Consumer Trust in Emerging Food Technologies: A Comparative Analysis of Croatia and India

Abstract

Consumer trust plays a critical role in the successful adoption of emerging food technologies. This study investigates how trust in five key food technologies—genetically modified organisms (GMO), 3D-printed food, lab-grown meat, nanotechnology, and functional foods—varies across two culturally distinct countries: Croatia and India. Utilizing a quantitative approach with responses from 538 participants, the research explores how demographic factors such as country of residence, gender, and urban-rural setting influence consumer attitudes. Statistical analysis was conducted using MANOVA and one-way ANOVA to test seven hypotheses regarding trust levels. The results revealed significant cross-national differences, with Indian consumers expressing higher trust across all technologies studied. In contrast, variables such as gender and place of residence showed limited or context-dependent influence. These findings underscore the importance of cultural context in shaping consumer trust and point to the need for targeted communication and policy strategies in promoting food innovation. The study contributes to the growing body of research on food technology adoption by emphasizing trust as a culturally embedded and demographically nuanced phenomenon.

1. Introduction

The demand for food is never-ending and continues to rise with an increasing population. It highlights the continuous need for innovation and technological advancement to enhance food security, sustainability, and the food supply chain. The global conversation around food innovation has moved beyond technical feasibility to include ethical, cultural, and psychological considerations. Consumer attitudes toward novel food technologies are shaped by more than just safety and functionality, they are tied to values, traditions, and trust in institutions. Public perception is often influenced by how transparent and consistent the communication is from both regulators and manufacturers. In some cases, consumers express greater concern about the process through which food is made than the final product itself. For instance, the idea of lab-grown meat may evoke discomfort despite its potential environmental advantages.

Emerging technologies such as 3D printing, nanotechnology, and alternative protein sources are key factors addressing these challenges [1]. These emerging technologies have a promising role in environmental benefits and improved nutrition. Genetically modified foods are derived from genetically modified organisms (GMOs), which have undergone intentional alterations in their genetic material. It enables the modification of the existing traits or incorporating new traits [2]. Nanotechnology in food involves the use of nanoparticles to improve nutritional delivery and enhance food safety in food processing and packaging [3]. Cultivated meat is an alternative to conventional meat, created in laboratory and derived from animal cells instead of harvesting from livestock [4]. 3D printing finds applications in different fields and is used in the food industry to create customized and intricate shapes with depositing edible successive layers [5]. Functional foods are foods that provide health benefits [6]. Adoption of food technology increases with greater trust in technology. A lack of knowledge often leads to fear and rejection. Consumer acceptance is largely influenced by perceived risks and benefits [2]. Trust is a major driver of consumer acceptance. Consumer acceptance is influenced by various factors ranging from fear of trying new products or neophobia, sensory, emotional, perceived risks, perceived benefits, and transparency in communication [7]. The introduction of the technologies requires large-scale projects, investments, and significant changes in the food supply chain. However, the major factor in their successful implementation lies in consumer trust. Understanding consumer perception of these technologies can help food companies and project teams develop products that people want and are willing to accept.

While some populations prioritize technological advancement and efficiency, others may emphasize naturalness, tradition, or ecological balance. Such variation highlights the need for tailored approaches in policy, marketing, and education efforts related to food innovation. Consumer trust cannot be assumed or engineered; it must be earned through consistent, respectful, and culturally aware engagement. Emerging food technologies will only succeed on a broad scale if they resonate with public expectations and are introduced in a way that aligns with local values and needs. This study aims to explore the consumer trust in emerging technologies in Croatia and India and to analyze the factors that influence consumer trust in these regions. Croatia and India were selected as case countries because they represent two contrasting contexts. Croatia, as part of the European Union, reflects a cautious and often risk-averse discourse on food technologies, while India, as a rapidly developing economy, is characterized by higher exposure to innovation and greater consumer openness. This contrast offers a meaningful setting to analyze how cultural and socioeconomic background shapes consumer trust in emerging food technologies. By understanding how consumers in different countries construct meaning around new technologies, stakeholders can better navigate the path from scientific discovery to everyday consumption.

While in some societies technological progress and efficiency are viewed as central values, in others naturalness, tradition, or ecological balance tend to be emphasized more strongly. Recent comparative research by Giacalone & Jaeger, 2023, indicates that these differences can shape public openness toward food innovation; for example, consumers in India have been shown to express greater acceptance of novel sustainable food technologies than respondents in other regions, reflecting a more innovation-oriented cultural outlook [8]. Trust is also not a uniform construction but develops along several dimensions. Lamm et al. (2025) demonstrate that cognitive, affective, and dispositional trust vary across demographic groups, underlining the importance of tailoring communication strategies to different audiences [9]. At the same time, the study by Monaco et al., 2024, highlight the role of heuristics, such as affective reactions, perceptions of naturalness, and trust-related shortcuts, in shaping attitudes toward novel foods [10]. Taken together, these insights point to the need for context-sensitive approaches when introducing new food technologies, ensuring that they resonate with local values and expectations. The study contributes to the growing field of food technology adoption by examining trust as a dynamic, context-dependent phenomenon.

2. Literature Review

2.1. Analysis Using VOSviewer

To explore the conceptual and thematic landscape of consumer trust in emerging food technologies, a bibliometric analysis was conducted using VOSviewer 1.6.20. This tool enabled the visualization and examination of research trends, keyword networks, and collaborative author relationships, offering insight into how scholarly attention is distributed across the field.

Table 1. Co-occurrence mapping in VOSviewer.

Clusters.	Mapped Color	Keywords
Cluster 1	Red	Technologies, Quality, Emotions, Consumption
Cluster 2	Green	Attitude, Perception, Impact, Innovation
Cluster 3	Blue (dark)	Benefit perception, Risk Perception, Social trust, Public acceptance
Cluster 4	Yellow	Acceptability, Neophobia, Disgust, Responses
Cluster 5	Violet	Acceptance, Novel food, 3d printing
Cluster 6	Blue (light)	Consumer acceptance, Perception, Challenges
Cluster 7	Orange	Novel foods, Functional foods, Protein, Sensory properties
Cluster 8	Brown	Risk, Benefits

Note: Cluster 1—Technological and Quality-Oriented Research; Cluster 2—Attitudes and Perceptions toward Innovation; Cluster 3—Risk–Benefit and Social Trust; Cluster 4—Psychological Barriers and Neophobia; Cluster 5—Acceptance of Novel Foods; Cluster 6—Consumer Challenges and Perceptions; Cluster 7—Functional Foods and Sensory Properties; Cluster 8—Risk–Benefit Trade-offs.

presents eight thematic keyword clusters identified through co-occurrence mapping in VOSviewer. Each cluster groups together frequently co-appearing terms in the literature on emerging food technologies and consumer trust. The clusters reflect interconnected research themes ranging from emotional and perceptual factors to technological innovation and risk perception. Notably, core concepts such as “acceptance,” “neophobia,” “attitude,” and “technologies” emerge as central nodes across the clusters, indicating their cross-cutting relevance to consumer trust.

Figure 1 presents a co-occurrence network visualization generated using VOSviewer, mapping the relationships among keywords in the literature related to emerging food technologies and consumer trust. Each node represents a specific keyword, and its size reflects the frequency of occurrence in the dataset. The lines connecting the nodes indicate the strength of co-occurrence between keywords, with thicker lines denoting stronger associations. The keywords are grouped into clusters, each marked by a distinct color, based on their thematic similarity and frequency of co-appearance in the same documents.

At the core of the map, central concepts such as “technologies,” “acceptance,” “consumer acceptance,” “attitudes,” and “neophobia” appear prominently, indicating their foundational role in the literature. The red cluster includes terms like “technologies,” “sustainability,” “acceptance,” and “innovation,” emphasizing a technology-centered discourse. The yellow cluster highlights psychological and behavioral themes, including “food neophobia,” “disgust,” “acceptability,” and “food choice.” The green and blue clusters focus more on perceptions of risk and benefit, nanotechnology, public acceptance, and gene editing.

This network map reveals how scholarly attention is distributed across interrelated domains such as emotional response, risk perception, sustainability, and consumer behavior. It underscores that consumer trust is not treated as a singular issue but rather as a multifaceted construct influenced by technological, psychological, and societal factors. The visualization also illustrates how keywords act as conceptual bridges between clusters, suggesting interdisciplinary overlaps and the growing integration of perspectives in this area of research (Table 1).

While concepts such as attitude, perception, and quality are also relevant, technologies and acceptance were chosen for further elaboration in Figure 2 and Figure 3 as they represent the most integrative nodes, technologies as the central structural hub and acceptance as the key outcome of related constructs.

Figure 2 visualizes a co-occurrence network of keywords with “technologies” clearly positioned as the central hub within the research landscape. The dense and multidirectional connections radiating from this node underscore its foundational role in discussions around emerging food innovations. Closely associated terms such as “consumer acceptance,” “sustainability,” “choice,” and “attitudes” indicate that a substantial segment of the literature is framed through a technological perspective, particularly in terms of how these advancements interact with consumer behavior, perceived product value, and broader social or environmental goals.

The presence of keywords like “emotions,” “disgust,” and “neophobia” within the connected clusters highlights that trust in food technology is not determined solely by objective attributes but is also shaped by emotional and psychological responses. These concepts suggest that adoption is influenced as much by personal attitudes and cognitive biases as by technological efficiency or safety assurances.

Terms such as “3D food printing,” “cultivated meat,” and “protein” appear on the periphery, linked but slightly more dispersed, indicating emerging but growing interest in specific applications of technology. Meanwhile, the proximity of words like “risk perception,” “benefits,” and “public acceptance” signals the central tension within the literature: how to bridge innovation with societal readiness. These patterns show that trust must be understood not only through technical validation but also through the lens of familiarity, communication, and alignment with consumer expectations.

This network provides a clear visual representation of how the concept of “technologies” acts as a nexus around which multiple dimensions of consumer trust are organized, ranging from emotional response and risk assessment to sustainability concerns and product familiarity.

Figure 3 illustrates a co-occurrence network map in which the keyword “acceptance” functions as the central conceptual anchor. Unlike previous configurations focused primarily on technological aspects, this map reveals a thematic emphasis on psychological, behavioral, and ethical dimensions of consumer interaction with food innovation. The most directly connected terms, such as “consumer perception,” “attitudes,” “neophobia,” “risk perception,” and “sustainability”, indicate that much of the current literature is exploring why consumers accept or reject emerging food technologies, rather than simply how those technologies function.

The strong links between “acceptance” and “food neophobia” suggest that emotional and cognitive resistance to unfamiliar food products remains a core obstacle to broader adoption. The presence of terms like “disgust,” “responses,” and “sensory properties” reinforces this, pointing to the role of subjective experience in shaping consumer trust. These findings are consistent with research that highlights how deeply rooted psychological factors, including fear of novelty or perceived unnaturalness, can override objective assessments of safety or utility.

The appearance of “sustainability,” “impact,” and “socio-environmental values” within the same network indicates that ethical considerations are becoming increasingly relevant to acceptance discourse. This reflects a broader shift in consumer expectations, one where ecological responsibility and alignment with personal values matter as much as taste or nutritional benefit. The proximity of “functional foods” and “novel foods” also suggests that acceptance is shaped by how well new products are integrated into existing food habits and cultural norms.

This co-occurrence map demonstrates that acceptance of food technology is not a passive outcome of exposure, but rather a dynamic process influenced by belief systems, emotional reactions, and value-driven reasoning.

Figure 4 displays a co-authorship network generated using VOSviewer, visualizing collaborative relationships among authors within the field of consumer trust in emerging food technologies. Each node represents an individual researcher, and the lines (edges) connecting them indicate co-authored publications. The proximity of the nodes reflects the frequency and strength of their collaborative ties, while the color coding denotes distinct clusters of researchers who tend to work closely together.

The network reveals a diverse and well-dispersed authorship landscape, characterized by multiple, relatively independent research groups. Some clusters, such as those surrounding authors like Siegrist and Hartmann, 2020, [11], and Frewer, 2014, [12], appear prominently connected and central to the discourse, indicating their strong influence and high level of collaboration within the field. These authors are commonly associated with studies on risk perception, consumer attitudes, and psychological factors influencing food technology acceptance. Other clusters, such as those featuring Cui, 2018, [13], appear more self-contained, possibly reflecting specialized or regionally focused teams working on niche areas such as behavioral economics, willingness to consume, or specific technological applications. The presence of more recent contributors, such as Fantechi, 2023, [14], also indicates an expanding and evolving research community, with newer voices entering and diversifying the discourse.

The relative separation between some clusters suggests that, while collaboration exists, the field still exhibits a degree of fragmentation. This may be due to differences in disciplinary backgrounds, geographic locations, or methodological approaches. Nonetheless, the network illustrates a healthy and growing research ecosystem, with a mix of well-established experts and emerging scholars contributing to the multidimensional exploration of trust in food innovation. This co-authorship map not only highlights the collaborative dynamics in the field but also suggests potential opportunities for cross-disciplinary integration and future joint research efforts, particularly between clusters that currently operate in parallel rather than in close interaction.

Table 2. Overview of Co-authorship Clusters in the Literature.

Claster/Author	Claster/Author	Claster/Author	Claster/Author
Cluster 1 Ali (2021) [2] Cui (2018) [13] Hu (2020) [28] Cluster 2 Asioli (2022) [29] Baum (2023) [30] Fu (2023) [4]	Cluster 3 Kahriman (2024) [31] Salgado-Beltrán (2018) [32] Wang (2023) [33] Cluster 4 Feindt (2019) [34] Frewer (2014) [12] Kuang (2020) [35]	Cluster 5 Bareen (2025) [36] Califano (2024) [37] Feng (2022) [38] Ross (2022) [39] Scheele (2022) [40] Silva (2024) [41] Tesikova (2022) [42]	Cluster 6 Kuttschreuter (2019) [43] Schnettler (2013) [44] Yue (2015) [45] Cluster 7 Proserpio (2019) [17] Proserpio (2020) [18]
Cluster 8 Sahrin (2023) [46] Szlachicuk (2024) [47] Tolve (2025) [48] Cluster 9 Perrey (2015) [49] Tsimitri (2022) [6]	Cluster 10 Aschemann (2022) [22] Chiaraluce (2024) [50] Cluster 11 Chang (2019) [51] Dupont (2022) [52] Fasanelli (2025) [53] Krings (2022) [54]	Cluster 12 Gan (2024) [55] Yano (2021) [56] Yano (2023) [57] Cluster 13 Banovic (2023) [58] Bucher (2023) [59] Demartini (2019) [20] Ford (2024) [60]	Cluster 14 Fantechi (2023) [14] Cluster 15 Idowu (2021) [7] Vidigal (2015) [23] Cluster 16 Kane (2021) [61] Szczepanski 2024 [62]

Note: Cluster 1—Risk–Benefit and GMO Perceptions; Cluster 2—Cultured Meat and Consumer Valuation; Cluster 3—Food Technology Adoption in Emerging Economies; Cluster 4—Risk Communication and Public Acceptance; Cluster 5—Innovation and Product Acceptance; Cluster 6—Nanotechnology, Risk, and Trust; Cluster 7—Sensory Properties and Novel Foods; Cluster 8—Neophobia and Food Preferences; Cluster 9—Attitudes Toward Novel Foods; Cluster 10—Sustainable Food Innovations; Cluster 11—Consumer Behavior and Neophobia; Cluster 12—Protein Alternatives and Innovation; Cluster 13—Precision Fermentation and Functional Foods; Cluster 14—Algae and Novel Ingredients; Cluster 15—Consumer Acceptance of Molecular Cuisine and Upcycled Foods; Cluster 16—Sustainability and Consumer Trust.

presents a summary of author groupings derived from co-authorship network analysis. Using data visualized in Figure 4, the table organizes researchers into 16 distinct clusters based on their collaborative patterns in published literature on consumer trust and emerging food technologies. Each cluster reflects a group of scholars who have frequently worked together, often sharing common research interests, theoretical frameworks, or methodological approaches.

These clusters represent intellectual communities that have contributed to specific aspects of the field. While some are composed of tightly connected teams focusing on areas such as risk perception, food neophobia, or sustainable innovation, others appear more interdisciplinary or regionally based. The clustering reveals both the depth of collaboration within subfields and the degree of fragmentation that still exists across the broader research landscape.

This structured overview helps identify which groups of authors are driving research themes and where opportunities may exist for cross-cluster collaboration. It also provides insight into how the field has evolved through networks of co-authorship rather than isolated individual efforts.

It is important to note that, unlike the eight thematic clusters in Table 1 which map conceptual relationships between keywords, the sixteen clusters in Table 2 reflect collaborative author networks; together they provide complementary insights into the intellectual and social structure of the field.

The purpose of Table 2 is to identify the key authors and collaborative clusters that have shaped research on consumer trust in emerging food technologies. While the table lists author groups, their research contributions are elaborated in the following sections of the chapter, ensuring that their relevance to the present study is clearly presented.

2.2. Emergence of Innovative Technologies in the Food Sector

Numerous studies have examined factors shaping consumer trust in food technologies. For genetically modified (GM) foods, Ali et al., 2020, [2] point to perceived risks and benefits as key determinants, while Cui et al., 2018, [13] emphasize public opinion being divided. Andrés-Sánchez et al., 2025, highlight subjective norms and transparency as important in shaping perceptions [63]. Perceived benefits influence acceptance of CRISPR technologies in the U.S. [64]. Within the EU, Hu et al., 2020, suggest regulatory adaptation and smart labeling can enhance public trust [28].

Nanotechnology and gene editing are explored by Parrella et al., 2024a and 2024b [3,15], noting improvements in food safety and shelf life. Perrea et al., 2023, show that perceived value outweighs cost in product acceptance [65]. Vasquez et al., 2022, identify public concern over gene editing as unnatural [66], while Junaedi et al., 2024, link low acceptance to limited knowledge [67]. Swedish consumers prefer traditional plant breeding, with age and gender influencing attitudes toward GM foods [68].

Cultured meat has also received attention. Asioli et al., 2022, find valuation differences across countries [29], and Baum et al., 2023, stress visual cues and neophobia [30]. Siegrist and Hartmann, 2020, highlight perceived unnaturalness and disgust [11]. Fu et al., 2023, associate acceptance with sustainability, health, and animal welfare [4], while Kühn et al., 2023, identify meat attachment as a barrier [69]. Marketing is key to overcoming resistance [70], and we can find growing consumer willingness to purchase cultured meat [16].

Behavioral predictors of interest include perceived control and subjective norms [52], while some authors report skepticism among animal scientists [71]. Boereboom et al., 2022, distinguish consumer profiles across Europe [72]. Acceptance of fungi and plant-based proteins depends on health and sustainability perceptions [73,74], and public discussion is crucial for novel food acceptance [53]. In Japan, perceived artificiality impacts attitudes [75], and there are economic concerns among livestock farmers [76].

For nanotechnology, Feindt et al., 2019, [34] and Frewer et al., 2014, [12] underscore knowledge gaps and ethical concerns. Gómez-Llorente et al., 2022, find familiarity improves acceptance [77], and Kuang et al., 2020, cite sensory quality and benefits as drivers [35]. Pérez-Esteve et al., 2022, note that consumers favor nanotechnology in packaging over direct food contact [78]. Social media influences trust explores seaweed-based products in sustainable innovation [43,79]. Skepticism remains around sunflower oil produced using GM or nanotech methods [44]. Yue et al., 2015, identify benefits, knowledge, and labeling as key to willingness to accept such products [45].

Regarding 3D-printed food, positive perceptions arise when consumers understand nutritional and sensory aspects [37]. Edible packaging boosts user experience [80], while barriers include perceived unnaturalness and lack of communication [39]. Chirico Scheele et al., 2022, show that shape and material affect consumer satisfaction [40]. Health, sustainability, and customization also enhance acceptance [41]. Concerns remain about processing and affordability, and we need to highlight the importance of sensory appeal and clear labeling [42,81].

Functional foods are viewed positively when health benefits are clearly communicated [6,82]. Visual and textual cues matter [83], and neophobia reduces adolescent acceptance [17]. Texture and appearance influence preference [18], while spirulina and microalgae-based products appeal to health-conscious consumers [14,84].

Broader studies on novel agri-food technologies also reveal that trust depends on transparency and perceived naturalness [55], as well as how information is presented [85]. Regional, economic, and nutritional concerns further shape opinion [56,57]. Insect-based foods face cultural resistance, but education can increase acceptance [61,86]. Frewer, 2014, argues for risk-based and socially responsive innovation [12]. Finally, Mehta et al., 2023, find that familiarity improves acceptance of SCOBY-based products [87].

2.3. Food Technology Neophobia and Its Impact on Consumer Trust

Food technology neophobia, defined as the hesitation or refusal to try foods produced with new technologies, has been identified as a major factor limiting consumer acceptance. Tools like the Japanese Alternative Food Neophobia Scale (J-FNS-A), developed by Kamei et al., 2024, help assess resistance toward innovations such as cultured meat [88]. Parrella et al., 2023, note that some consumers view new technologies as unnecessary [89]. In Turkey, the Food Technology Neophobia (FTN) Scale was validated as a reliable instrument to measure perceptions [31].

Cultural context also shapes neophobia levels. Wang et al., 2023, found that Chinese consumers’ acceptance was linked to perceived benefits, while in New Zealand, both perceived risks and beliefs played a role [33]. In Northern Uganda, Okello et al., 2022, used the FTN Scale to examine attitudes toward processed milk, revealing that high neophobia and low familiarity significantly reduced acceptance [90]. Among university students, Sahrin et al., 2023, found neophobia was influenced by gender, income, BMI, and allergy status [46].

Neophobia also hinders acceptance of alternative proteins. Krings et al., 2022, identified it as a key barrier to clean meat adoption [54], while Szlachciuk and Żakowska-Biemans, 2024, reported that Polish adults were more open to insect-based food when insects were not visibly present, suggesting that visual cues related to disgust play a role [47]. Tolve et al., 2025, emphasize the need to align insect-based products with consumer preferences to overcome emotional resistance [48].

Chang et al., 2019, argue that companies should improve product experience or address neophobia directly to increase acceptance [51]. Perry et al., 2015, note that childhood neophobia can affect long-term dietary habits and recommend early introduction of nutritious foods before neophobia becomes entrenched [49]. In the context of sustainability, Aschemann-Witzel et al., 2022, highlight that food neophobia is a significant obstacle to the adoption of upcycled foods [22]. However, when these products are presented with clear benefit information, skepticism can be reduced and trust improved [91].

Bucher et al., 2023, found that high neophobia reduced willingness to try coated apples, unless accompanied by specific product information [59]. Similarly, Idowu-Adebayo et al., 2021, observed that consumers were reluctant to try turmeric-fortified drinks despite being aware of their health benefits [7]. Vidigal et al., 2015, report that Brazilian consumers generally favored conventional food production methods over novel techniques, primarily due to concerns over safety and unfamiliarity [23].

2.4. Determinants of Consumer Trust in Emerging Food Technologies

Perceptions of sustainability play a key role in shaping trust in novel food technologies. Lundén et al., 2020, suggest that sustainability concerns directly influence consumer attitudes [92], while Chiaraluce et al., 2024, argue that transparent alignment with environmental goals can enhance trust [50]. Perito et al., 2020, found that eco-conscious consumers are particularly receptive to innovative products [24]. Faber et al., 2024 showed that there are significant cross-national differences in the acceptance of alternative proteins, with age, dietary habits, and the level of familiarity with technology strongly influencing consumer trust [93].

In their analysis of consumer response to precision-fermented yogurt, Ford et al., 2024, observed that environmental messaging significantly shaped attitudes [60]. Similarly, Težak Damijanić et al., 2024, noted that willingness to try new products often stems from prior knowledge and favorable predispositions [94].

Sensory experience also plays a role. Głuchowski et al., 2021, compared traditional foods with molecular cuisine, noting that sensory appeal strongly affects acceptance [95]. Montero et al., 2024, showed that salt content and emotional response influenced older adults’ satisfaction with meals [19]. Fantechi et al., 2024, added that both emotional and ethical views shape perceptions of new food technologies [96], echoing Seegebarth et al.’s (2019) emphasis on the emotional component of food-related decisions [97].

Effective communication was consistently identified as a driver of trust. Rolland et al., 2020, found that clear, benefit-oriented product descriptions increase acceptance [98]. A similar pattern was observed by Szczepanski et al., 2024, in their study of Solein^®, where framing played a critical role in shifting attitudes [62]. According to Rabadán, 2021, consumers with higher education and involvement in wine culture were more positive toward wine innovations [99]. Demartini et al., 2019, showed that transparency about shelf-life extension in fish packaging improved consumer attitudes [20], while Salgado-Beltrán et al., 2018, found that appearance influenced initial preference for new rice pudding, though traditional flavors remained favored after disclosure [32].

Benefit perception emerged as a key predictor of willingness to accept novel foods in Jin et al.’s 2023 study, though naturalness and trust remained essential [21]. In a similar study, Rojas and Saldaña, 2021, reported that providing information about guava juice processed with ultrasound positively affected consumer responses, even when prices were lower [25].

Several studies emphasized prior knowledge and familiarity as central. Bareen et al., 2025, pointed to consumer innovativeness and satisfaction as influential factors [36]. Feng et al., 2022, noted that labeling strongly impacts perceived product quality [38]. Cattaneo et al., 2019, linked trust in food by-products to variables like education and openness to food technology [26]. Xia et al., 2024, differentiated between cognitive and emotional trust, stressing that rational understanding often precedes emotional acceptance [100].

Song et al., 2020, found that limited exposure to food innovations correlated with low trust [101], while their later research in 2022 showed that health and nutritional framing improved consumer openness in multiple European countries [27]. In a cross-cultural study, Banovic and Grunert, 2023, emphasized the influence of familiarity and cultural context on acceptance of precision fermentation [58].

Mixed attitudes were reported in Califano et al.’s 2024 studies on urban farming techniques such as hydroponics and robotic harvesting, with sustainability appealing to some, while others remained skeptical [37]. Monteiro et al., 2022, demonstrated that Brazilian consumers were hesitant toward foods processed with UV and ultrasound, highlighting the need for improved risk and knowledge communication around such technologies [102].

2.5. Research Gaps and Questions

Although previous studies have provided valuable insights into consumer trust in food technologies, several research gaps remain. First, much of the literature is regionally focused, with limited cross-national comparison between culturally diverse settings. Second, while psychological constructs such as neophobia or perceived naturalness are well established, the influence of demographic and contextual factors (e.g., gender, rural–urban residence) is less consistently understood. Third, the interaction between cultural context and consumer trust has not been systematically analyzed in relation to multiple technologies simultaneously. To address these gaps, this study formulates the following research questions: (1) How does consumer trust in emerging food technologies differ between Croatia and India? (2) To what extent do demographic variables (gender, place of residence) influence levels of trust? (3) Which technologies enjoy the highest levels of trust across both contexts, and why? By answering these questions, this study contributes to literature by combining cultural comparison with demographic analysis, offering a more nuanced understanding of consumer trust in food innovation.

3. Materials and Methods

The rationale for conducting this study lies in the limited availability of cross-national research on consumer trust in emerging food technologies. Most existing studies are conducted within single-country contexts, which makes it difficult to disentangle the influence of cultural and socio-economic background. Croatia and India were chosen as comparative cases because they represent two contrasting contexts: Croatia, embedded in the European Union with a generally cautious and risk-averse public discourse toward food technologies, and India, a rapidly developing economy with higher exposure to innovation and more positive predispositions. This contrast provides a meaningful setting to test how cultural context shapes consumer trust. Against this background, the following hypotheses were formulated.

The primary objective of this research was to explore the level of consumer trust in emerging food technologies in two culturally and economically distinct countries, Croatia and India, and to identify the influence of demographic variables on how these technologies are perceived. Consumer trust in emerging food technologies is a multidimensional construct shaped by cultural, social, and demographic factors. Previous research highlights that acceptance of food innovation is not universal but depends on contextual variables such as perceived risks, cultural familiarity, and institutional credibility. In line with this perspective, the following hypotheses are formulated, each grounded in recent empirical evidence:

H1: 

Consumers in India demonstrate significantly higher trust in genetically modified (GMO) food compared to consumers in Croatia.

Cross-national studies reveal that consumers in Asia tend to report higher confidence in the safety and regulation of genetically modified food than their European counterparts. Tomasevic et al., 2023, emphasize that Asian consumers, including those in India, often perceive GMO products as less controversial compared to European consumers, where skepticism remains deeply rooted in regulatory and cultural discourse [103]. This supports the expectation that Indian consumers are more open to genetically modified food than Croatian consumers.

H2: 

Consumers in India show significantly greater trust in 3D-printed food than their Croatian counterparts.

The adoption of 3D-printed food depends heavily on perceptions of innovation, customization, and credibility of information. According to Kamrath et al., 2025, the Food Technology Acceptance Model demonstrates that rational and emotional trust, when reinforced through transparent communication, substantially increases consumer willingness to adopt 3D-printed food. Given India’s younger demographic structure and greater exposure to technological advances, it is reasonable to expect stronger trust in 3D-printed food among Indian consumers compared to Croatia [104].

H3: 

Consumers in India express significantly higher trust in lab-grown meat than consumers in Croatia.

Lab-grown meat has been presented as a sustainable substitute for conventional meat, yet its acceptance varies across cultural contexts. Pakseresht et al., 2022, found that consumers in India and China express significantly higher openness to cultured meat than those in Western countries, largely due to environmental and ethical considerations [105]. These findings provide theoretical grounding for the assumption that Indian consumers will demonstrate greater trust in lab-grown meat than Croatian consumers.

H4: 

Consumers in India have significantly more trust in the application of nanotechnology in food than those in Croatia.

Nanotechnology in food is often met with mixed reactions, depending on how well consumers understand its benefits and risks. Gayathri et al., 2024, stress that consumer trust in nano-based food applications is fostered by transparent regulation and communication, which are increasingly present in rapidly developing economies such as India [106]. By contrast, European consumers, including Croatians, remain more cautious toward technologies that are perceived as less “natural.” This suggests a higher level of trust in food-related nanotechnology among Indian consumers.

H5: 

Consumers in India exhibit significantly greater trust in functional foods than Croatian consumers.

Functional foods are closely linked with perceptions of health and lifestyle. In the Indian context, Sinha & Parmar, 2023, demonstrate that the cultural integration of Ayurveda and holistic health practices enhances consumer trust in functional foods, which are seen as an extension of traditional dietary beliefs [107]. In contrast, European consumers, including those in Croatia, approach functional foods with more caution, often demanding stronger scientific validation and clearer labeling. This cultural difference supports the expectation of higher trust levels among Indian consumers.

H6: 

In both India and Croatia, trust in food technologies is significantly higher among urban residents than among those in rural areas.

Urbanization is often associated with greater exposure to innovative products and lifestyles, which translates into a higher likelihood of trust in novel food technologies. A report from Gartner, 2022, highlights that urban consumers report greater satisfaction with their dietary habits and greater openness to new food options compared to rural populations [108]. This suggests that across both India and Croatia, urban residents may demonstrate higher levels of trust in new food technologies.

H7: 

Men in both countries demonstrate higher levels of trust in new food technologies compared to women.

Gender is another factor that may influence openness to innovation. While some studies show negligible differences, others suggest that men may demonstrate slightly higher risk tolerance in the context of new food technologies. Feraco et al., 2024, for example, report that while gender differences in food preferences are generally small, men sometimes exhibit greater willingness to adopt unfamiliar food innovations [109]. This theoretical insight underpins the hypothesis that male consumers in India and Croatia may report higher trust in new food technologies compared to women.

These hypotheses reflect the complex interplay of cultural, demographic, and psychological factors shaping consumer trust in emerging food technologies. They build on contemporary evidence suggesting that acceptance varies not only across nations but also within populations depending on lifestyle, values, and risk perception.

To test these hypotheses, a quantitative research approach was applied, based on a structured questionnaire. Data were analyzed using both descriptive and inferential statistical methods, including multivariate analysis of variance (MANOVA) and follow-up ANOVA tests. The research was designed as a cross-sectional study, meaning data was collected at a single point in time, without longitudinal tracking of participants. Post hoc comparisons for country, gender, and place of residence were conducted using the Bonferroni correction to control for Type I error.

The survey instrument used in the study consisted of several sections. The initial part of the questionnaire gathered basic demographic information from participants, including countries of residence, gender, and whether they lived in an urban or rural setting. The core of the questionnaire focused on statements related to five contemporary food technologies: genetically modified food (GMO), 3D-printed food, lab-grown (cultured) meat, nanotechnology in the food sector, and functional foods. Participants were asked to rate their level of trust in each of the five technologies using a standardized five-point Likert scale, ranging from 1 (“do not trust at all”) to 5 (“fully trust”). This unified scale allowed for consistent measurement and statistical comparison across the various technologies.

The survey was administered online in 2025 and remained open for 30 days. A total of 538 individuals completed the questionnaire, including 227 respondents from Croatia and 311 from India. Participants were recruited primarily through social media platforms (Facebook, Instagram, LinkedIn, WhatsApp) and a mailing list. Because recruitment was based on open calls via social media, the total number of potential respondents cannot be precisely determined. However, among those who completed the survey, 65% completed it in full. All partially completed questionnaires were excluded, and only fully completed responses were retained for analysis. This method enabled broad geographical reach within both countries and included participants from diverse regions and social backgrounds. The sample was demographically heterogeneous in terms of gender and place of residence, providing variation across key sociodemographic characteristics. Participation was entirely voluntary and anonymous. Respondents first reviewed an informed consent statement, were assured of confidentiality, and could withdraw at any time. No personal data that could identify individuals were collected, thereby ensuring privacy and compliance with ethical standards.

To minimize the risk of common method bias (CMB), several procedural safeguards were implemented during survey design and administration. Participation was entirely voluntary and anonymous, and respondents were explicitly informed that there were no right or wrong answers. This helped reduce social desirability pressures and evaluation apprehension. The questionnaire was structured to minimize response patterns, with questions ordered randomly and thematically separated across sections. Different measurement formats were employed (e.g., Likert-type scales for attitudes and perceptions, categorical items for demographics) to reduce uniformity in responses. Items were formulated in a neutral and concise manner to limit ambiguity and interpretative bias. These measures collectively reduce the likelihood that CMB substantially threatens the validity of the findings.

Table 3. Demographic profile of responders.

Demographic Variable	Category	Croatia (n = 227)	India (n = 311)	Total
gender	men	121	152	273
	women	106	159	265
residence	urban	108	179	287
	rural	119	132	251

provides a summary of the demographic structure of the sample, disaggregated by country (Croatia and India), gender, and place of residence (urban vs. rural). The total sample consisted of 538 respondents, with 227 participants from Croatia and 311 from India. When examining gender distribution, the overall sample includes 273 male and 265 female respondents, with a relatively balanced representation in both countries. In Croatia, 121 men and 106 women participated, while in India, the number of male respondents was 152 and female 159. Regarding place of residence, a total of 287 respondents indicated that they live in urban areas, while 251 participants reported living in rural settings. Among Croatian respondents, 108 were from urban areas and 119 from rural, whereas Indian respondents included 179 urban and 132 rural participants. This distribution ensures a solid basis for comparing consumer attitudes across both geographical and sociocultural contexts.

After the data collection phase, the responses were analyzed using the statistical software JASP. A multivariate analysis of variance (MANOVA) was employed to examine the simultaneous influence of several independent variables, namely, country of residence, gender, and place of residence, on a set of dependent variables representing levels of trust in each food technology. Prior to conducting the MANOVA, assumption checks were performed to ensure the appropriateness and robustness of the statistical analysis.

Prior to conducting the MANOVA, assumption checks were performed. Box’s M-test indicated significant differences in covariance matrices between groups (χ² = 465.695, df = 105, p < 0.001). Although this represents a violation of homogeneity, the analysis employed Pillai’s Trace, which is robust to such violations, and the results therefore remain valid. The Shapiro–Wilk test (W = 0.985, p < 0.001) suggested some deviation from perfect normality; however, given the large sample size (>500), such deviations are common and do not compromise the robustness of the analysis. These diagnostics confirm that the MANOVA results can be interpreted with confidence.

Levene’s test indicated significant violations of the homogeneity of variances assumption (e.g., GMO trust: F(7, 529) = 13.108, p < 0.001; 3D-printed food trust: F(7, 529) = 20.389, p < 0.001). However, given the large sample size and the robustness of Pillai’s Trace, these violations are not considered to compromise the validity of the MANOVA results.

To further investigate the individual hypotheses in greater detail, one-way analyses of variance (ANOVA) were conducted for each technology separately. These tests allowed for the identification of statistically significant effects of each demographic variable on attitudes toward specific technologies. In addition, interaction effects between variables were analyzed, with particular attention given to combinations such as country * place of residence and gender * place of residence, to gain deeper insights into potential cultural and spatial differences in the perception of food innovation.

A standard threshold for statistical significance of p < 0.05 was applied in determining whether to accept or reject the hypotheses. In cases involving multiple comparisons and interaction effects, the interpretation of results also considered contextual factors specific to each country, ensuring a more nuanced understanding of the findings.

The research was conducted in accordance with the principles of ethical conduct in scientific research. Participation was entirely voluntary, and respondents were informed in advance about the purpose of the study and the way their data would be handled. Given the anonymous nature of data collection and the absence of any sensitive personal information, formal approval from an ethics committee was not required. Nevertheless, the research was carried out in line with the ethical guidelines of the Helsinki Declaration for social science research.

4. Results

This section presents the findings of the conducted analyses, highlighting the influence of key demographic factors on consumer trust in various emerging food technologies.

Table 4. Pillai’s Trace Test: Multivariate Effects of Country, Gender, and Place of Residence on Trust in Food Technologies.

Cases	df	Approx. F	Trace Pillai	Num df	Den df	p
(intercept)	1	1403.911	0.930	5	525.000	<0.001
country	1	107.167	0.505	5	525.000	<0.001
gender	1	1.178	0.011	5	525.000	0.319
country * gender	1	0.928	0.009	5	525.000	0.462
place of residence	1	0.535	0.005	5	525.000	0.750
country * place of residence	1	2.586	0.024	5	525.000	0.025
gender * place of residence	1	2.122	0.020	5	525.000	0.061
country * gender * place of residence	1	0.832	0.008	5	525.000	0.527
residuals	529

presents the results of a multivariate analysis of variance (MANOVA), which examined the simultaneous influence of multiple independent variables, namely, country of residence, gender, place of residence, and their interactions, on five dependent variables reflecting levels of trust in various emerging food technologies.

The findings reveal that the participants’ country of origin has a statistically significant effect on the combined dependent variables (Pillai’s Trace = 0.505, p < 0.001), indicating that the differences between Croatian and Indian consumers are both pronounced and consistent across all technologies assessed. Given that the technologies in question include GM foods, 3D-printed food, lab-grown meat, nanotechnology, and functional foods, these results support the assumptions outlined in hypotheses H1 through H5, specifically, that Indian consumers demonstrate significantly higher levels of trust in this food innovations compared to their Croatian counterparts. The significant interaction between country and place of residence (p = 0.025) highlights an important nuance: the urban-rural divide does not manifest uniformly across the two countries, suggesting that local context may shape consumer attitudes in distinct ways.

Table 5. Trust in Genetically Modified Foods by Country, Gender, and Place of Residence.

Cases	Sum of Squares	df	Mean Square	F	p
(intercept)	3444.320	1	3444.320	3741.976	<0.001
country	291.608	1	291.608	316.808	<0.001
gender	0.659	1	0.659	0.716	0.398
country * gender	2.102	1	2.102	2.283	0.131
place of residence	0.007	1	0.007	0.008	0.929
country * place of residence	7.653	1	7.653	8.315	0.004
gender * place of residence	0.043	1	0.043	0.047	0.829
country * gender * place of residence	2.687	1	2.687	2.920	0.088
residuals	486.921	529	0.920

presents the results of a one-way analysis of variance (ANOVA) examining how different demographic factors influence consumer trust in genetically modified (GM) food. The topic of GMOs has long been the subject of public debate in both scientific and media circles, and consumer perceptions vary depending on the country, exposure to information, education level, and cultural values.

The most important result in this table relates to the country variable, which shows a strong and statistically highly significant effect on trust in GM food (F = 316.808, p < 0.001). This indicates a pronounced difference between respondents from Croatia and India. This is a direct confirmation of Hypothesis H1, which predicted that Indian consumers would demonstrate higher levels of trust in genetically modified food compared to Croatian respondents. Since the p-value is far below 0.05, we can say with high confidence that Hypothesis H1 is confirmed. This finding likely reflects different levels of public debate, institutional communication, and historical contexts regarding the acceptance of biotechnology in the two countries. For example, GMO cotton has long been in use in India, while in Croatia (and the broader European context) there is often regulatory and societal skepticism toward GMOs. The result for the country * place of residence interaction is also statistically significant (F = 8.315, p = 0.004). This suggests that the impact of place of residence (urban vs. rural areas) differs between India and Croatia. For instance, it is possible that urban consumers in India are significantly more open to GM food than rural ones, while in Croatia the difference between urban and rural areas may not be as pronounced, or may even go in the opposite direction. This finding adds further complexity to Hypothesis H6 (related to place of residence), and although this hypothesis is not confirmed in all analyses, this interaction in the case of GM food points to the need for more detailed research into the spatial dynamics of perceptions toward new technologies.

Table 6. Trust in 3D-Printed Food Across Demographic Variables.

Cases	Sum of Squares	df	Mean Square	F	p
(intercept)	2654.816	1	2654.816	2953.251	<0.001
country	61.204	1	61.204	68.084	<0.001
gender	0.906	1	0.906	1.008	0.316
country * gender	0.025	1	0.025	0.028	0.867
place of residence	0.001	1	0.001	0.001	0.969
country * place of residence	0.619	1	0.619	0.688	0.407
gender * place of residence	0.270	1	0.270	0.300	0.584
country * gender * place of residence	2.617	1	2.617	2.911	0.089
residuals	475.543	529	0.899

presents the results of a one-way analysis of variance (ANOVA) examining differences in consumer trust levels toward 3D-printed food technology. Although this technology is still in a broader phase of commercial development, it elicits both interest and concern depending on market context and cultural factors. The analysis includes the main independent variables: respondents’ country, gender, and place of residence, as well as their interactions.

The results show that country is a statistically significant factor in shaping attitudes toward this technology (F = 68.084, p < 0.001). This means there is a clear difference in trust levels between respondents from Croatia and India. This difference can be interpreted through the lens of cultural and societal distinctions, as well as levels of awareness and openness to technological innovations in the food sector. India, with its rapid technological development and large young population, may exhibit greater readiness to accept innovative solutions like 3D food printing, while the Croatian market may still display a degree of caution toward new and lesser-known technologies in everyday food consumption. This very difference confirms Hypothesis H2, which assumes that consumers in India have significantly higher trust in 3D-printed food compared to consumers in Croatia. Given the very high level of statistical significance (p < 0.001), we can say with strong confidence that H2 is confirmed. This finding highlights the need for future information and education campaigns about food technologies to be further tailored to cultural differences and levels of technological openness. The variables gender (F = 1.008, p = 0.316) and place of residence (F = 0.001, p = 0.969) do not show statistically significant influence on trust in 3D-printed food. This means that regardless of whether respondents are male or female, or whether they live in urban or rural areas, trust in this technology remains stable. Furthermore, all interaction variables (e.g., country * gender, country * place of residence, etc.) are not significant, which further confirms that national context is the primary differentiating factor in this case. The only value near the threshold of significance is the three-way interaction (country * gender * place of residence, p = 0.089), which does not reach the statistically accepted threshold of p < 0.05. Although not significant, it may suggest subtle patterns among subgroups that could be the subject of further research. Hypothesis H2 is confirmed, as country is a significant predictor of trust in 3D-printed food, and the data indicate that respondents from India demonstrate higher levels of trust than those from Croatia.

Table 7. Consumer Trust in Lab-Grown Meat in Croatia and India.

Cases	Sum of Squares	df	Mean Square	F	p
(intercept)	2492.829	1	2492.829	2614.479	<0.001
country	121.163	1	121.163	127.076	<0.001
gender	0.046	1	0.046	0.049	0.826
country * gender	0.426	1	0.426	0.447	0.504
place of residence	4.015 × 10−5	1	4.015 × 10−5	4.211 × 10−5	0.995
country * place of residence	0.749	1	0.749	0.786	0.376
gender * place of residence	6.348	1	6.348	6.658	0.010
country * gender * place of residence	1.052	1	1.052	1.104	0.294
residuals	504.386	529	0.953

presents the results of a one-way analysis of variance (ANOVA) regarding respondents’ trust in lab-grown meat, also referred to as cultured meat. The analysis includes three main independent variables, country, gender, and place of residence, as well as their interactions, to explore the extent to which these sociodemographic characteristics influence differences in trust toward this specific food technology.

The first and most notable finding relates to the country variable, which shows a very strong and statistically highly significant effect (F = 127.076, p < 0.001). This result clearly indicates a significant difference in trust levels toward lab-grown meat between participants from Croatia and India. This difference can be interpreted through cultural, religious, dietary, and social contexts, for example, in some parts of India, there is growing demand for alternatives to conventional meat due to environmental concerns and animal rights, while Croatian consumers may still exhibit greater skepticism toward artificially produced foods. This result is crucial as it directly relates to Hypothesis H3, which predicts that consumers in India will show greater trust in lab-grown meat technology compared to Croatian consumers. Given the high F-value and the very low p-value (p < 0.001), Hypothesis H3 can be confirmed with a high degree of confidence. This confirmation further supports the assumption that attitudes toward innovative food solutions are not universal but are deeply rooted in national, cultural, and socioeconomic contexts. On the other hand, the variables gender (p = 0.826) and place of residence (p = 0.995) are not statistically significant, meaning that neither the gender of the respondent nor whether they live in an urban or rural area plays a significant role in shaping trust in this technology. Furthermore, the country * gender interaction, as well as other interactions (such as country * place of residence, gender * place of residence, and the three-way interaction), are also not significant, with one exception, the interaction between gender and place of residence (F = 6.658, p = 0.010), which is statistically significant. This interaction suggests that, although neither gender nor place of residence individually influences attitudes significantly, their combination may have a certain effect. For instance, it is possible that urban women show a different pattern of trust than rural women, or that men from one type of area react differently than women from another, an intriguing direction for further research. Hypothesis H3 is confirmed: there is a clear and statistically significant difference in trust between Croatian and Indian consumers when it comes to lab-grown meat, with Indian consumers showing greater openness toward this technology.

Table 8. Trust in Nanotechnology in Food by Country, Gender, and Place of Residence.

Cases	Sum of Squares	df	Mean Square	F	p
(intercept)	2749.022	1	2749.022	2989.756	<0.001
country	401.854	1	401.854	437.044	<0.001
gender	1.139	1	1.139	1.238	0.266
country * gender	1.739	1	1.739	1.891	0.170
place of residence	1.486	1	1.486	1.616	0.204
country * place of residence	0.745	1	0.745	0.810	0.369
gender * place of residence	0.100	1	0.100	0.109	0.742
country * gender * place of residence	0.511	1	0.511	0.555	0.456
residuals	486.405	529	0.919

presents the results of an analysis of variance (ANOVA) examining the influence of demographic variables, country, gender, and place of residence, on consumer trust in the application of nanotechnology in food and the food industry. Nanotechnology is one of the most dynamic areas of innovation in the food sector, particularly in packaging, freshness preservation, and targeted nutrient delivery. However, despite scientific advantages, the level of consumer trust often depends on access to information, industry transparency, and cultural factors.

The main finding in this table concerns the country variable, which shows an extremely strong and statistically significant impact on respondents’ attitudes (F = 437.044, p < 0.001). This result confirms Hypothesis H4, which assumed that Indian consumers exhibit higher levels of trust in food-related nanotechnology compared to Croatian respondents. Given that the p-value is far below 0.05, we can confirm this hypothesis with high confidence. This outcome can be explained from several perspectives. India, as a country undergoing rapid technological development, is increasingly investing in new food technologies, including nanotechnology, often with government support and positive public communication. Additionally, the Indian market tends to show a higher threshold for innovation acceptance, particularly when such innovations are perceived as beneficial for health, food safety, or shelf life. In contrast, European consumers, including those in Croatia, traditionally exhibit greater caution toward technological innovations in food, often due to past debates on GMOs and similar technologies.

No other variable in this analysis, including gender (F = 1.238, p = 0.266) and place of residence (F = 1.616, p = 0.204), showed a statistically significant influence on trust levels. The same applies to all interaction effects between variables, including gender * country, country * place of residence, and the three-way interaction. These results suggest that national differences are the key factor, while demographic aspects such as gender or urban/rural origin are secondary or irrelevant in shaping attitudes. This confirms that differences in the acceptance of nanotechnology are not rooted in personal characteristics of the respondents, but rather in the broader social and cultural context. This has important implications for policymakers, as it highlights the importance of communication strategies tailored to national contexts. Hypothesis H4 is confirmed: Indian respondents show statistically significantly higher trust in food-related nanotechnology compared to Croatian respondents. Since neither gender nor place of residence had a significant impact on attitudes, we can conclude that macro-level differences between countries are the key predictor in this case, while individual demographic differences do not play a significant role.

Table 9. Perception and Trust Toward Functional Food Products.

Cases	Sum of Squares	df	Mean Square	F	p
(intercept)	5993.363	1	5993.363	5731.550	<0.001
country	231.360	1	231.360	221.253	<0.001
gender	0.240	1	0.240	0.229	0.632
country * gender	0.368	1	0.368	0.352	0.553
place of residence	0.163	1	0.163	0.155	0.694
country * place of residence	0.965	1	0.965	0.923	0.337
gender * place of residence	1.701	1	1.701	1.627	0.203
country * gender * place of residence	0.676	1	0.676	0.646	0.422
residuals	553.164	529	1.046

presents the results of an analysis of variance (ANOVA) examining the influence of country, gender, and place of residence on consumer trust in functional food, a category of food products that, in addition to nutritional value, provide added health benefits such as improved digestion, strengthened immunity, or prevention of chronic diseases. Functional food is increasingly becoming central to consumer eating habits, especially in the context of preventive health, which makes this part of the research particularly relevant.

The results clearly show that the variable “country” is statistically significant (F = 221.253, p < 0.001), indicating a significant difference between Croatian and Indian respondents in their level of trust in functional food. This is fully in line with Hypothesis H5, which assumes that Indian consumers show a higher level of trust in functional food compared to respondents from Croatia. Based on the obtained values, Hypothesis H5 can be unequivocally accepted. The significance of this finding can be interpreted from several angles. In India, the consumption of functional food is often associated with traditional health approaches (e.g., Ayurveda, herbal medicine), which facilitates the acceptance of modern products that promote health benefits. In contrast, in Croatia and many European contexts, functional food is still approached with a degree of caution, partly due to a lack of knowledge about functional ingredients and partly due to distrust in industrial innovations in the food sector. None of the other variables, including gender (F = 0.229, p = 0.632) and place of residence (F = 0.155, p = 0.694), nor their interactions, show statistically significant effects. This means that trust in functional food is not differentiated based on demographic characteristics within a given country but is instead shaped by the broader cultural and market environment. Even interactions such as country * gender or country * place of residence did not yield significant results, further confirming that differences in perception stem exclusively from the national context. Hypothesis H5 is confirmed: Indian consumers place significantly more trust in functional food than Croatian consumers. Since no demographic variable showed significance, the acceptance of functional food is shaped by collective attitudes and cultural predispositions, rather than by personal characteristics.

Hypothesis H6 predicted that respondents from urban areas would exhibit higher levels of trust in new food technologies compared to respondents from rural areas. The rationale behind this hypothesis is based on previous research suggesting that urban consumers are more frequently exposed to innovative food products, have better access to information, and are more engaged with modern sustainable food trends, which makes them more likely to accept technological innovations. In the conducted multivariate analysis of variance (MANOVA), the effect of place of residence (urban vs. rural) as an independent variable was not statistically significant (p = 0.750), indicating that there is no overall difference in the level of trust between urban and rural respondents when considering the combined effect across all analyzed technologies (GMO, 3D-printed food, cultured meat, nanotechnology, and functional food). This finding was further confirmed in all individual ANOVA analyses (Table 5, Table 6, Table 7, Table 8 and Table 9), where place of residence also showed no significant effect (all p-values > 0.05). The MANOVA analysis did reveal a statistically significant interaction between country and place of residence (Country * Place of Residence; p = 0.025). This means that the effect of place of residence varies depending on the country—in other words, urbanicity does not influence trust in the same way in India and Croatia. For example, in one country, urban respondents might demonstrate more trust, while in the other, the difference may be less pronounced or even reversed. H6 is not confirmed in its expected form as a main effect of place of residence. However, it can be conditionally and partially supported, as differences do emerge in interaction with national context. Thus, urbanicity does not have a universal impact; rather, its influence depends on the social and cultural environment in which it is measured. For this reason, H6 is classified as partially confirmed, with necessary qualifications and interpretive limitations.

Hypothesis H7 assumed that men would show higher levels of trust in new food technologies compared to women. This assumption draws from previous findings in the literature suggesting that men are generally more open to risk and novelty, whereas women tend to express greater concern for food safety and health, which can lead to lower trust in technologies such as genetically modified food or lab-grown meat.

However, the results of the statistical analysis do not support this hypothesis. Within the MANOVA framework, the effect of gender as an independent variable was not statistically significant (p = 0.319), indicating no significant differences in overall trust levels between male and female respondents. Additionally, in all individual ANOVA analyses for each technology (Table 5, Table 6, Table 7, Table 8 and Table 9), the gender effect also failed to reach statistical significance (all p-values > 0.05), further confirming that gender does not play a meaningful role in shaping attitudes toward food innovations. No statistically significant interactions involving gender (e.g., gender * country, gender * place of residence) were found in any analysis, which further strengthens this conclusion. Hypothesis H7 is clearly rejected, as gender did not show any influence on trust in food technologies among respondents from India and Croatia. This implies that differences in attitudes toward these technologies are not gender-dependent in this study, which may be attributed to the increasing normalization of food technology innovations or to cultural specifics of the sample.

In addition to statistical significance, we calculated effect sizes using Hedges’ g for country differences. The results indicate large effects for GMO trust (g = −1.54), nanotechnology in food trust (g = −1.82), and functional food trust (g = −1.30), a medium-to-large effect for lab-grown meat trust (g = −0.98), and a medium effect for 3D-printed food trust (g = −0.72). These results suggest substantial cross-country differences in consumer trust across all emerging food technologies.

Post hoc tests with Bonferroni adjustment indicated that participants from Croatia reported significantly lower trust compared to those from India (p < 0.001). No significant differences were found between genders (p = 0.454) or between urban and rural residents (p = 0.595).

5. Discussion

The study by Knight and Paradkar, 2008, concluded that there is relatively lower skepticism toward GMO food in India compared to European countries [110]. The study by Shew et al., 2016, also confirms a higher level of openness among Indians (76%) toward GMO food compared to Europeans [111]. Both findings support the acceptance of Hypothesis 1. Yang et al. (2024) found that in an emerging economy (Indonesia), personal innovativeness (i.e., individual propensity to innovate), perceived compatibility, and perceived product value significantly increase both consumers’ intention to consume and their willingness to pay a premium for 3D-printed food [112]. The study by Feng et al., 2022, highlights the strong influence of information in the acceptance of 3D-printed food in India, especially due to labeling and education, while consumer commitment in Croatia is significantly lower [38]. These two studies support Hypothesis 2. The study by Bryant et al., 2019, conducted on a sample of 3030 participants from the USA, China, and India, found that 56% of consumers in India expressed a willingness to regularly consume lab-grown meat, significantly more than in several European countries [113]. In contrast, Franceković et al., 2021, concluded that only 41% of European consumers would consider purchasing lab-grown meat, primarily due to concerns about its unnatural origin [114]. These findings support the acceptance of Hypothesis 3. The study by Knežević et al., 2012, from Croatia clearly states that consumer awareness of nanotechnology in food exists, but is significantly lower than in industrialized countries, stemming from concerns about lack of knowledge and potential risks [115]. In contrast, results from Gupta et al., 2024, show generally high trust, particularly in categories with obvious benefits (extended freshness, safety) [116]. Key factors are information, perceived risk/benefit balance, and trust in regulations, which indicates higher trust among Indian consumers toward nanotechnology. This supports Hypothesis 4. The study by Sharma et al., 2023, notes that health awareness, lifestyle trends, and rising income are key in shaping positive perceptions and trust toward functional foods in India [117]. On the other hand, research by Brečić et al., 2014, in Croatia found that consumers distinguish between motivations for health and practicality but also demonstrate low trust and a need for clearer product labeling [118]. These studies confirm Hypothesis 5. The qualitative study by Kumar et al., 2022, conducted among high school populations in Delhi, Mumbai, and Kochi, found that urban residents follow more modern dietary habits, including a greater inclination toward processed foods and new food technologies [119]. This supports Hypothesis 6. On the other hand, the study by Khalili et al., 2023, emphasizes that although urban consumers generally show more willingness, well-informed rural consumers can match or even surpass urban trust levels [120], leading to conditional acceptance of Hypothesis 6. Castellini et al., 2025 concluded in a systematic review of consumer acceptance of sustainable crop production technologies that gender was not highlighted as a consistent or significant predictor of attitudes toward those emerging technologies. [121]. The study by Rosenfeld and Tomiyama, 2021, shows that women are slightly more open to changing dietary habits, but the effect is small and supports the idea that gender is not a key determinant in the acceptance of food innovations [122]. Both arguments support the rejection of Hypothesis 7. The results of this study align with and further enrich insights from previous research efforts. Earlier investigations underscored the significance of cultural context and consumer openness in shaping attitudes toward novel food technologies, particularly in rapidly evolving markets like India and transitional economies such as Croatia [123]. In previous work, we observed that perceived naturalness, trust in regulation, and consumer familiarity play a central role in shaping acceptance, themes that reemerged strongly in the present analysis [124]. Notably, this study provides additional empirical depth by confirming that demographic factors such as country of residence, gender, and urban-rural location influence the degree of trust in food technologies, but often in nuanced or conditional ways [124,125]. These findings offer continuity with earlier conclusions while also opening new perspectives on how emerging markets engage with food innovation under complex social and informational dynamics. The results revealed clear differences between Croatian and Indian consumers. Indian respondents consistently reported higher levels of trust across all five emerging food technologies, while Croatian respondents were more cautious. These differences can be explained [125,126] by several factors. In India, the rapid pace of technological development, frequent public exposure to innovation, and integration of traditional health practices (e.g., Ayurveda) with modern functional foods contribute to greater openness. In contrast, Croatian consumers are influenced by the broader European context, where food technologies are often framed through precautionary regulation and risk-averse public discourse, which reinforces skepticism. Historical exposure [125,126] also plays a role: for example, the long-standing use of GMO cotton in India has normalized biotechnological applications, whereas in Croatia, public debates have often emphasized potential risks rather than benefits. There were small but significant cross-national differences in food neophobia with British and Swedish children being the most neophobic and significantly higher in food neophobia than Finnish children, who were the most neophilic [126].

Our findings confirm the importance of both cognitive and emotional trust in the acceptance of emerging food technologies, which is consistent with Xia et al. (2024), who emphasize that rational understanding of an innovation often precedes its emotional acceptance [100]. This pattern helps explain why respondents in Croatia demonstrated lower levels of trust in new technologies compared to respondents in India, the lack of prior knowledge and experience limits the development of both cognitive and emotional trust. Also, Califano et al. (2024) highlights perceptions of sustainability are central to the acceptance of urban agricultural technologies, although skepticism remains among some consumers [37]. These insights align with our results, suggesting that positive associations with sustainability can enhance willingness to embrace new technologies, but only when consumers are convinced of their practical benefits and reliability.

6. Conclusions

As societies face growing pressure to ensure sustainable, efficient, and resilient food systems, the role of public trust in emerging food technologies becomes increasingly critical. This study explored how consumers in two culturally and economically distinct countries (Croatia and India) respond to five major food innovations: genetically modified foods, 3D-printed food, lab-grown meat, nanotechnology in food applications, and functional foods. The findings clearly indicate that national context exerts the strongest influence on trust levels, with Indian participants showing significantly more openness toward all five technologies than their Croatian counterparts. These differences likely stem from variations in historical exposure to technological innovation, regulatory communication, and the integration of traditional beliefs with modern science.

Demographic characteristics such as gender and place of residence, which are often assumed to influence consumer behavior, proved to be statistically insignificant in most cases. This points to a deeper structural influence, where broader social narratives, cultural values, and the perceived role of technology in everyday life shape consumer responses more strongly than individual traits. While urban-rural distinctions emerged in interaction with national background, they were not presented as universally influential variables. This highlights the importance of viewing consumer trust not through isolated demographic lenses, but within a broader cultural and communicative framework.

Consumer trust in food technologies cannot be engineered overnight. It must be earned through consistency, openness, and a sincere acknowledgment of public concerns. As technological innovation in the food sector accelerates, building this trust will be essential, not only for market success but also for ensuring that food innovation serves public interest, respects cultural diversity, and supports the transition toward more sustainable food systems on a global scale.

The study advances theoretical understanding of consumer trust by framing it as a fluid and context-dependent phenomenon, rather than a fixed attitude toward technology. The comparison between Croatia and India demonstrates that trust in emerging food technologies is shaped not only by individual assessments of risks and benefits, but also by broader cultural narratives, previous exposure to innovation, and the perceived credibility of institutions. By combining cognitive and emotional dimensions of trust, the research enriches existing literature and points to the need to approach trust as a process that evolves within specific cultural and social settings.

In practical terms, the results provide guidance for different groups of stakeholders. For policymakers, the findings stress the importance of transparent communication and regulatory practices that acknowledge cultural differences in consumer perception. For the food industry, building acceptance requires a focus on communicating clear benefits, aligning new products with sustainability values, and providing unambiguous labeling. Educators and communicators also play a crucial role by increasing familiarity with novel technologies, thereby reducing uncertainty and food technology neophobia. Taking together, these measures can strengthen consumer confidence and support the responsible integration of new food technologies into everyday diets.

A limitation of this study is that some assumption tests (Box’s M and Levene’s test) indicated deviations from homogeneity, which may influence interpretation of the results. However, the use of Pillai’s Trace and the large sample size mitigate these issues. Future research could also consider complementary non-parametric approaches to further validate findings. In addition, several broader limitations should be acknowledged. First, the comparative scope was restricted to only two countries (Croatia and India), which limits the generalizability of the findings to other cultural or socioeconomic contexts. Second, while demographic variables such as gender and place of residence were included, other potentially relevant cultural and socioeconomic factors (e.g., income, education, dietary habits) were not captured in the model and may have influenced consumer attitudes. Third, the study relied on self-reported data, which is inherently subject to potential biases such as social desirability and subjective interpretation. Recognizing these constraints enhances transparency and provides a clearer basis for interpreting the results, while also offering directions for future research.