

H1a: 

CPS has a significant impact on travelers’ attitude toward (ATT) the use of IMT in agritourism.

H1b: 

CVL positively influences travelers’ ATT the use of IMT in agritourism.

H1c: 

CVO positively influences travelers’ ATT the use of IMT in agritourism.

3.2. Immersive Technology Resistance

Innovation resistance refers to the tendency of individuals to resist the adoption of innovations. This resistance may arise from negative perceptions, previous experiences, established behavioral patterns, and the perceived risks associated with the adoption process [81,82]. Chen, et al. [83] analyzed the factors influencing resistance, categorizing them into three distinct groups:

• Innovation characteristics denote the attributes of an innovation that users believe affect their resistance, including its relative advantages, compatibility, perceived risk, and complexity;
• Consumer characteristics suggest that resistance to innovation depends on users’ psychological attributes, including their perceptions, motivations, and experiences;
• Propagation mechanisms involve the credibility, transparency, and similarity of information sources and the volume of information available.

Lifestyle compatibility refers to the degree to which an innovation relates to users’ established values, previous experiences, and requirements [84]. The compatibility of IMT adoption pertains to the degree to which users regard these technologies to be aligned with their existing habits and lifestyles, subsequently affecting their intention to persist in using them in the future [85]. Conversely, technological compatibility and apprehensions about its efficacy or safety are obstacles to the adoption of technologies [68,86,87]. Moreover, obstacles and difficulties related to IMT have attracted heightened academic attention, encompassing the incompatibility of head-mounted displays (HMDs) with physical accessibility devices (e.g., glasses), the discomfort arising from VR headsets, and the misperception of reality (e.g., users interpreting VR as real) [88]. Research indicates that the development of immersive 3D environments by game engines could improve the compatibility with virtual reality (VR) [89].

Perceived risk refers to the degree to which users acknowledge the potential risks linked to the use of a service or product. This recognition is influenced by various factors, including their evaluation of the safety of an innovation or online platform, which may affect users’ privacy [71,90]. Previous studies demonstrate that safety improves virtual experiences by promoting trust and comfort. User perceptions of safety lead to increased engagement, which improves their connection to a destination and positively influences their perceptions [55,91]. Conversely, the recognition of privacy risks by users may result in resistance to the adoption of innovation, as IMT devices and platforms can collect extensive biometric data related to individuals’ physical, physiological, and behavioral characteristics, including their facial expressions, eye movements, gestures, gait, and posture [92]. From a destination perspective, security risk refers to the possibility of unexpected laws and regulations being enacted at a tourist attraction [93].

This study examines the characteristics of IMT, specifically its compatibility (COM) and privacy and security risk (PSR), that may influence resistance to the adoption of IMT in agritourism during the post-experience phase. The hypotheses are stated as follows:

H2a: 

COM positively influences travelers’ ATT the use of IMT in agritourism.

H2b: 

PSR positively influences travelers’ ATT the use of IMT in agritourism.

3.3. User Characteristics

The acceptance or rejection of an innovation is related to consumers’ psychological characteristics, such as their perception, motivation, and experience [83]. The TAM has been utilized to analyze the factors influencing users’ acceptance and utilization of technology, focusing on key variables: perceived ease of use (PEOU), perceived usefulness (PU), and attitude toward (ATT). Perceived ease of use (PEOU) is a term that refers to the hardware and a user-friendly interface in the context of IMT. PU is the user’s evaluation of the effectiveness of IMT in addressing their requirements. ATT indicates the degree to which an individual’s emotional inclination towards technology use is affected. Individuals with positive perceptions of technology choose to view it as reliable and helpful [44]. Previous studies indicate that perceived ease of use and perceived usefulness affect virtual reality experiences. The application of IMT to destination image has been studied in [18,55]. Furthermore, the attitude toward technology serves as a predictor for the adoption of IMT in tourism [44]. The hypotheses are stated as follows:

H3a: 

PEOU positively influences travelers’ ATT the use of IMT in agritourism.

H3b: 

PEOU positively influences travelers’ PU of IMT in agritourism.

H4: 

PU positively influences travelers’ ATT the use of IMT in agritourism.

3.4. Continued Decisions

The process of accepting or rejecting an innovation involves users evaluating its advantages and disadvantages. Users will choose to continue utilizing an innovation if they perceive it as advantageous. Conversely, users who perceive an innovation as ineffective will choose to abandon it [29]. IMT, particularly through VR experiences, significantly affects perceptions of destinations, which in turn influences both travel intentions and the intention to revisit [57,94]. Individuals with high levels of immersion choose to show improved emotional and cognitive effectiveness, which in turn affects their intention to visit [50]. This study aims to analyze the factors influencing the decision to revisit a location and the ongoing use of IMT in agritourism. The hypotheses are stated as follows:

H5: 

ATT positively influences travelers’ REV with the use of IMT in agritourism.

H6: 

ATT positively influences travelers’ CON in agritourism.

This research’s conceptual model is formulated based on hypotheses generated from a review of the relevant literature, aiming to explain users’ decision-making processes concerning IMT in the post-experience phase. The next section describes the research methodology, including the sampling and data collection, the measurement instruments, and data analysis, to systematically and empirically evaluate the research questions and hypotheses formulated in this study.

4. Methodology

4.1. The Sampling and Data Collection

This research was a cross-sectional study conducted from March to April 2025, employing a purposive sampling method to fulfill the study objectives and recruiting participants with experience in utilizing IMT for tourism for no more than three months in Prachuap Khiri Khan Province, Thailand, irrespective of their gender or occupation. The selection of users with no more than three months of experience in immersive tourism technology was for the following reason: users who have long-term experience may be unable to remember certain information correctly or may be confused about past situations, resulting in inaccurate survey data in certain areas.

The selection of the research participants followed the ethical protocols set by the Institutional Review Board of Rajamangala University of Technology Rattanakosin, Thailand (RMUTR-IRB, COA No. 012/2025). The procedure was divided into two main phases. The first involved a pilot test conducted prior to the main study to evaluate the reliability and validity of the developed scale measuring place attachment styles. Concurrently, a feasibility assessment of the questionnaire was carried out by three experts specializing in IMT. A total of 100 participants were selected for the pilot phase. For the main study, the minimum required sample size was calculated using the G*Power program version 3.1.9.7 [95], resulting in a target of 277 participants. To enhance the data’s reliability, an extra 45% of the sample size—around 123 individuals—was gathered, resulting in a final sample of approximately 400 participants.

The data collection commenced with the distribution of online questionnaires through platforms such as Facebook and Line. In the subsequent phase, the objectives of the research were clearly communicated, and measures were implemented to ensure data confidentiality. Additionally, the online contact information of both the principal researcher and the research assistant was provided so that they could address any inquiries from the respondents, thereby minimizing potential bias and fostering trust in the accuracy of the data collected [96]. Respondents who voluntarily consented to participate were able to confirm their participation and proceed to complete the online questionnaire in the following section. Participants retained the right to withdraw from this study at any stage, and all questionnaire data were scheduled for deletion upon the completion of the data analysis.

4.2. The Measurement Instrument

The questionnaire was developed utilizing the DOI framework, TAM theory, and the theory of innovation resistance. The questionnaire comprised five sections: Section 1: general information on the respondents, including gender, age, and education; Section 2: an analysis of immersive storytelling content, encompassing CPA, content valence (CVA), and content volume (CVO); Section 3: the characteristics of IMT potentially associated with innovation resistance, including compatibility (COM) and privacy and security risk (PSR); Section 4: user characteristics that may influence innovation resistance, encompassing perceived usefulness (PU), perceived ease of use (PEOU), and attitudes toward (ATT); and Section 5: continuation decisions (revisit intention (REV) and the continued use of IMT (CON)). The responses were assessed utilizing a 5-point Likert scale (1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; and 5 = strongly agree), as illustrated in

Table 4. Measurement items from the questionnaire.

Construct	Items	Survey Item	References
Content passive (CPA)	CPA1	Storytelling content delivered through immersive technologies, such as images, videos, or texts, elicits emotional responses from travelers.	Adapted from Luo and Xia [24], Ghorbanzadeh, Nair, Chandra, Bakhtiyorovich Ergashev and Prasad [94], Song and Lu [97]
	CPA2	Immersive-technology-based storytelling increases travelers’ awareness.
	CPA3	Storytelling content delivered via immersive technologies increases travelers’ participation in activities.
	CPA4	Immersive technologies deliver storytelling content that increases engagement in a variety of destinations.
Content valence (CVA)	CVA1	Destination storytelling combines emotional elements with immersive technology to increase traveler engagement.	Adapted from Song and Lu [97], Jattamart and Leelasantitham [98]
	CVA2	Destination storytelling integrates emotional engagement with immersive technology to elicit emotional responses from travelers.
	CVA3	Destination storytelling uses emotional engagement and immersive technology to elicit emotional responses from travelers.
	CVA4	Destination storytelling combines emotional elements with immersive technology to increase engagement with destinations.
Content volume (CVO)	CVO1	Storytelling content, which is prevalent in immersive technologies, encourages travelers to participate in a variety of activities.	Adapted from Abou-Shouk, Zouair, Abdelhakim, Roshdy and Abdel-Jalil [44], Jattamart and Leelasantitham [98]
	CVO2	Storytelling content found in immersive technologies elicits emotional responses from travelers.
	CVO3	Immersive technologies use storytelling content to help create a visual perception of a destination.
	CVO4	Immersive technology’s storytelling content boosts destination engagement.
Compatibility (COM)	COM1	Devices designed for immersive media are limited in terms of compatibility with current hardware.	Adapted fromZhuang [71], Yang, Yu, Zo and Choi [87]
	COM2	Devices used for displaying immersive media have limitations in their application in agritourism.
	COM3	Wearable devices restrict the use of immersive technology in travel.
	COM4	Immersive technology may not provide the expected benefits in travel.
Privacy and security risk (PSR)	PSR1	There are concerns that devices used to access immersive media may collect excessive personal data.	Adapted from Kieanwatana and Vongvit [55], Faqih [99]
	PSR2	There are concerns that their usage may result in the disclosure of personal information to unrelated third parties.
	PSR3	There are issues with the collection of biometric data by the devices used for immersive media presentations.
	PSR4	Insecurities associated with the use of immersive technology have an impact on privacy.
Perceived usefulness (PU)	PU1	Immersive technology is extremely useful in agritourism.	Adapted from Abou-Shouk, Zouair, Abdelhakim, Roshdy and Abdel-Jalil [44], Yang, Yu, Zo and Choi [87]
	PU2	Immersive technology can display information that is relevant to agritourism.
	PU3	Immersive technology contributes to the creation of a perceived image of the destination.
	PU4	Immersive technology aids in the collection of data relevant to agritourism.
Perceived ease of use (PEOU)	PEOU1	Immersive technology is easy to use.	Adapted from Abou-Shouk, Zouair, Abdelhakim, Roshdy and Abdel-Jalil [44]
	PEOU2	The use of immersive technology requires no learning effort.
	PEOU3	The immersive technology’s operation interface is user-friendly and understandable.
	PEOU4	The use of immersive technology for agritourism is simple.
Attitudes toward (ATT)	ATT1	Immersive technology is beneficial to agritourism.	Adapted from Kwangsawad and Jattamart [60]
	ATT2	Immersive technology combined with agritourism is a good idea.
	ATT3	Immersive technology makes it easier for tourists to travel.
	ATT4	Immersive technology allows for more convenient and immersive access to destination information.
Revisit intention (REV)	REV1	I intend to return to the real-world agritourism site.	Adapted from Casais, Coelho and Escadas [16], Hasan, et al. [100]
	REV2	I think I’ll return to the agritourism site soon.
	REV3	I am willing to spend time and money visiting the agritourism site again.
	REV4	If the opportunity arises, I intend to return to participate in agritourism.
Continued use of IMT (CON)	CON1	I plan to continue using immersive technology as a source of information for agritourism in the future.	Adapted from Kwangsawad and Jattamart [60], Song, et al. [101]
	CON2	In the future, I plan to continue using immersive technology to gather agritourism information.
	CON3	In the future, I plan to use immersive technology for agritourism.
	CON4	I plan to recommend others to use immersive technology for agritourism.

.

4.3. The Data Analysis

This study utilized partial least squares structural equation modeling (PLS-SEM) to analyze the relationships among the proposed variables. The data were analyzed using SmartPLS version 4 [102]. PLS-SEM is acknowledged as an effective method for analyzing consumer technology behavior and accurately predicting relevant components. Previous research indicates that PLS-SEM has been extensively utilized to examine the adoption of IMT within the tourism sector [7,19,40,44,103]. This method simultaneously analyzes the measurement and structural models, assessing the validity of the research instrument through composite reliability (CR) and average variance extracted (AVE) statistics. Considering the outlined technical advantages, PLS-SEM is an appropriate method that aligns with the objectives of this research.

5. Results

5.1. Descriptive Statistics

A total of 402 responses were received in the survey. The analysis of the data revealed that only 400 entries contained complete information.

Table 5. Demographic characteristics.

Characteristics	Frequency (N = 400)	Percentage
Gender
Female	150	37.5
Male	250	62.5
Age
20 years old or younger	72	18.0
20–25 years	212	53.0
26–30 years	71	17.8
31–35 years	29	7.2
36–40 years	15	3.8
41–45 years	1	0.3
Education level
Below high school/vocational certificate	72	18
High school/vocational certificate	46	11.5
Associate degree/vocational certificate	45	11.3
Bachelor’s degree	218	54.5
Master’s degree	14	3.5
Doctorate degree	5	1.3

presents the demographic characteristics of the sample, indicating that the majority of the respondents were male (62.5%) and aged 20–25 years (53.0%) and possessed a bachelor’s degree (54.5%).

5.2. The Measurement Model

A convergent validity assessment was implemented in the measurement model test [104]. The evaluation indicated that the component weight values (Outer Loadings) were within the range of 0.855 to 0.948, which was consistent with the requirement of a minimum of 0.7. The Cronbach’s α values ranging from 0.828 to 0.943 also satisfied the minimum requirement of 0.7. The composite reliability (CR) values were consistently within the range of 0.921 to 0.959, in accordance with the criterion of 0.7. Moreover, the Average Variance Extracted (AVE) values exceeded the minimum criterion of 0.5, with a range of 0.776 to 0.893, as demonstrated in

Table 6. Measurement model (convergent validity, reliability, and multicollinearity).

Constructs	Items	Factor Loadings (>0.70)	Cronbach’s α (>0.70)	Composite Reliability (>0.70)	AVE (>0.50)
Content passive (CPA)	CPA1	0.903	0.913	0.939	0.779
	CPA2	0.887
	CPA3	0.867
	CPA4	0.904
Content valence (CVA)	CVA1	0.897	0.927	0.948	0.820
	CVA2	0.910
	CVA3	0.911
	CVA4	0.905
Content volume (CVO)	CVO1	0.934	0.908	0.942	0.844
	CVO2	0.917
	CVO3	0.905
Compatibility (COM)	COM1	0.933	0.943	0.959	0.853
	COM2	0.916
	COM3	0.924
	COM4	0.920
Privacy and security risk (PSR)	PSR1	0.909	0.927	0.948	0.820
	PSR2	0.881
	PSR3	0.903
	PSR4	0.929
Perceived usefulness (PU)	PU1	0.873	0.904	0.933	0.776
	PU2	0.901
	PU3	0.858
	PU4	0.891
Perceived ease of use (PEOU)	PEOU1	0.942	0.943	0.944	0.893
	PEOU2	0.948
Attitudes toward (ATT)	ATT1	0.893	0.887	0.930	0.816
	ATT2	0.917
	ATT3	0.899
Revisit intention (REV)	REV1	0.923	0.927	0.926	0.806
	REV2	0.855
	REV3	0.914
Continued use of IMT (CON)	CON1	0.929	0.828	0.921	0.853
	CON2	0.918

.

The Heterotrait–Monotrait (HTMT) correlation ratio was employed to assess the discriminant validity, as presented in

Table 7. Discriminant validity assessment using the Heterotrait–Monotrait (HTMT) ratio.

Constructs	CPA	CVA	CVO	COM	PSR	PU	PEOU	ATT	REV	CON
CPA
CVA	0.717
CVO	0.682	0.725
COM	0.691	0.735	0.650
PSR	0.655	0.644	0.598	0.661
PU	0.484	0.501	0.608	0.573	0.512
PEOU	0.683	0.607	0.626	0.569	0.490	0.311
ATT	0.585	0.607	0.709	0.644	0.492	0.655	0.529
REV	0.698	0.787	0.757	0.746	0.629	0.531	0.618	0.688
CON	0.627	0.683	0.757	0.652	0.575	0.586	0.578	0.763	0.791

Note: Content passive (CPA), content valence (CVA), content volume (CVO), compatibility (COM), privacy and security risk (PSR), perceived usefulness (PU), perceived ease of use (PEOU), attitudes toward (ATT), revisit intention (REV), and continued use of IMT (CON).

. The HTMT ratio values fell below the 0.90 threshold [104], indicating that all measures exhibited construct validity through discriminant validity.

According to the Fornell and Larcker [105] criterion for assessing variable relationships, as presented in

Table 8. Discriminant validity assessment using the Fornell–Larcker criterion.

Constructs	CPA	CVA	CVO	COM	PSR	PU	PEOU	ATT	REV	CON
CPA	0.924
CVA	0.655	0.945
CVO	0.621	0.639	0.898
COM	0.648	0.668	0.588	0.906
PSR	0.607	0.576	0.535	0.608	0.919
PU	0.450	0.454	0.545	0.529	0.465	0.890
PEOU	0.638	0.550	0.565	0.529	0.450	0.289	0.906
ATT	0.541	0.542	0.632	0.592	0.446	0.595	0.485	0.881
REV	0.616	0.673	0.646	0.656	0.546	0.463	0.540	0.596	0.924
CON	0.574	0.605	0.668	0.592	0.516	0.527	0.525	0.683	0.679	0.903

Note: Content passive (CPA), content valence (CVA), content volume (CVO), compatibility (COM), privacy and security risk (PSR), perceived usefulness (PU), perceived ease of use (PEOU), attitudes toward (ATT), revisit intention (REV), and continued use of IMT (CON).

, the square roots of the AVE values for each variable (highlighted in bold) exceed the values in the corresponding horizontal and vertical rows. This finding confirms that the variables exhibit discriminant validity, thus accepting their application in structural equation modeling analysis.

5.3. The Structural Model

The model fit was evaluated before testing the significance of the path coefficients in the structural model, in accordance with the criteria established by [104,106]. The Stone–Geisser’s Q² values were assessed through the blindfolding procedure, revealing that all constructs exhibited Q² values exceeding zero—ATT = 0.542, REV = 0.453, PU = 0.198, and CON = 0.490—thereby confirming the model’s predictive relevance. The standardized root mean square residual (SRMR) was also analyzed. The established criterion indicates that the SRMR must be below 0.08. The SRMR value obtained was 0.037, suggesting a satisfactory model fit. Additionally, Chin [107] suggested that the coefficient value (R2) must exceed 0.1 to confirm the model fit. The results showed that the endogenous variable “ATT” has an R2 value of 0.578, while “REV” has an R2 value of 0.453, “CON” has an R2 value of 0.408, and “PU” has an R2 value of 0.203. All four R2 values exceeded the recommended threshold, indicating that the study model adequately represented the collected data.

After meeting the fit criteria, a structural model analysis was performed using the Bootstrap method with 5000 samples. This approach sought to improve the assessment of the relationships among latent variables and to evaluate multicollinearity issues using the Variance Inflation Factor (VIF). The results of the analysis demonstrated that the values for all causal variables were below the 5.0 threshold set by Grewal et al. [108], indicating a lack of multicollinearity in this model.

The significance test for the model’s path required an assessment of the path coefficients, the statistical significance (p-value), and the t-values based on established criteria. A t-value exceeding 1.96 indicates significance at the 5% level, exceeding 2.58 indicates significance at the 1% level, and exceeding 3.29 indicates significance at the 0.1% level. The results of this study found that the following hypotheses were accepted: H1c: CVO influences ATT at a significance level of 0.05 (β = 0.124, t = 2.144); H2a: COM influences ATT at a significance level of 0.001 (β = 0.450, t = 8.567); H2b: PSR influences ATT at a significance level of 0.01 (β = 0.216, t = 2.635); H3a: PSR influences ATT at the 0.05 significance level (β = 0.173, t = 2.396); H4: PU influences ATT at the 0.001 significance level (β = 0.267, t = 3.310); H5: ATT influences REV at the 0.001 significance level (β = 0.267, t = 3.310); and H6: ATT influences CON at the 0.001 significance level (β = 0.673, t = 18.958). The results are presented in

Table 9. Hypothesis testing.

Hypotheses	Relationship	Coefficient (β)	t-Values	p-Values	VIF	Results
H1a	Content passive (CPA) → attitudes toward (ATT)	0.016	0.317	0.751	1.898	Not supported
H1b	Content valence (CVA) → attitudes toward (ATT)	0.102	1.564	0.118	1.891	Not supported
H1c	Content volume (CVO) → attitudes toward (ATT)	0.124	2.144	0.032 *	1.898	Supported
H2a	Compatibility (COM) → attitudes toward (ATT)	0.450	8.567	0.000 ***	1.000	Supported
H2b	Privacy and security risk (PSR) → attitudes toward (ATT)	0.216	2.635	0.008 **	2.481	Supported
H3a	Perceived ease of use (PEOU) → attitudes toward (ATT)	0.173	2.396	0.017 *	2.317	Supported
H3b	Perceived ease of use (PEOU) → perceived usefulness (PU)	0.035	0.541	0.589	2.378	Not supported
H4	Perceived usefulness (PU) → attitudes toward (ATT)	0.267	3.310	0.001 ***	2.356	Supported
H5	Attitudes toward (ATT) → revisit intention (REV)	0.639	15.662	0.000 ***	1.000	Supported
H6	Attitudes toward (ATT) → continued use of IMT (CON)	0.673	18.958	0.000 ***	1.000	Supported

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

and Figure 4.

6. Discussion and Implications

IMT significantly contributes to the tourism sector by improving travelers’ experiences and providing users with convenient and immersive access to destinations before their visit. Previous research has demonstrated that immersive marketing techniques influence tourists’ behavioral intentions; however, there is a lack of comprehensive studies specifically addressing agritourism, especially post-immersive experiences. This research investigates the characteristics of storytelling content in IMT and the user traits linked to innovation resistance and risk perception that affect user behavior, specifically concerning revisit intention and continued intention to use IMT post-experience.

6.1. Theoretical Contributions

This research presents a conceptual model that combines the Diffusion of Innovation (DOI) theoretical framework with the technology acceptance model (TAM) and the psychological factors associated with innovation resistance. This model aims to clarify the decision-making process for IMT users in the post-experience phase of agritourism.

This study investigates three research questions related to post-experience agritourism via IMT, with RQ1 focusing on the influence of narrative content in IMT on user behavior. The findings indicate that the frequency of exposure to narrative content aiming to promote participation in agritourism activities directly influences users’ attitudes (H1c). Regular interaction with immersive media that includes narrative content about destination activities improves users’ positive emotional responses and memory formation concerning a location [7,77,109]. This subsequently promotes positive behavioral intentions during the post-experience phase, including the intentions to revisit or utilize IMT further. These findings contribute to the understanding of storytelling’s role in IMT, which can enhance the user experience for novices and maintain the interest and satisfaction of experienced users [78,79]. This subsequently influences destination engagement, loyalty, and the technologies utilized.

The analysis did not indicate a relationship between content passive (CPS) and content valence (CVL) concerning travelers’ attitudes towards (ATT) the use of IMT in agritourism during the post-experience phase (H1a, H1b). These findings provide empirical evidence that the clarity of content delivered through IMT—comprising images, videos, virtual characters, and soundscape design—does not influence the perceptions of users with prior experience. This contrasts with previous research indicating that these elements might increase entertainment value and improve the immersive experience for users [75,76]. The analysis of CVL, which includes historical narratives, community environments, and activities across different dimensions of the destination, indicated no effect on the formation of positive attitudes or behavioral intentions post-experience. These findings suggest that tourists with previous experience in a destination or travel style demonstrate diminished responsiveness to immersive content that relies solely on technical features. Attention should focus on developing narrative content linked to personal motivations or emotional contexts, alongside contextual factors that affect immersion perception. These include individual interests in destination activities, the popularity of locations or related activities, multisensory experiences, and connections to nearby places [97,103].

The investigation of RQ2 aimed to analyze the influence of IMT’s characteristics on the resistance to technology adoption behavior after an agritourism experience (H2a, H2b). The results of this study were validated. This study provides initial empirical evidence regarding the influence of compatibility (COM) and privacy and security risk (PSR) factors on the likelihood of accepting or rejecting the future adoption of IMT. The analysis identified COM as a critical determinant of user behavior. Individuals who perceived IMT as congruent with their personal behavior and lifestyles exhibited a greater likelihood of acceptance and intentions to continue its use in the future. In contrast, users who perceive a conflict between the technology and their lifestyle—stemming from issues like inconvenient accessibility, perceived costs, or misalignment with daily activities—may demonstrate resistance or a tendency to reject its adoption [70,85,88].

This study demonstrates that PSR influences later behaviors associated with the use of IMT. Many users have expressed concerns about data privacy, as IMT may collect biometric data or specific usage behaviors from devices connected to larger platforms, such as VR/AR headsets or motion trackers [92,110]. Additionally, users’ perceived parasocial relationships from previous experiences at a destination may influence their intention to revisit, a factor that should be considered alongside the development of tourism-supporting technologies. The findings of this section support innovation resistance theory, demonstrating that behavioral compatibility and privacy risk management are essential strategies for reducing user hesitation and promoting ongoing engagement with IMT in agritourism.

RQ3 sought to examine the relationship between psychological characteristics, specifically perceived ease of use (PEOU), perceived usefulness (PU), and users’ “attitudes towards” (ATT) that influence their intention to revisit a destination and their intention to utilize IMT post-experience. The analysis results validated that PEOU and PU significantly influence ATT, as hypothesized (H3a, H4). Users who view IMT as user-friendly and beneficial are likely to cultivate favorable attitudes toward it. This attitude is a significant factor that directly influences positive behaviors in the post-experience phase, specifically the intention to revisit the original destination and the intention to continue utilizing IMT. Specifically, ATT demonstrated the most significant influence on the intention to continue using IMT, with a β value of 0.673. This finding underscores the important influence of psychological characteristics, specifically PEOU and PU, on ATT and their subsequent indirect effect on travelers’ behavioral intentions during the post-experience phase. This is particularly relevant in the context of employing IMT to enhance sustainable agritourism [18,44,55,111].

6.2. Managerial Implications

Tourists seeking innovative travel experiences exhibit considerable demand for smart destinations, especially in the realms of sustainability and revisit marketing [44]. These findings suggest that tourists’ decision-making process concerning the use of immersive media technology is influenced by the interaction of the immersive storytelling content’s characteristics, IMT’s attributes, and the tourists’ psychological traits. Users demonstrate an increased tendency to engage with IMT and revisit a destination when they experience positive emotions and favorable memories, often elicited by the consistent presentation of immersive content that conveys narratives associated with the activities at the destination. The findings suggest that virtual experiences can improve traveler loyalty by encouraging a connection to revisiting a destination. IMT can simulate virtual experiences in various destinations. However, experienced travelers express concerns about the compatibility of these technologies with their behaviors and lifestyles, as well as worries regarding data privacy when using devices and platforms related to IMT.

Developers of immersive hardware and software must address the following considerations: (1) Develop technologies that align with user behaviors and lifestyles: IMT must be adaptable and fulfill travelers’ expectations by accommodating various devices, offering straightforward usability, and minimizing user burden. (2) Address privacy and security concerns: The reluctance to adopt IMT over the long term is partly due to apprehensions regarding personal data and biometrics. Therefore, platform developers must clarify the data management practices and implement strong security measures. (3) Decrease the expenses associated with technology access: Some travelers may have limited resources or devices. The design of immersive platforms that ensure accessibility at affordable prices, while providing shared or trial models, can improve sustainability and expand the user base. (4) Create narrative-driven immersive content: Highlight the creation of content that nurtures emotional connections and sustainability through travelers’ genuine experiences in a destination, thus enhancing place attachment and encouraging enduring loyalty to a destination.

Agritourism agents and destination marketers can apply these findings to improving destination marketing through various strategies. (1) Market agritourism activities in structured formats that accommodate varying levels of participation, allowing travelers to customize their experiences. This includes passive observation of agricultural practices or community lifestyles, interactive participation in selected activities, and commercial engagement through support of locally produced goods [6,33,34]. (2) Design immersive experiences that engage emotions through storytelling techniques related to farmers’ lives, community lifestyles, or agricultural production processes, complemented by the creation of digital content that generates positive emotional responses. Furthermore, promoting community and farmer or local community engagement may be achieved by involving them in co-designing content and serving as storytellers through IMT, thereby enhancing authenticity and cultural connection. (3) Utilize IMT as a post-travel engagement tool to foster loyalty and encourage repeat visits, demonstrated by virtual tours that generate memories and motivate users to return to a destination. Agritourism agents and IMT software developers can collaboratively address these issues to improve their engagement in both real and virtual environments, thereby fostering emotional and motivational technology use and encouraging revisits to a destination.

Government agencies and tourism policymakers must prioritize the following policy issues: (1) Enhancing local personnel capabilities: Policies must prioritize the development of knowledge and skills in IMT among local entrepreneurs, enabling their involvement in the design and delivery of activities aligned with quality agritourism contexts. (2) Encouraging involvement from the community: Facilitate community ownership of narratives and collaborative content creation that authentically represents the identity and values of the communities. (3) Managing data and privacy: It is essential to establish stringent standards for the protection of user data in IMT, particularly when biological or behavioral data is collected, to foster trust among travelers who have privacy concerns. (4) Designing policies to enhance accessibility to technology: Relevant agencies should develop guidelines to reduce the disparities in access to IMT, including the provision of digital devices at tourist service centers or major attractions for interested individuals to utilize.

6.3. Limitations and Future Research

This study’s primary strength lies in its examination of IMT within the specific context of agritourism, a research area that has been relatively underexplored. While IMTs have been extensively examined within the broader tourism sector, their relationship to local identity and sustainable tourism, particularly in agritourism, remains an area with insufficient empirical data. This research is thus significant in offering both theoretical and practical insights that can facilitate the formulation of effective marketing strategies and experience design. Nonetheless, this research has several limitations that should be acknowledged in future studies, as outlined below:

(1)

Limitations of technology and platforms: This study did not concentrate on a particular technology or platform. While IMT encompasses various tools, including AR, VR, and MR, each possessing distinct functional attributes and experiential levels, future research concentrating on each type of technology can yield deeper knowledge into user behavior.

(2)

Limitations of the study duration: This research employed a cross-sectional study methodology which sought to gather data within a specific timeframe. This design cannot elucidate the long-term alterations in tourists’ behaviors or attitudes regarding IMT. Consequently, subsequent research ought to employ a longitudinal study design to perpetually examine behavior and usage intentions.

(3)

Limitations of demographic moderators: While this study concentrated on user behavior, it lacked an examination of the influence of personal factors such as gender, age, or educational attainment, which may significantly affect attitudes and accessibility to IMT. The sample exhibited a lack of diversity, as the respondents were exclusively from a single country and predominantly aged between 20 and 25 years. Neglecting these factors may have led to the study results inadequately representing the sample’s diversity.

(4)

Limitations of the data sources (self-reported bias): The data utilized to examine tourist behavior originated from a self-reported survey, which is prone to evaluative bias, including memory inaccuracies or social desirability bias, potentially leading to a misrepresentation of the actual experience.

7. Conclusions

This study is the first to utilize IMT in the context of agritourism, with a particular focus on the post-experience behavior of travelers. This study presents a conceptual model that combines the DOI theoretical framework with the TAM and the psychological factors associated with innovation resistance to clarify the decision-making process of users of IMT. The findings indicate that the frequency of exposure to storytelling content, PEOU, and PU directly influence travelers’ attitudes, subsequently impacting their intention to continue using IMT and their intention to revisit a destination. COM and PSR significantly affect the willingness to persist with IMT and the decision to revisit to a destination.