Perceived Value and Consumer Intention to Use Smart Farm Restaurant Systems in Al Ahsa, Saudi Arabia: A Value–Attitude–Behavior Model

Abstract

The adoption of smart farm tourism in agritourism is primarily determined by consumer acceptance, yet limited research has assessed the psychological determinants of the intention to apply smart farm systems. The current study aimed to explore the impact of perceived value on consumer’s attitudes and intentions to use indoor smart farm restaurant systems in Al-Ahsa, Saudi Arabia, using a value–attitude–behavior framework. A cross-sectional survey was conducted among 110 participants, and partial least squares structural equation modeling (PLS-SEM) was employed to assess the hypothesized relationships. The final measurement model showed acceptable levels of convergent and discriminant validity. The results of the structural model demonstrated that the perceived value significantly predicted both attitudes (β = 0.687, p < 0.001) and intentions to use (β = 0.308, p = 0.002). Attitudes also had a direct positive effect on the intention to use (β = 0.557, p = 0.001) and significantly mediated the relationship between perceived value and intention (indirect effect β = 0.383, p = 0.003), accounting for 55.4% of the total effect. These results highlight the positive effect of perceived value in shaping favorable consumer attitudes and behavioral intentions towards the adoption of smart farm restaurant systems. As a consequence, targeting consumer perceived values would influence the behavioral attributes of consumers and support sustainable agritourism innovations in Saudi Arabia and beyond.

1. Introduction

Agriculture’s mechanization is being transformed by technological innovations. In response to economic, ecological, and social challenges, smart farming technologies (SFT) have emerged with an aim to improve farming and to support the process of developing agriculture sustainably worldwide (Kernecker et al., 2019). The concept of “Smart Farming” increases agricultural output and efficiency by automatically adjusting environmental factors such as lighting, water supply, and air conditioning (Joo & Hwang, 2023; Safarov et al., 2024). Farming systems that are smart are regarded as the most sustainable type of farming, since they provide great productivity and labor efficiency, while remaining unaffected by pesticides, natural catastrophes, or any other external force (Joo et al., 2022). Unlike traditional farming, which causes environmental pollution, smart farm systems are ecologically friendly and controlled in a sustainable manner (Joo & Hwang, 2025; Joo et al., 2022). With smart farming, more food can be produced sustainably while maintaining high levels of safety and quality (Joo & Hwang, 2025). Furthermore, smart farming contributes to SDG 2 of zero hunger by enabling a greener, more sustainable, and more profitable farming system (Musa & Basir, 2021). This smart method of farming enables sustainable crop development and management, lowering carbon footprints (Shin & Hwang, 2024).

For technological innovations to thrive in smart farming, ensuring their acceptance and their benefits to support the environment is essential, according to Schuka and Heise (Schukat & Heise, 2021). Nevertheless, consumers should not only recognize the potential advantages of smart technologies in agriculture but also be open to the concrete actions taken by farmers to implement and utilize smart farming solutions. In addition, the consumer perceived value of smart farming, as well as the intention to use it, has been linked with multiple determinants, including psychological factors. According to Joo and Hwang, the extent to which the individual is connected to the natural environment and their awareness of environmental issues and their risks and impact on health food quality, as well as the feeling of responsibility to protect the environment, can all impact the intention to use a smart farming system (Joo & Hwang, 2023). Furthermore, a prior knowledge of smart farms influences how fruits and vegetables grown in smart farms are perceived, making them more valuable than those grown in traditional facilities, associating them with a higher health value and leading to higher intentions to pay for fruits and vegetables from smart farms (Kim & Lee, 2022). It is also important to note that smart farming systems do not only impact the perceived value among consumers and farmers. When integrated practically and conceptually with tourism and hospitality, technology-driven farming innovation can be transformed into a tourism experience enhanced by quality, sustainability, and authenticity (Nguyen et al., 2024; Zhang et al., 2019).

In keeping with the global shift, Saudi Arabia has made significant advancements in smart technology, which is employed in all sectors as part of its shift to smart cities (Doheim et al., 2019). One of the primary objectives of the Saudi Vision is to attain a sustainable environment, aiming to protect natural resources and the environment, as noted by Doheim, Farag, and Badawi (Doheim et al., 2019). As with other domains, Saudi Arabia has started the adoption of IoT technologies to improve farming practices, as well as to support the efforts toward water and food security, as well as environmental protection (Jabbari et al., 2023). While there are studies in Saudi Arabia that assesses the perceived value of smart technology utilization in farming, the majority focused on the farmers’ perspectives rather than the consumer. In addition, the complexity of Saudi culture, as well as the presence of other issues like digital literacy and technophobia, makes the assessment of perceived value very relevant in the Saudi context.

In order to understand this relationship, the aim of this study is to assess the relationship between the consumer’s perceived value of smart farms and their attitude and intention of use, while also exploring the possible mediators in this relationship.

2. Literature Review and Hypothesis

2.1. Smart Farming Technology and Agricultural Transformation

Agriculture is now encountering significant difficulties in securing food for a worldwide growing population and dealing with climate change, which brings about severe weather conditions. At the same time, the agricultural industry is often challenged with a number of other issues related to crop quality and land availability, technical limitations, and others (Ena & Siewa, 2022). As the farming industry adjusts to climate change and shifts towards sustainability and achieving food security, the shift toward smart farming will be crucial in promoting sustainable agriculture (Ena & Siewa, 2022; Schukat & Heise, 2021). In the past few years, the main focus of smart farming (also known as digital farming and digital agriculture) has been largely centered on boosting productivity and efficiency, according to Eastwood et al. (Eastwood et al., 2017). Moreover, smart farming has the potential to address societal concerns about agriculture, such as food origins and traceability; therefore, scientists and policymakers are increasingly looking to smart farming as a technical answer that gives a path to sustainable agricultural growth (Eastwood et al., 2017). However, Fraser (Fraser, 2022) argues that there is a risk of moving beyond practical implementation when focusing solely on technological innovation, suggesting that smart farming initiatives must remain grounded in real-world agricultural needs rather than becoming overly focused on data collection without tangible benefits.

The integration of artificial intelligence and smart logistics systems has emerged as a crucial component in enhancing the farmer–customer corridor in the smart agriculture sector (Ramirez-Asis et al., 2022). Moreover, this technological evolution has opened doors for hybrid models of agriculture, combined with tourism/hospitality experiences. The technology advancements offered will have a positive impact not only on operational efficiency but also on new direct farmer–consumer relationships, especially within the scope of farm tourism (including agritourism) experiences. Agritourism and smart farming will become more intertwined as technology advances, providing enriched experiences among farmers, consumers, and tourists, and supporting sustainable agriculture. Moreover, smart farming technologies have demonstrated an ability to solve many of the challenges agriculture faces all at once, including resource optimization, supply chain management, etc. The technology change in agriculture is not confined to agriculture alone but is also being developed in new and different ways, such as smart portable gardens and indoor farming. For instance, Widianto’s research on smart portable gardens in Indonesia shows how technological innovation can create new market segments and consumer experiences within agriculture (Kasornbua & Pinsame, 2019).

2.2. Consumer Behavior and Technology Acceptance in Agricultural Contexts

In addition to having appropriate and sufficient resources and technology, working on the consumer perspective when it comes to smart farm technology plays a crucial role in creating smart agriculture and motivates consumers to rely on smart farm products as a choice rather than traditional farms (Chuang et al., 2020). According to Zhuang W, Luo X, and Riaz, marketers are required to pay attention to consumer preferences and the factors affecting consumers’ decision-making processes (Zhuang et al., 2021). Consumers’ purchase intentions are the probability of the willingness to purchase, and can be described as variables to determine how they make decisions before making a purchase, how they repeat their purchases, and how they repurchase a product in the future (Kasornbua & Pinsame, 2019). According to Kasornbua and Pinsame, multiple factors such as cultural dimensions, brand image, perceived quality, and word of mouth can influence purchase intentions (Kasornbua & Pinsame, 2019). Because of the complexity of consumer behaviors in both the agricultural sector and in tourism contexts, it is evident that decision-making processes would call for a multidimensional understanding. Tajeddini et al. (Tajeddini et al., 2021) used value–attitude–behavior and a theory of planned behavior frameworks to explore visitors’ accommodation choices and the connection between perceived value, attitude, and behavioral intentions. In a different context, Sadiq et al. (Sadiq et al., 2022) explored eco-friendly hotel stays using value–attitude–behavior theory, and demonstrated how environmental attitudes mediate between values and sustainable behavior choices. Another study in Indonesia by Widianto assessed customer intentions to buy from smart portable gardens and found that customer attitudes, product pricing, perceived behavioral control, and environmentally conscious purchasing were all associated with the consumer purchase intention (Widianto & Nita, 2022). This point was also supported by Idris and Zulkifli in their argument, which asserted that technology is more effective in increasing the quantity of goods produced than traditional methods, but that there are several factors that play into this which hinder the full use of smart farms, including individuals’ knowledge, attitudes, and practices (Idris & Zulkifli, 2024).

In another recent study, Joo and Hwang emphasized that a diverse set of psychological factors goes a long way toward impacting individual pro-environmental behavior and the process of decision-making (Joo & Hwang, 2023). For instance, engaging natural elements and technological innovations can shape the consumer’s actual perceptions, since consumers often associate naturalness with quality and authenticity in agricultural products. Another experiment study measured the perceptions of naturalness in designed urban green spaces, where the participants perceived “nature” with a higher value as being biodiverse, attractive, and restorative (Hoyle et al., 2019). As a result, nature’s and naturalness’ roles in planning, design, and management, as well as their impact on product value, are underlined (Hoyle et al., 2019).

Based on the previous arguments regarding the importance of naturalness in consumer perceptions of smart farming technologies, we hypothesize the following:

H1. 

Perceived naturalness positively influences consumers’ perceived value of smart farm restaurant experiences.

The importance of an internal environmental locus of control has been highlighted in the context of indoor smart farm restaurants, where individual psychological factors significantly influence behavioral intentions (Joo et al., 2023). This research demonstrates that consumers’ sense of personal control over environmental outcomes plays a crucial role in their willingness to engage with smart farming technologies and experiences. The psychological benefits derived from engaging with environmentally conscious agricultural practices appear to create additional value for consumers beyond the tangible product benefits. Based on the previous arguments regarding psychological factors and their impact on consumer behavior in smart farming contexts, we hypothesize the following:

H2. 

Psychological benefits positively influence consumers’ perceived value of smart farm restaurant experiences.

The environmental consciousness and sustainable purchasing behaviors of consumers have become increasingly important factors in agricultural technology adoption. Research indicates that environmentally conscious purchasing patterns are strongly associated with consumer purchase intentions in smart farming contexts (Widianto & Nita, 2022). This suggests that consumers who prioritize environmental considerations may derive a greater psychological satisfaction from engaging with smart farm technologies, particularly when these technologies demonstrate clear environmental benefits. Consumer well-being considerations, particularly health-related benefits, have emerged as significant drivers of perceived value in agricultural technology contexts. Based on the previous arguments about environmental consciousness and its relationship to consumer well-being and satisfaction, we hypothesize the following:

H3. 

Healthy well-being positively influences consumers’ perceived value of smart farm restaurant experiences.

Consumer attitudes toward technology pricing and perceived behavioral control also play significant roles in purchase intention formation. The study by Widianto revealed that customer attitudes, product pricing, perceived behavioral control, and environmentally conscious purchasing were all associated with consumer purchase intentions (Widianto & Nita, 2022). The enjoyment factor emerges as a critical component when consumers evaluate their experiences with innovative agricultural technologies, particularly in hospitality and tourism contexts. Research suggests that experiential satisfaction and enjoyment significantly contribute to the overall perceived value in technology-enhanced agricultural settings. Based on the previous arguments regarding the role of experiential factors and consumer satisfaction in technology adoption, we hypothesize the following:

H4. 

Enjoyment positively influences consumers’ perceived value of smart farm restaurant experiences.

However, technology adoption is not without its challenges and perceived risks. According to the study by Wilmes, Waldhof, and Breunig, a higher utilization of technology by a farm was associated with less willingness to buy products by consumers (Wilmes et al., 2022). This finding suggests that excessive technological interventions may create concerns about product authenticity, safety, or other risk-related factors among consumers. Consumer risk perceptions can significantly impact their overall evaluation of smart farming technologies and their willingness to engage with these systems. Based on the previous arguments regarding technology-related concerns and their impact on consumer acceptance, we hypothesize the following:

H5. 

Perceived risk negatively influences consumers’ perceived value of smart farm restaurant experiences.

2.3. Perceived Value, Attitudes, and Purchase Intentions in Farm Tourism

Although multiple factors can impact the willingness to purchase, the attitude toward technology value in agriculture, which is a result of individuals’ perceptions, is one of the most important factors that must be considered, as it can greatly impact customers’ purchase intentions. According to the study by Wilmes, Waldhof, and Breunig, a higher utilization of technology by a farm was associated with less willingness to buy products by consumers (Wilmes et al., 2022). However, the results from the same study found that stating the environmental concerns and justifying the use of technology to protect the environment were associated with a higher intention to buy products (Wilmes et al., 2022). The researchers explained that, due to the interconnected nature of agriculture and its environment, the technical features of digital agriculture may be affected by concerns about the environment (Wilmes et al., 2022).

The evolution of farm tourism has created new contexts for understanding consumer behavior and perceived value. Busby and Rendle (Busby & Rendle, 2000) termed the evolution from traditional tourism on farms to specialized farm tourism as a progressive change in the agricultural industry, and demonstrated that farms have moved from being incidental tourist hosts to providing a specialized service to consumers. This has important implications for how consumers value farm-based experiences and the extent to which they will engage with technologically enabled agriculture.

Thus, our exploration of the contemporary literature surrounding farm tourism illustrates an increasingly complex landscape in the motivations and challenges facing consumers. Yamagishi et al. (Yamagishi et al., 2021) examined the future of farm tourism and established the opportunities and challenges influencing consumer adoption and satisfaction. Their findings highlight that deliberate planning and being attuned to consumer preferences are instrumental in the success of farm-based tourism development. Nematpour and Khodadadi’s (Nematpour & Khodadadi, 2020) conclusions related to the drivers of farm tourism have demonstrated the revenue-generating capability of farm tourism for the regions, with the value closely affecting locals, even if their revenue subsequently only benefits the individual at the time. The consumer value in and of itself creates socio-economic benefits beyond the individual consumer for the local community (or region) in which the farm-based tourism ventures engage and operate.

Affecting consumer perceptions and intentions, the performance characteristics of agritourism operations are key factors in determining the level of consumer satisfaction and return intentions. Choo and Park (Choo & Park, 2020) focused on the following farm characteristics associated with performance in agritourism in South Korea, showing that the operational characteristics of a farm are factors affecting consumer satisfaction intentions. Yu and Spencer (Yu & Spencer, 2020) described some of the motivations and difficulties farmers face involved in farm tourism (farmers’ transformations and adaptations), as well as how these transformations and adaptations have implications for consumer experience quality.

It is important to underline also that consumer perception is considered as the core process that leads to decisions, as the way consumers perceive information shapes how they collect, interpret, and respond to it, and ultimately affects their choices and actions. Perceived value, an important, complex, and multidimensional concept in a consumer’s intention to use a product or service, can be defined as the result of an interaction between a consumer and a product; and it is subjective, owing to its comparative, personal, and situational nature, according to Sánchez-Fernández and Iniesta-Bonillo (Sánchez-Fernández & Iniesta-Bonillo, 2007). Wilmes, Waldhof, and Breunig further explain that values serve as principles that guide behavior, and, if the same values are shared between the supplier and the consumer, then commercializing technologies will be more effective (Wilmes et al., 2022).

Based on the previous arguments regarding the relationship between perceived value and attitude formation in technology acceptance, we hypothesize the following:

H6. 

Perceived value positively influences consumers’ attitude toward using smart farm restaurants.

In order to gain a competitive edge, perception of value is a key measure. In a study investigating consumer perceptions of smart farm vegetables and low-carbon labels, participants’ attitudes and willingness to pay extra for smart farm vegetables were achieved when they perceived that the food from smart farms was natural, healthy, and of high quality (Shin & Hwang, 2024). Another study by Shin and Hwang argues that both environmental and health benefits were positively affected by perceived naturalness, but environmental benefits were more profound. This in turn led to more intentions to buy smart farm vegetables (Shin & Hwang, 2024). The researchers added that attitudes were influenced more by environmental benefits, whereas the willingness to pay premiums was impacted more by health benefits (Shin & Hwang, 2024). This research establishes a direct connection between perceived value and consumer intentions to engage with smart farming technologies and experiences. Based on the previous arguments about the direct relationship between perceived value and behavioral intentions in smart farming contexts, we hypothesize the following:

H7. 

Perceived value positively influences consumers’ intention to use smart farm restaurant systems.

The level of knowledge about technology and smart farms can also play a role in the attitudes of consumers and their intentions to use. In a study that assessed the perception of smart farms in Europe, the researchers reported that the benefits of smart agriculture technologies are widely recognized by those with a higher education, and future trends of the technology are optimistic among this group, giving them more of an intention to adopt the new technology (Kernecker et al., 2019). When the technology acceptance model was used to assess the factors that affect the intention to use smart technologies in agriculture, Sara et al., reported that the perceived benefits, perceived usefulness, and attitudes toward the use of smart digital technology all had statistically significant effects on the intention of use (Widianto & Nita, 2022). This research establishes a fundamental link between consumer attitudes and their behavioral intentions regarding smart farming technology adoption. Based on the previous arguments regarding attitude–intention relationships in technology adoption, we hypothesize the following:

H8. 

Attitudes positively influence consumers’ intention to use smart farm restaurant systems.

The research also demonstrates that consumer attitudes serve as critical mediators in the decision-making process. When it comes to technology, Wiprayoga, Gede, and Suasana explained that attitude can be defined as the level of interest users have in using new technologies (Wiprayoga & Widagda, 2023). An individual’s behavioral attitude can also be measured based on their positive or negative feelings when performing the desired behavior (Wiprayoga & Widagda, 2023). The mediation effect of attitudes in the value–intention relationship represents a crucial mechanism in consumer decision-making processes. Nevertheless, attitude’s role as a mediator varies among different perceptions. According to Wiprayoga, Gede, and Suasana (Wiprayoga & Widagda, 2023), attitudes toward using do not influence behavioral intentions to use in response to perceived usefulness, but they influences behaviors in response to perceived ease of use. When it comes to perceived value, Zheng, Wang, and Yu (Zheng et al., 2024) pointed out that perceived value influences intentions both directly and indirectly through attitude formation, indicating that attitudes serve as an important mediating variable in the consumer decision-making process. Similarly, Zhuang et al. (Zhuang et al., 2010) argue that perceived value has a significant positive impact on purchasing intentions, as well as a significant indirect influence on purchasing intentions that is mediated via attitudes. Based on the previous arguments about the mediating role of attitudes in the value–intention relationship, we hypothesize the following:

H9. 

Attitudes mediate the relationship between perceived value and intentions to use smart farm restaurant systems.

Building on the previous observations, the hypotheses of the current study can be illustrated in the Figure 1.

3. Materials and Methods

3.1. Sampling Selection Criteria and Participant Recruitment

This study targeted adult consumers aged 18 and over who lived in Al-Ahsa, Saudi Arabia, and who had experience eating in general restaurants. Al-Ahsa is strategically important as an agricultural region in Saudi Arabia and has the capacity for the implementation of smart farming technology. In this study, “general restaurants” refers to conventional food service establishments (i.e., dining out) that participants observed or potentially dined at. Examples would include, but are not limited to, casual and family dining, cafes, and fast-food venues in the Al-Ahsa region, excluding fine dining/specialty venues. The terms “smart farm restaurant” or “smart agriculture restaurant,” as utilized in the study, indicate a concept restaurant model which incorporates technologically advanced smart farming practices (e.g., vertical farming, hydroponics, automated climate control, and IoT-enabled agriculture) within a restaurant and/or establishment in such a way which allows consumers to observe and interact with the food production system. This notion of a “smart farm restaurant” (or a smart agriculture restaurant) would not include merely a single restaurant space; however, it instead would refer to the notion of a developing subset of food service establishments which allow guests to enjoy dining on a sustainable, locally sourced agricultural venue in combination with food service experiences. To be eligible for participation in the study, all participants were required to have had general restaurant dining experience at least once per month to account for a generic commonality in the general socio-environment for eating out at a restaurant, which assisted in measuring the participants’ perceptions, attitudes, and intentions toward a smart farm restaurant concept specifically. This methodological framework allowed the assessment of consumer acceptance of this novel (to them) restaurant model, amongst consumers who had previous dining habits and expressed a prior patronage of restaurants.

The sampling frame included individuals with different demographics to ensure representative sampling of the local consumer population. The inclusion criteria were the following: aged 18 years or over, living in Al-Ahsa, ate in general restaurants no less than once per calendar month, could read and understand Arabic or English, and were willing to provide informed consent for participation. The exclusion criteria were the following: aged under 18 years of age, not living in Al-Ahsa region, had no experience eating in restaurants, had no ability to comprehend survey questions due to language barriers, and did not participate as workers in the agricultural or restaurant industry to maintain bias.

An appropriate sample size was calculated with G*Power 3.1.9.7 software based on medium effect size (f² = 0.15), alpha level (α = 0.05), and power level (1-β = 0.80). As a result, the minimum sample size was calculated to be 68. To account for potential respondents with incomplete responses, as well as to sufficiently account for effect sizes to provide adequate statistical power for PLS-SEM analysis, we determined the proposed sample size for this study would be targeted at 110 participants (applying the “10 times rule” relating to a minimum sample size of 10 times number of structural paths directed at any construct in the model (Hair et al., 2011, 2021)). Although convenience sampling is less reliable and hinders generalizability due to possible sampling bias, this sampling method was essential to overcome practical constraints and accessibility considerations, and to ensure gender balance and diversity among participants. And, even though the sample size is modest, it is adequate for exploratory analysis, providing a balanced perspective on consumer perceptions of smart farm restaurant systems in Saudi Arabia. Participants were recruited via several methods, including social media (e.g., WhatsApp, Twitter, Instagram), community recreation centers, mall shopping, and university campuses in Al-Ahsa. In order to diversify our sample, we completed recruitment in a variety of neighborhoods. We also varied our recruitment time periods to acquire a broad variety of demographics.

3.2. Construct Measures and Instrument Development

The survey instrument was developed based on an extensive literature review and validated scales of technology acceptance, consumer behavior, and agricultural tourism studies, consisting of nine domains: one demographic section and eight psychometric constructs measured on a 5-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The constructs included perceived naturalness (three items adapted from natural food perception studies), psychological benefits (three items from environmental psychology literature measuring satisfaction from environmentally conscious practices), healthy well-being (one item focusing on health benefits), enjoyment (three items from experiential consumption literature measuring hedonic aspects), perceived risk (one item addressing quality concerns), perceived value (one item measuring overall value perception), attitude (two items from theory of planned behavior), and intention to use (two items from behavioral intention literature). Content validity was established through expert review by three specialists in consumer behavior, agricultural technology, and tourism management, whose feedback informed item refinement and cultural adaptation for the Saudi Arabian context, followed by a pilot study with 15 participants that led to minor modifications for improved clarity and cultural relevance.

3.3. Data Collection Procedures

Data collection was completed over a four-week period in both online and paper formats to accommodate different preferences and levels of access to technology available to the participants. The online version (using the Qualtrics platform) and the printed versions were made available for distribution locations throughout Al-Ahsa. Before beginning data collection, ethical approval was obtained from the institutional review board, and all participants were made aware of and provided with detailed information about the study purpose, procedures, measures of confidentiality, and their right to withdraw from the study at any time without penalty. Informed consent was obtained from all participants before proceeding with the survey, and the study followed the principles of the Declaration of Helsinki when involving human participants.

For online data collection, participants accessed the survey using secure links that were distributed via participant social media groups and by email networks. A researcher monitored which IP address had submitted the survey in order to eliminate the potential for multiple submissions from the same device, while also monitoring completion time and timing out participants if completion time was extreme to identify careless respondents. The participants were required to complete all items before submitting. Trained research assistants completed our paper data collection by administering surveys at locations such as shopping centers, universities, and community gathering spaces. All participants received the same standardized instructions to support consistency in survey administration, and completed surveys were collected directly and secured. Data entry from paper surveys was conducted by two different researchers who cross-validated the data for accuracy and to reduce typing errors.

Several quality control procedures were put in place to maintain data integrity during the collection process. Attention check items were placed throughout the survey to flag careless respondents, and a minimum completion time of three minutes was developed so that the attendee could take some time to think through their answers. Duplicate responses were detected through a procedure matching demographics and removed from the data set. We also examined patterns of missing data to determine potential systematic bias, along with response consistency on semantically similar items, to determine if a response was accurate or invalid. In total, 127 surveys were collected initially, with 17 not able to be used due to incomplete data, failed attention checks, or completion times below the minimum limit, resulting in an aggregate sample of 110 valid responses (86.6% response rate). The data screening procedures followed for the final data set led us to examine cases for outliers using Mahalanobis distance, test normality using Shapiro–Wilk, and perform complete missing data analysis, which ensured the quality of the final data set.

3.4. Statistical Analysis

All statistical analyses were performed using RStudio (version 2024.9.1.394, Boston, MA, USA) with R version 4.4.2. The measurement and structural models were assessed using partial least squares structural equation modeling (PLS-SEM). To evaluate the measurement model, convergent validity was assessed using mean bootstrap factor loadings, Cronbach’s alpha, composite reliability (rhoC), average variance extracted (AVE), and rhoA. Discriminant validity was examined using the Fornell–Larcker criterion and the Heterotrait–Monotrait Ratio (HTMT), with 95% confidence intervals generated through bootstrapping. Items with factor loadings below 0.50 were excluded from the final model. For the structural model, path coefficients (β), t-values, p-values, and 95% confidence intervals were estimated using bootstrapping to determine the significance of direct and indirect relationships between constructs. Mediation analysis was conducted to test the indirect effect of the perceived value on the intention to use through attitude, and the variance accounted for (VAF) was calculated to determine the extent of mediation. A p-value of less than 0.05 was considered statistically significant.

4. Results

4.1. Characteristics of the Participants

A total of 110 respondents participated in the current study, with an equal distribution of gender: 55 males (50.0%) and 55 females (50.0%). Among the respondents who reported their age, the largest proportion were aged 18 to 24 years (49.2%), followed by those aged 35 to 44 years (27.0%). In terms of educational attainment, nearly half of the respondents held a bachelor’s degree (49.1%), followed by 30.6% with less than a high school diploma. When asked about dining companions, more than half of the participants (54.5%) reported usually dining with family, followed by friends (30.9%), while a smaller proportion dined alone (8.2%). Regarding dining frequency, most participants reported eating out either two to three times (41.8%) or once (37.3%) per week (

Table 1. Characteristics of the participants.

Characteristic	Description
Gender
Male	55 (50.0%)
Female	55 (50.0%)
Age
18 to 24	54 (49.2%)
25 to 34	7 (6.3%)
35 to 44	30 (27.0%)
45 to 54	14 (12.7%)
55 to 64	3 (3.2%)
>65	2 (1.6%)
Level of education
Less than a high school diploma	33 (30.0%)
High school	10 (9.1%)
Bachelor	54 (49.1%)
Master	5 (4.5%)
Doctorate	8 (7.3%)
Types of companions
Alone	9 (8.2%)
Family	60 (54.5%)
Friends	34 (30.9%)
Lover	7 (6.4%)
Frequency of dining out
Once	41 (37.3%)
2 to 3 times	46 (41.8%)
4 to 5 times	11 (10.0%)
>5 times	12 (10.9%)

).

4.2. Convergent and Construct Validity

Table 2. Results of the convergent and construct validity.

Scale and Items	Mean Bootstrap Factor Loading	Alpha	rhoC	AVE	rhoA
Perceived naturalness		0.721	0.834	0.633	0.831
I think food produced in smart farms is natural and healthy	0.792
I believe this method provides fresh and high-quality food products	0.850
I feel that food grown in smart farm restaurants is more natural than conventional food	0.539
Psychological benefit		0.754	0.852	0.662	0.856
Visiting a smart farm restaurant would make me feel like I am contributing to environmental protection	0.790
This experience would give me psychological comfort and satisfaction	0.871
I see it as an opportunity to feel connected to nature	0.628
Healthy well-being		1.000	1.000	1.000	1.000
I believe food served in these restaurants is beneficial for my health	1.000
Enjoyment		0.872	0.919	0.792	0.938
I think dining at an indoor smart farm restaurant would be enjoyable	0.819
I see it as a new and exciting experience	0.851
I believe this experience would make dining more fun	0.872
Perceived risk		1.000	1.000	1.000	1.000
I am concerned that the food quality might be lower than expected	1.000
Perceived value		1.000	1.000	1.000	1.000
I believe dining at an indoor smart farm restaurant would be valuable to me	1.000
Attitude		0.781	0.900	0.819	0.802
I believe dining at a smart farm restaurant would be a positive experience	0.864
I have a positive attitude towards experiencing smart farm restaurants in Al-Ahsa	0.911
Intention to Use		0.827	0.920	0.852	0.839
I intend to visit an indoor smart farm restaurant if available in Al-Ahsa	0.868
I am likely to choose a smart farm restaurant experience for my future outings	0.933

AVE: average variance extracted; Alpha: Cronbach’s alpha.

presents the results of the convergent and construct validity assessment for the final measurement model. We excluded eight items to ensure that all retained indicators met the minimum threshold for convergent validity (bootstrap factor loading > 0.50). Specifically, one item was excluded from the attitude and intention to use domains, whereas two items were omitted from the perceived value, healthy well-being, and perceived risk domains. All constructs demonstrated an acceptable internal consistency reliability, with Cronbach’s alpha values ranging from 0.721 to 0.933 and composite reliability (rhoC) values from 0.834 to 0.920. The average variance extracted (AVE) exceeded the recommended threshold of 0.50 for all constructs, ranging from 0.633 for perceived naturalness to 1.000 for single-item constructs (Table 2).

4.3. Discriminant Validity

Table 3. Discriminant validity.

	Perceived Naturalness	Psychological Benefit	Healthy Well-Being	Enjoyment	Perceived Risk	Perceived Value	Attitude	Intention to Use
Perceived naturalness	0.795
Psychological benefit	0.761	0.814
Healthy well-being	0.578	0.600	1.000
Enjoyment	0.341	0.373	0.224	0.890
Perceived risk	0.378	0.341	0.332	0.267	1.000
Perceived value	0.436	0.409	0.316	0.212	0.200	1.000
Attitude	0.654	0.654	0.487	0.203	0.343	0.687	0.905
Intention to use	0.723	0.680	0.501	0.266	0.326	0.690	0.768	0.923

The square roots of the average variance extracted are displayed on the diagonal of the matrix, whereas the correlations between domains are shown on the lower triangular section.

presents the Fornell–Larcker criterion to assess the discriminant validity among the study constructs. The square roots of the average variance extracted (AVE) are presented on the diagonal and are greater than the inter-construct correlations in all cases. These results support an adequate discriminant validity. Based on the results of the bootstrapped Heterotrait–Monotrait Ratio (HTMT) analysis, an additional method for evaluating discriminant validity, construct pairs exhibited an acceptable discriminant validity, with HTMT values below the threshold and confidence intervals not exceeding 1.0 (

Table 4. Outcomes of the bootstrapped HTMT.

Relationship	T Stat	B-HTMT Values (95% CI)
Perceived naturalness → Psychological benefit	5.93	0.606 (0.533 to 0.734)
Perceived naturalness → Healthy well-being	8.48	0.701 (0.501 to 0.834)
Perceived naturalness → Enjoyment	2.17	0.439 (0.103 to 0.811)
Perceived naturalness → Perceived risk	2.34	0.454 (0.085 to 0.798)
Perceived naturalness → Perceived value	3.01	0.451 (0.168 to 0.726)
Perceived naturalness → Attitude	4.52	0.824 (0.461 to 1.023)
Perceived naturalness → Intention to use	12.19	0.901 (0.722 to 1.010)
Psychological benefit → Healthy well-being	9.07	0.716 (0.559 to 0.859)
Psychological benefit → Enjoyment	2.02	0.473 (0.112 to 0.922)
Psychological benefit → Perceived risk	2.14	0.445 (0.091 to 0.819)
Psychological benefit → Perceived value	2.71	0.424 (0.163 to 0.722)
Psychological benefit → Attitude	3.67	0.768 (0.229 to 1.012)
Psychological benefit → Intention to use	5.16	0.854 (0.385 to 1.075)
Healthy well-being → Enjoyment	1.68	0.249 (0.037 to 0.555)
Healthy well-being → Perceived risk	2.43	0.322 (0.047 to 0.574)
Healthy well-being → Perceived value	2.28	0.297 (0.041 to 0.553)
Healthy well-being → Attitude	3.60	0.543 (0.241 to 0.746)
Healthy well-being → Intention to use	5.18	0.542 (0.309 to 0.721)
Enjoyment → Perceived risk	1.66	0.271 (0.029 to 0.595)
Enjoyment → Perceived value	1.50	0.237 (0.035 to 0.556)
Enjoyment → Attitude	1.21	0.304 (0.087 to 0.683)
Enjoyment → Intention to use	1.65	0.326 (0.063 to 0.707)
Perceived risk → Perceived value	1.33	0.215 (0.009 to 0.534)
Perceived risk → Attitude	1.63	0.372 (0.024 to 0.774)
Perceived risk → Intention to use	1.64	0.341 (0.026 to 0.723)
Perceived value → Attitude	5.32	0.756 (0.442 to 0.923)
Perceived value → Intention to use	6.07	0.727 (0.425 to 0.900)
Attitude → Intention to use	7.67	0.920 (0.603 to 1.014)

).

4.4. Results of the Structural Model

The structural model revealed the significant direct effects of perceived value on both attitudes (β = 0.687, 95% CI [0.257, 0.846], p < 0.001) and intentions to use (β = 0.308, 95% CI [0.122, 0.530], p = 0.002). Additionally, attitudes significantly predicted intentions to use (β = 0.557, 95% CI [0.049, 0.732], p = 0.001). Mediation analysis demonstrated a significant indirect effect of perceived value on the intention to use through attitudes (β = 0.383, 95% CI [0.015, 0.566], p = 0.003), indicating that attitudes partially mediated this relationship. The variance accounted for (VAF) was 55.4%, supporting the presence of a partial mediation (

Table 5. Outcomes of the structural model.

Relationship	Beta (95% Confidence Intervals)	t-Value	p-Value
Perceived naturalness → Perceived value	0.295 (−0.308 to 0.780)	0.959	0.170
Psychological benefit → Perceived value	0.079 (−0.518 to 0.734)	0.230	0.409
Healthy well-being → Perceived value	0.078 (−0.164 to 0.347)	0.612	0.271
Enjoyment → Perceived value	0.059 (−0.260 to 0.377)	0.361	0.359
Perceived risk → Perceived value	0.020 (−0.224 to 0.340)	0.134	0.447
Perceived value → Attitude	0.687 (0.257 to 0.846)	4.693	<0.001
Perceived value → Intention to use	0.308 (0.122 to 0.530)	2.877	0.002
Attitude → Intention to use	0.557 (0.049 to 0.732)	3.325	0.001
Perceived value → Attitude → Intention to use (indirect effect)	0.383 (0.015 to 0.566)	2.749	0.003

and Figure 2).

5. Discussion

Consumers’ willingness to embrace smart farming technologies is one of the key factors for the success of smart farming systems (Ena & Siewa, 2022). The current study results found that the perceived value of smart farming systems was related to their positive impact on both the environment and health, while, at the same time, lowering the negative risks had a positive impact on both consumer attitudes and intentions to use. This indicates that, compared to more well-known consumer goods contexts, the direct impact of the perceived value on intentions may be more noticeable in smart-farming or innovative agricultural technology settings. This implies that the cognition of value may more immediately induce intentions in circumstances with a high benefit or risk salience connected with health, sustainability, or farming (Nakpathom et al., 2024). The findings were supported by the findings from multiple studies (Ena & Siewa, 2022; Joo et al., 2022; Kim & Lee, 2022). In a study that investigated consumer behavior in the indoor smart farm restaurant context, researchers reported that consumers who are aware of environmental pollution are also aware of its consequences, which in turn led to a sense of responsibility and influenced their personal norms related to indoor smart farm restaurant use (Joo et al., 2022). From another point of view, Ena and Siewa explain that smart farming technology remains a relatively new concept, and its application has not been tested to determine whether its perceived value influences the consumers’ intentions to use it or not use it (Ena & Siewa, 2022). Nevertheless, Ena and Siewa argued that, when consumer health is at stake, the health-related perceived value of a specific technology can support the intention to use that technology (Ena & Siewa, 2022). In addition to the perceived health benefits, Kim and Lee argue that people are willing to pay more for smart farm cultivation when they have more information about it and when its health impact is higher (Kim & Lee, 2022). Perceived risk, which is also closely linked with perceived value, has also been examined in the study by Jayashankar et al., which assessed the roles of trust, perceived value, and risk in adopting technology in agriculture (Jayashankar et al., 2018). Jayashankar et al. argue in their analysis that the adoption of the Internet of Things was positively impacted by the perceived value, while higher perceived risks negatively impacted IoT adoption (Jayashankar et al., 2018).

The current study also reported that attitudes significantly predicted the intention to use smart farm technology among consumers (Table 5), and that attitudes mediated the relationship between the perceived value and the intention to use; as the perceived value increases, attitudes improve, resulting in an increased intention to use. In a recent study that assessed the influence of perceived value on attitudes and intentions to use, Joo and Hwang (Joo & Hwang, 2025) reported similar findings, as individuals’ perceptions of value had an impact on forming the participants’ attitude, as well as their intentions to use smart farm restaurant systems (Joo et al., 2022). Similarly, another study that looked into consumer acceptance and behavior toward smart technology supported this finding, as their results found a significant impact of attitudes on the willingness to pay extra for smart farms vegetables (Joo & Hwang, 2025). On the other hand, Gemtou et al. believe that, although preferences or attitudes are key elements that influence intentions to use, they may not necessarily transfer into adoption owing to different impediments, such as a lack of skills or financial resources (Kasornbua & Pinsame, 2019).

The current study also shows a statistically significant indirect influence of perceived value on intentions to use via attitudes (Table 5). As a result, the perceived value raises positive sentiments, which improves consumers’ desire to utilize a specific smart farm technology. The partial mediation also leaves perception with a direct influence on intention that is independent of attitude. This indicates that the perceived value improves both consumers’ perceptions of smart farm technology, as well as their willingness to utilize it. This result aligned with the research by Zhuan, Luo, and Riaz, which indicated that the perceived value serves as a dependable predictor of behavioral intentions, and that attitudes frequently mediate this connection in various contexts, including the intentions to purchase green items (Zhuang et al., 2021). This association can be best described through the value attitude behavior theory, and the theory of planned behavior perspective. In a recent study that explored the adoption of smart technology from a value belief norm perspective, the researchers emphasized that value assessments shape mid-level attitudes that then influence behavioral intentions (Jayashankar et al., 2018). The researchers explained that the perceived green value of sustainability, the personal environmental beliefs of resource sustainability, and the personal norm of sustainability have significantly contributed to attitudes and behavioral changes by making consumers utilize green products more (Jayashankar et al., 2018). Another study also evaluated consumers’ decision-making framework in the context of indoor smart farm restaurants, and found that individual values like authenticity, health awareness, and sustainable intelligence positively affected the mediating elements of the theory of planned behavior concerning the intentions to return to the farm restaurant (Girish et al., 2023). In the same vein, Ahmmadi, Rahimian, and Movahed explain that attitudes toward a particular behavior are the first component that determines the intention to engage in the behavior (Ahmmadi et al., 2021). Moreover, numerous studies have indicated a connection between individuals’ attitudes and their willingness to adjust to a healthy ecosystem. These studies concentrated on topics like reducing home energy use, purchasing eco-friendly products, and operating an environmentally safe vehicle (Wang et al., 2019; Yadav & Pathak, 2016), which can explain the findings on the mediating role of attitudes from the current study.

When it comes to the practical implications of this study’s findings, it can be noted that an increasing market adoption requires addressing both the rational and emotional aspects of value perception, since consumer acceptance depends not only on how well smart farming technologies work but also on how consumers feel about and perceive them. It is the stakeholders’ responsibility to improve both the performance and advantages of smart farming technologies, while also influencing the perception of value in the minds of consumers. This means emphasizing benefits, minimizing perceived risks, improving user experience, and creating trust and credibility among the general public.

As for the study limitations, the current study did not assess the role of demographic variables such as age and work experience as mediators on the attitudes and intentions to use, which may overlook how different groups respond differently to perceived value, attitudes, or intentions to use. The study by Joo, Lee, and Hwang argues that the relationship between attitudes and behavioral intentions is moderated by age (Joo et al., 2022). Moreover, Schukat and Heise reported the moderating effects of age and work experience on the intention to use smart products (Schukat & Heise, 2021). Knowing these associations, future studies are encouraged to adjust and control those confounding variables to provide better estimates of the true relationship. Another limitation is that most of the study participants chosen were young individuals from the new generation, which creates a bias and means that the findings cannot be generalized to the general population, as they are more inclined toward new innovations and support the adoption of technology at work. Thus, more studies are required that include older farmers, and that assess the possible impacts of confounding variables like age, knowledge, and experience on the intention to use smart technologies.

6. Conclusions and Future Directions

To conclude, this research enhances the theoretical insight into the elements influencing the behavioral intentions of consumers to accept smart farming technologies and embrace products from them. Factors such as the perceived value in terms of risks, enjoyment, and health benefits have the potential to significantly impact consumer attitudes and their intention to use smart farming. Moreover, people’s perceptions of value enhance their eagerness to use the product, partly because of a more positive attitude towards it. Nevertheless, perceived value is also found to influence intentions directly, instead of indirectly via attitudes. Based on the findings of this study, it is necessary to consider how each of these determinants can be utilized and designed to ensure consumers’ willingness and use of smart products. Stakeholders should focus on developing technologies that enhance consumers’ perceptions of value by emphasizing health benefits, enjoyment, and risk reduction. As part of marketing and education strategies, tangible benefits such as improved food safety, quality, and sustainability should be highlighted, while also addressing perceived risks through transparent communication. Moreover, companies ought to focus on intuitive designs, social validation, and immersive experiences to foster favorable perceptions, as perceived value directly and indirectly affects intentions. While emerging innovative technologies have become accessible to many agricultural sectors in the majority of countries, the full optimization of smart farming technologies is not yet achieved, as not all consumers are willing or have the intention to purchase from them. As the adoption of smart technologies into any field comes with different obstacles that hinder the process, policymakers in agriculture sectors need to address the obstacles more comprehensively and, at the same time, in a way that is tailored to different consumer categories. In addition, more research papers are required that look into the possible confounding variables that have the potential to impact the association between perceived value, attitudes, and intentions to use among consumers. Future work should also situate smart farm restaurants within broader tourism digital ecosystems to accelerate adoption (Shilibekova et al., 2024). We recommend more research papers that focus on older consumers, as they are less likely to adopt and utilize smart technologies than their younger counterparts due to several factors like skepticism about new technology reliability, believing in traditional farming methods, and finding digital tools to be complex and less trustworthy. Additionally, we suggest carrying out qualitative research to enhance the comprehension of moderating factors like trust, technology readiness, or social influence, enabling researchers to gain insights into how perceived value impacts adoption behaviors in various demographic and cultural settings.