Diffusion of innovation in emerging food technologies: Adoption dynamics of cultured meat

Abstract

The contribution of emerging food technologies, such as cultured meat, to sustainability transitions depends on societal adoption processes rather than on technological performance alone. Drawing on Diffusion of Innovation (DoI) theory, this study examines the perceptual mechanisms shaping consumer adoption Intention for cultured meat in an early-stage transition context. Using survey data from 956 Romanian consumers and structural equation modeling, the study analyzes how innovation Knowledge and salience, perceived Risk and complexity, Compatibility with existing food practices, and perceived Relative advantage interact to influence adoption Intention. The results show that the Knowledge and salience construct is not directly associated with adoption Intention; instead, it is indirectly associated with Intention through lower perceived Risk and complexity and greater perceived Compatibility and Relative advantage. Perceived Risk and complexity emerge as major barriers, being negatively associated with adoption Intention and consumers' ability to recognize the technology's Relative advantage and value alignment. In contrast, Compatibility and Relative advantage are the main factors associated with adoption Intention. However, sensitivity analysis indicates that Risk and complexity show distinct structural patterns when modeled separately; therefore, their combined representation should not be interpreted as evidence that they are psychologically equivalent. The study contributes to food innovation research within sustainability transitions by conceptualizing innovation attributes as an interdependent system of perceptions that shape transition dynamics in socio-technical contexts, rather than as independent predictors of adoption. From a policy and governance perspective, the findings highlight the importance of reducing perceived complexity and aligning innovations with existing food practices to support sustainability-oriented food system transitions.

Graphical abstract

Highlights

• •Cultured meat adoption intention is examined using Diffusion of Innovation theory.
• •Adoption intention depends on perceptions, not only objective sustainability benefits.
• •Knowledge and salience is indirectly linked to cultured meat adoption intention.
• •Compatibility is positively linked to perceived advantage and adoption intention.
• •Risk and complexity are barriers to cultured meat adoption intention.

1. Introduction

1.1. Sustainability challenges

Food systems are the central arena in which climate mitigation, biodiversity protection, public health, and ethics converge (IPCC, 2022; Pang and Chen, 2024; Willett et al., 2019). Animal-derived foods occupy approximately 83% of global farmland and account for 56-58% of food-related greenhouse gas emissions, despite supplying only 37% of dietary protein and 18% of total caloric intake, underscoring a pronounced imbalance between environmental burdens and nutritional returns (Poore and Nemecek, 2018). Within this context, ruminant meat production, particularly beef, is associated with especially high greenhouse gas emissions, water demand, and biodiversity impacts compared to alternative protein sources (FAO, 2022).

As demand for animal-sourced foods continues to rise, livestock-related emissions have increased correspondingly. For instance, global meat and milk consumption rose by 24% and 22%, respectively, between 1990 and 2013, and this upward trend has persisted in subsequent years (IPCC, 2022). This demand-driven expansion is expected to continue, with global livestock production projected to increase by around 14% by 2029, particularly in middle-income countries undergoing dietary transitions (IPCC, 2022). Meanwhile, overall global food demand (which constitutes the bulk of agricultural commodity use) is also projected to grow steadily over the next decade, with average annual increases of roughly 1.3% (OECD and FAO, 2023).

1.2. Cultivated meat and terminology

A persistent cultural attachment to meat-centered diets reinforces these pressures (Petrescu-Mag et al., 2025a; Polyportis et al., 2025). In many regions, meat remains the focal element of meals and a key marker of food identity, making reductions in meat consumption socially and culturally challenging (van Dijk et al., 2023). Together, these dynamics have shifted research and policy debates away from incremental efficiency improvements toward more transformative strategies capable of reshaping protein production and consumption (Batubara et al., 2026; Petrescu-Mag et al., 2026).

Among emerging alternatives, cultured meat, also referred to as lab-grown, cultivated, artificial, or cell-based meat, has attracted attention as a technological innovation that seeks to preserve meat consumption while fundamentally altering its mode of production. Cultured meat is produced in vitro through the cultivation of animal cells, thereby reducing or eliminating the need for large-scale animal rearing and slaughter (Post, 2012). The terminology surrounding this technology has evolved alongside its scientific development and remains subject to scientific, regulatory, and industry debate. Earlier literature refers to these products using various names, such as cultured meat, in vitro meat, cell-based meat (or cell-based food), cell-cultured meat, artificial meat, and lab-grown meat. Cultivated meat has become increasingly preferred in recent years and has generally shown favorable consumer acceptance compared with several alternative terms (Malerich and Bryant, 2022). The FAO and WHO (2023) consultation on cell-based foods highlighted the need for consistent terminology and acknowledged that the use of the term meat raises regulatory, labeling, and consumer communication challenges due to its traditional association with products derived from slaughtered animals, a debate that has subsequently influenced national labeling policies and industry conventions (Buijs, 2023). Although consumer studies suggest that terminology can shape product perceptions, the evidence remains mixed. While cultivated and cultured meat are generally preferred over cell-based meat in some contexts (Szejda et al., 2021), other studies report more favorable responses to terms such as clean meat or animal-free meat, whereas lab-grown meat tends to evoke less positive evaluations (Bryant and Barnett, 2019; Siddiqui et al., 2022). More broadly, cultivated meat belongs to the category of alternative proteins, alongside plant-based, fermentation-derived, and insect-based products, and more generally to the broader class of meat replacers, which are classified as meat extenders or meat analogs depending on whether they partially replace meat or mimic whole-muscle products (Good Food Institute, n.d.; Petrescu-Mag et al., 2025b; Vallikkadan et al., 2023).

1.3. Environmental benefits and trade-offs

Life-cycle assessments frequently present cultured meat as a potentially more resource-efficient alternative to conventional livestock production, particularly with respect to land use and certain environmental pressures (e.g., water consumption, terrestrial acidification, energy demand) associated with ruminant systems (Sinke et al., 2023). For instance, El Wali et al. (2024) project that a transition to cellular agriculture by 2050 could reduce annual greenhouse gas emissions by 52%, use 83% less land, and decrease phosphorus demand by 53%. However, existing evidence remains incomplete, highly assumption-dependent, and contested. While early assessments suggested substantial reductions in land use and water requirements compared to conventional beef production (see also Tuomisto and Teixeira de Mattos, 2011), subsequent analyses emphasize that such comparisons are often partial and sensitive to methodological choices, including system boundaries, energy sources, and reference production systems (Chriki and Hocquette, 2020). Thus, estimates of water use in beef production vary widely depending on methodological assumptions, with cited figures substantially overstating actual freshwater consumption. Current assessments indicate that beef production typically requires approximately 550–700 L of water per kilogram, and most water inputs are associated with feed crop growth rather than direct animal consumption (Chriki and Hocquette, 2020). Moreover, land-use comparisons overlook that approximately 1.3 billion hectares of livestock grasslands are non-arable, thereby limiting the environmental relevance of land savings attributed to cultured meat (Chriki and Hocquette, 2020; Mottet et al., 2017).

Other research emphasizes that environmental performance remains highly dependent on energy inputs, but suggests that greenhouse gas emissions could be substantially lower than those of beef and comparable to or lower than pork and poultry if cultured meat production is scaled using low-carbon electricity (Smetana et al., 2015; Tavan et al., 2025). These findings position cultured meat as a contributor to sustainability objectives related to responsible consumption and climate action, while also underscoring the conditional nature of its environmental benefits.

1.4. Consumer acceptance

The sustainability potential of cultivated meat cannot be assessed solely by technological or environmental metrics (e.g., Geurs et al., 2025; Tang et al., 2024), and interdisciplinary research is needed (da Silva Nunes et al., 2025). Its contribution to food system transformation ultimately depends on social legitimacy and consumer acceptance (Matsuoka et al., 2023). Empirical research often shows that public attitudes toward cultured meat are characterized by ambivalence rather than uniform enthusiasm or rejection (Bryant and Barnett, 2018; van Dijk et al., 2023). While some consumers associate cultured meat with environmental responsibility and improved animal welfare, others express strong reservations related to food neophobia, perceived unnaturalness, safety concerns, and anticipated high prices (Hanan et al., 2024; Petrescu-Mag et al., 2025b; Siegrist and Hartmann, 2020; Van Loo et al., 2020). These perceptions are influenced by broader cultural meanings attached to meat, trust in food technologies, and normative understandings of what constitutes “natural” or acceptable food, resulting in substantial variation in acceptance across consumer segments and national contexts (Chia et al., 2024; Kamrath et al., 2025; Petrescu-Mag et al., 2025b; Wilks et al., 2024). In addition, consumers’ interpretations of food quality – shaped by intrinsic and extrinsic cues – play a key role in how environmental attributes are perceived and evaluated (Petrescu et al., 2022), further influencing acceptance of novel food technologies such as cultivated meat.

The diffusion of breakthrough innovations depends on interrelated antecedents and preconditions rather than isolated drivers, particularly in early-stage contexts (Fabra et al., 2023). Moreover, information exposure does not necessarily translate into adoption directly but operates through intermediate perceptual mechanisms such as knowledge formation and evaluation of innovation attributes (Chen et al., 2024). Building on this perspective, the present study conceptualizes innovation attributes as an interdependent system of perceptions shaping adoption Intention. This perspective is particularly relevant for culturally embedded and contested innovations such as food technologies, where adoption depends on how innovations are interpreted within existing practices and social meanings. In this context, resistance to cultured meat reflects not only concerns about safety or unfamiliarity but, more fundamentally, questions its relevance to everyday food practices. Consumers evaluate cultured meat against established meanings of meat, dietary habits, and social norms, raising the question of whether such a technologically complex product is perceived as a necessary response to sustainability challenges or as an imposed solution to a problem consumers do not fully recognize.

As a result, consumer acceptance emerges as a critical constraint on the diffusion of cultured meat, with implications for whether alternative protein transitions can move beyond niche innovation toward systemic change. Innovation adoption perspectives are therefore central to understanding these dynamics, as they emphasize that uptake depends less on technological performance than on how potential adopters perceive an innovation's value, compatibility (defined as its consistency with existing values, past experiences, and needs), and its broader legitimacy within everyday consumption contexts (Saaksjarvi, 2003).

Consumer acceptance of cultivated meat should be interpreted within the broader institutional context in which this innovation is introduced, as it develops within food systems dominated by established livestock regimes, incumbent firms, and vested economic interests rather than in isolation (Dueñas-Ocampo et al., 2023). In countries with large livestock sectors, the diffusion of cultivated meat may be shaped by lobbying activities, regulatory debates, and labeling controversies, while incumbent companies simultaneously invest in alternative proteins (Dueñas-Ocampo et al., 2023). Similarly, governmental priorities regarding food security and protein self-sufficiency influence the policy environment for cultivated meat adoption. In China, for example, support for domestic livestock production coexists with growing recognition of alternative proteins (Wang, 2022). Beyond the influence of livestock actors, consumer acceptance may also depend on broader cultural, economic, and food-environment characteristics (Lewisch and Riefler, 2023). This is particularly relevant in Central and Eastern Europe, where food practices are shaped by historically rooted informal networks and where trust may rely more strongly on interpersonal relationships than on formal or technocratic institutions (Trenouth and Sovová, 2025). Moreover, regulatory requirements can delay the transition from consumer interest to market availability, as illustrated by the multi-stage approval pathway for cultivated meat in the United States (Dueñas-Ocampo et al., 2023). Cross-national evidence further demonstrates that acceptance varies across countries. Delley et al. (2024) found that environmental, health, and price considerations differed between Swiss and Vietnamese consumers, while a recent review concluded that acceptance of plant-based and cultivated meat alternatives depends on national sociocultural and market conditions, including affordability, retail availability, and food system characteristics (Majcher, 2025). Accordingly, the findings of the present study should be interpreted within the Romanian context, recognizing that the diffusion of cultivated meat reflects not only individual perceptions but also the broader institutional and sociocultural environment.

1.5. Research objectives and contributions

Accordingly, the present study investigates consumer acceptance of cultured meat through an empirical survey of Romanian consumers, using Diffusion of Innovation (DoI) theory as an integrative analytical framework. Romania is a particularly relevant context for this analysis, as it combines a strong cultural attachment to meat-based diets (Petrescu et al., 2017) with limited public exposure to emerging food technologies, making consumer acceptance uncertain, both theoretically and practically.

The main objective of the study is therefore to examine how Romanian consumers evaluate cultured meat with respect to Knowledge and salience, perceived Risk and complexity, Compatibility, and Relative advantage, and how these perceptions jointly shape adoption Intention. While individual DoI attributes (Relative advantage, Compatibility, Complexity, Trialability, and Observability) have already been examined separately in the cultured meat literature (e.g., Bryant and Barnett, 2018; Chriki and Hocquette, 2020; Frewer et al., 2011; Hoek et al., 2011; Siegrist and Hartmann, 2020; Verbeke et al., 2015; Weinrich et al., 2020), these studies have relied on comparative or main-effects approaches (e.g., ANOVA-based group comparisons, principal component analysis, or path/regression models without interaction terms) rather than testing how DoI attributes interact with and mediate one another within a unified structural model. This study offers several novel contributions relative to this prior work. First, unlike previous studies that examined DoI attributes through comparative or main-effects approaches, this study integrates them into a single, comprehensive, perception-based structural model that tests their interrelationships and mediating pathways. Second, it demonstrates that Knowledge and salience is associated with adoption Intention indirectly by reshaping interconnected perceptions rather than directly driving it. Third, it highlights perceived Risk and complexity as central mechanisms that reduce both benefit recognition and perceived Compatibility. Fourth, it nuances the traditional DoI characterization of earlier adopters as more highly educated by showing that higher education does not necessarily correspond to greater adoption Intention in ethically and technologically contested food domains. Finally, the study advances understanding of sustainable food innovation by emphasizing social legitimacy and cognitive coherence as prerequisites for large-scale adoption.

2. Theoretical foundation and hypotheses development

2.1. DoI as a framework for cultivated meat adoption

This study draws on the DoI theory to explain how cultured meat may gain social acceptance. Initially developed by Rogers (1962) and refined in later editions (Rogers, 2003), DoI theory conceptualizes innovation adoption as a social process through which new ideas and technologies spread over time within a population. A central assumption of the theory is that adoption decisions are shaped less by the objective characteristics of an innovation and more by how potential adopters perceive it.

DoI theory has been widely applied to explain adoption processes across multiple domains, including finance, agriculture, and food systems (Ali et al., 2019; Taherdoost, 2018). More recently, DoI-based approaches have been used to analyze consumer acceptance of sustainable and novel foods such as organic products, 3D food printing, plant-based diets, and edible insects (Gonera et al., 2021; Guaqueta-Garcia et al., 2025; Li et al., 2021). Across these contexts, research consistently demonstrates that consumer perceptions of benefits, value alignment, and perceived difficulty are decisive factors influencing the acceptance of unconventional products.

Despite growing interest in cultured meat as a sustainable protein source, the DoI theory has been applied to explain consumer responses in this context only rarely. Nevertheless, existing research suggests that acceptance of cultured meat is strongly shaped by perception-based mechanisms that closely mirror the core DoI attributes. In particular, perceived Relative advantage, such as anticipated environmental benefits, improved animal welfare, and potential health or cost advantages, has been shown to positively influence acceptance (Bryant and Barnett, 2018; Weinrich et al., 2020). Compatibility also plays a critical role, as consumers are more receptive when cultured meat is perceived as aligning with their dietary practices, ethical values, and cultural norms, while perceived misalignment with notions of naturalness or tradition can generate resistance (Siegrist and Hartmann, 2020; Verbeke et al., 2015).

At the same time, high perceived Complexity, particularly related to unfamiliar production processes, scientific uncertainty, and food safety concerns, has been identified as a key barrier to adoption (Chriki and Hocquette, 2020; Frewer et al., 2011). Beyond these factors, limited opportunities for trialability, such as the ability to taste or sample cultured meat, may constrain acceptance by preventing consumers from forming experiential evaluations, an important mechanism in food-related decisions (Hoek et al., 2011). Finally, low observability, resulting from limited market presence and restricted social visibility, may further slow diffusion by reducing opportunities for social learning and normalization (Frick and Ishii, 2024). Kapur (1995) observes that the slow diffusion of innovations is attributed to informational externality and social learning, where firms prefer others to adopt first, potentially exacerbated by low observability.

Applying DoI theory, therefore, enables a systematic and integrative examination of how these interrelated perceptions jointly shape consumers’ Intentions to try or adopt cultured meat. In doing so, this study extends DOI theory to an emerging and controversial food domain. It provides insights relevant to both advancing theory on sustainable food innovation and informing communication and policy strategies to facilitate consumer acceptance.

Following Flight et al. (2011), we used Knowledge and salience, Risk and complexity, Relative advantage, and Compatibility as predictor variables for Intention. All four predictors were modeled as formative constructs, with their measurement items adapted from Flight et al. (2011) and detailed in Table A.1. The Intention variable captured respondents’ intention to partially replace conventional meat with cultured meat. Adoption Intention was measured with one item because the construct was defined as a single, global intention toward cultivated meat rather than as separate intentions to taste, purchase, recommend, or consume the product regularly. Although multi-item scales are generally preferable when a construct comprises several facets, single-item measures can be appropriate for concrete and narrowly defined constructs when the item is clearly worded (Diamantopoulos et al., 2012). The present measure should therefore be interpreted as global adoption Intention. Age, Gender, Education, Income, and Living environment were included as demographics in the model.

The construct originally labeled “Information” in the Flight et al. (2011) model was renamed here to “Knowledge and salience” to more accurately reflect the content of its indicators. It captures prior knowledge, perceived opportunities for product experience, perceived observability, and perceptions of the potential impacts of cultivated meat. It should therefore be interpreted as a context-specific informational and evaluative basis for adoption. Within this construct, Perceived impact is a cognitive evaluation formed partly through information and observation, not an objective measure of information received. The structural paths from Knowledge and salience are consequently interpreted as associations with a broader informational-perceptual basis for evaluating cultivated meat, rather than as causal effects of factual information alone. Thus, the present study introduces Knowledge and salience as an exogenous, higher-order construct that captures the knowledge and experiential basis from which consumers form perceptions of cultivated meat, thereby extending the Diffusion of Innovations framework. Knowledge and salience is not conceptualized as a sixth perceived innovation attribute. Rogers' five attributes – Relative advantage, Compatibility, Complexity, Trialability, and Observability – describe perceived characteristics of the innovation, whereas Knowledge and salience construct describes the prior knowledge, perceived availability of opportunities for experience, and perceived visibility of relevant consequences available to the potential adopter. This distinction is consistent with DOI research that examines antecedents and consequences of Rogers’ five attributes rather than treating them as a closed set of predictors (Kapoor et al., 2014). The five Rogers attributes therefore remain conceptually distinct; the model extends the framework by specifying how Knowledge about the innovation and innovation salience to a potential adopter may shape perceptions of those attributes in the context of cultivated meat. Knowledge and salience are particularly relevant to cultivated meat because consumers may be required to evaluate a highly unfamiliar food technology before direct consumption experience is available. Prior studies on cultivated meat show that awareness is limited and that information can influence perceptions and acceptance (Mancini and Antonioli, 2020; Verbeke et al., 2015). The Knowledge and salience construct is therefore modeled as a contextual antecedent of perception formation rather than as an additional innovation attribute.

Knowledge and salience is operationalized formatively through Knowledge, Trialability, Observability, and Perceived impact. Knowledge measures prior factual awareness; Trialability measures the opportunity to experience the product; and Observability and Perceived impact measure the perceived visibility of social, nutritional, health, environmental, animal-welfare, and market consequences. These dimensions are treated as complementary rather than interchangeable indicators because each captures a distinct aspect of the informational basis for evaluating cultivated meat.

The inclusion of Trialability and Observability as dimensions of Knowledge and salience does not eliminate or redefine their theoretical meaning in Rogers' framework. Trialability retains its original meaning as the extent to which an innovation can be experimented with on a limited basis, while Observability retains its meaning as the extent to which the innovation's consequences are visible. In the present context, these concepts are used to describe the informational and experiential conditions through which potential adopters can evaluate cultivated meat before adoption. This contextualization is consistent with evidence that trialability is related to information-seeking and appraisal processes in technology adoption (Lin and Bautista, 2017). The five Rogers attributes therefore remain conceptually distinct even though Trialability and Observability are used as dimensions within the higher-order Knowledge and salience construct for this specific empirical context.

This specification preserves the explanatory integrity of DOI in four ways. First, Knowledge and salience is not treated as a sixth Rogers attribute. Second, the substantive meanings of Relative advantage, Compatibility, Complexity, Trialability, and Observability are retained. Third, the formative measurement model reflects the fact that the Knowledge and salience domain comprises distinct factual, experiential, social, nutritional, health, environmental, animal-welfare, and market-related facets. Fourth, adoption Intention continues to be explained through perceived innovation characteristics and their Knowledge and salience antecedent. The model is therefore a contextual extension of DOI that makes explicit the role of knowledge and salient innovation-related cues in the evaluation of an unfamiliar food innovation, rather than a replacement of Rogers’ five-attribute framework.

Existing research supports modeling Knowledge and salience in relation to, rather than as a substitute for, Rogers’ attributes. A review of innovation adoption in construction identified existing knowledge as important for innovation decisions while retaining the five DOI attributes as the relevant perceived characteristics of the innovation (Weerapperuma et al., 2022). Consumer adoption research has likewise shown that product knowledge can have direct effects and alter relationships involving attitudes and social norms (Fu and Elliott, 2013).

In our model, the Complexity in use items were reverse-coded so that their scoring direction was aligned with the intended interpretation of perceived Complexity. Complexity in design and Risk items were retained in their original direction. The structural results were interpreted according to these coding decisions, with higher values on the recoded Complexity measure representing greater perceived Complexity and higher values on Risk representing greater perceived Risk.

2.2. Hypotheses

H1

Knowledge and salience is negatively related to Risk and complexity

DoI theory posits that uncertainty is a significant barrier to the adoption of innovation and that knowledge acquisition reduces both perceived Risk and perceived Complexity (Rogers, 2003). In the context of cultured meat, awareness and perception are therefore expected to mitigate consumer concerns regarding technological novelty, safety, and ethical implications. Well-informed individuals are less likely to perceive cultured meat as mysterious, unsafe, or difficult to understand. Rolland et al. (2020) confirmed that “awareness of cultured meat is the best predictor of acceptance,” while Siddiqui et al. (2022) noted that “educating the masses likely reduced the unfamiliarity with newly marketed products.”

Empirical research on novel food technologies consistently demonstrates that lack of information amplifies perceptions of Risk and complexity (Frewer et al., 2011; Siegrist, 2008). Specifically for cultured meat, studies show that transparency about production processes, ingredient composition, and regulatory oversight reduces perceived health and ethical risks (Bryant and Barnett, 2018; Chriki and Hocquette, 2020). The evidence suggests that explaining the production of cultured meat can positively influence consumer attitudes, but with important nuances. Falowo et al. (2022) found that after explaining the concept and production process, 53% of respondents became willing to eat cultured beef. Comparable findings from research on genetically modified foods and nanotechnology adoption confirm that increased knowledge reduces perceived complexity and uncertainty (Cox and Evans, 2008). Thus, higher levels of awareness are expected to be negatively related to perceived Risk and complexity. However, Siegrist et al. (2018) cautioned that informing participants about production can paradoxically increase acceptance of traditional meat.

H2

Knowledge and salience is positively related to Relative advantage

Relative advantage refers to the extent to which an innovation is perceived as superior to existing alternatives (Rogers, 2003). In the context of food innovation, consumer awareness plays a crucial role in shaping perceptions of benefits. When consumers are informed about the potential advantages of cultured meat, such as reduced environmental impact, enhanced animal welfare, and future affordability, they are more likely to evaluate it favorably relative to conventional meat.

Empirical studies show that knowledge about sustainability and ethical benefits increases the perceived usefulness of cultured meat (Rolland et al., 2020; Weinrich et al., 2020). Specifically, Piochi et al. (2022) demonstrated that information about environmental impact, human safety, and animal welfare improved participants' favor towards cultured meat compared to control groups. Similar relationships have been observed in research on plant-based meat alternatives and organic foods, where information exposure strengthens perceived Relative advantage (Hoek et al., 2011; Janssen et al., 2016). Accordingly, information is expected to positively influence perceptions of cultured meat's Relative advantage.

H3

Knowledge and salience is positively related to Compatibility

Compatibility refers to the extent to which an innovation aligns with consumers’ values, beliefs, experiences, and lifestyles (Rogers, 2003). Information is a prerequisite for consumers to assess whether a novel product fits their personal and social contexts. In the absence of information, consumers may be unable to meaningfully evaluate such alignment.

Research on cultured meat suggests that informed consumers are more likely to perceive consistency with ethical values (e.g., animal welfare), environmental concerns, and health motivations (Bogueva and Marinova, 2020; Siegrist et al., 2018). Similar findings are reported in studies on organic and functional foods, where knowledge enhances perceived value congruence (Aertsens et al., 2009; Hansen et al., 2018). Therefore, increased information and awareness are expected to positively influence the perceived Compatibility of cultured meat.

H4

Compatibility is positively related to Relative advantage

Compatibility and Relative advantage are closely linked constructs within DoI theory. When an innovation fits consumers’ existing values and needs, its benefits become more salient and meaningful. Rogers (2003) emphasizes that innovations perceived as compatible are more likely to be evaluated as advantageous.

Empirical evidence supports this relationship in food innovation contexts. Consumers who perceive alternative proteins as aligned with their dietary identity or ethical orientation are more likely to recognize their advantages (Grunert et al., 2014; Vainio et al., 2016). At the organizational level, Qader et al. (2023) demonstrate that Compatibility strengthens evaluations of Relative advantage by facilitating integration into existing practices and belief systems. By analogy, when cultured meat aligns with consumers’ health goals, environmental values, or moral beliefs, it is more likely to be perceived as offering superior benefits compared to conventional meat. Bryant and Barnett (2020) found substantial market potential for cultured meat, with consumers primarily identifying animal welfare and environmental benefits.

H5

Risk and complexity are negatively related to Relative advantage

Perceived Risk and complexity can overshadow perceived benefits, even when objective advantages exist. Prior research on food technologies indicates that high levels of uncertainty, perceived unnaturalness, or safety concerns reduce consumers’ ability to recognize product benefits (Frewer et al., 2003; Siegrist and Hartmann, 2020)

In the context of cultured meat, studies indicate that concerns about health risks, ethical ambiguity, and technological novelty diminish perceived environmental and animal-welfare advantages (Wilks and Phillips, 2017). Similar effects have been documented in the adoption of edible insects, where perceived complexity diminishes perceived usefulness. McDade and Collins (2019) note that complexity is a key barrier to entomophagy, identifying it as a critical factor in innovation diffusion theory that can impede widespread acceptance. Consequently, higher perceived Risk and complexity are expected to negatively influence perceived Relative advantage.

H6

Risk and complexity are negatively related to Compatibility

Perceived risk and complexity also undermine perceived Compatibility. When a novel food elicits fear, disgust, or moral unease, consumers may perceive it as misaligned with their lifestyle or values. Research on cultured meat indicates that perceptions of unnaturalness and scientific opacity reduce perceived fit with traditional food norms (Bryant and Barnett, 2020; Verbeke et al., 2015).

Comparable patterns are observed in research on genetically modified foods and nanotechnology, where perceived Risks erode value congruence and cultural acceptance (Cox and Evans, 2008). Therefore, higher perceived Risk and complexity are expected to negatively affect perceived Compatibility.

H7

Risk and complexity are negatively related to Intention

Behavioral Intention is strongly influenced by perceived uncertainty. DoI theory identifies complexity as a major barrier to adoption decisions (Rogers, 2003). Empirical evidence from research on novel foods shows that perceived safety risks and technological uncertainty reduce willingness to try and adopt innovations (Frewer et al., 2011; Siegrist et al., 2018).

Specifically, in the context of cultured meat, higher perceived Risks are associated with lower Intentions to taste or purchase the product (Bryant and Dillard, 2019; Slade, 2018). Thus, perceived Risk and complexity are expected to negatively influence the Intention to consume cultured meat.

H8

Relative advantage is positively related to Intention of adoption

Relative advantage is one of the most robust predictors of adoption Intention in DoI theory. When consumers perceive clear benefits relative to existing alternatives, the likelihood of adoption increases (Rogers, 2003). In cultured meat studies, perceived advantages related to sustainability, animal welfare, health, and future affordability are positively associated with Intention to consume (Weinrich et al., 2020).

Similar relationships have been documented for plant-based and organic foods, where perceived benefits directly translate into higher purchase Intentions (Janssen et al., 2016; Teixeira et al., 2021). Accordingly, Relative advantage is expected to positively influence adoption Intention.

H9

Compatibility is positively related to Intention

Perceived Compatibility facilitates adoption by reducing psychological resistance and increasing perceived appropriateness. Research on food consumption indicates that innovations that align with consumers' identities and routines are more readily accepted (Pliner and Hobden, 1992). Donato et al. (2024) further showed that consumers are more receptive to innovations that don't threaten their existing identity. For instance, meat-eaters were more willing to try plant-based foods when presented through a “common identity” framework.

In the case of cultured meat, alignment with ethical values, social norms, dietary preferences, and lifestyle considerations significantly predicts Intention to consume (Bogueva and Marinova, 2020; Engel et al., 2024). Therefore, perceived Compatibility is expected to influence adoption Intention positively.

H10a-H10e

Demographic factors and adoption Intention

Demographic characteristics are also expected to shape Intention to consume cultured meat. Age (H10a) is typically negatively related to acceptance of food innovations. Multiple studies show that younger consumers exhibit greater openness to cultured meat, whereas older individuals express higher skepticism (Slade, 2018; Weinrich et al., 2020).

Gender differences (H10b) have also been reported, with men generally expressing greater acceptance and willingness to try cultured meat than women (Siegrist et al., 2018; Verbeke et al., 2021). The evidence spans diverse geographical contexts. Liu et al. (2023) found that men are more likely to accept cultured meat in Southern European countries. Similarly, Wilks and Phillips (2017) found that men were more receptive to in-vitro meat in the United States. This pattern may reflect lower food technology neophobia among men.

Socioeconomic factors are also relevant. Higher income (H10c) and education (H10d) levels are associated with greater openness to food innovations due to increased scientific literacy and trust in technology (Kimenju and De Groote, 2008; Rabadán, 2021; Sajdakowska et al., 2018; Stubelj et al., 2025). Rogers (2003) notes that early adopters tend to have higher socioeconomic status. However, Priest et al. (2003) critically challenge the assumption, finding only weak relationships between educational levels and biotechnology acceptance. Rodríguez-Entrena and Salazar-Ordóñez (2013) revealed that scientific literacy's impact is complex, with significant variations between scientific-technical and social-humanistic educational backgrounds.

Finally, residence (H10e) plays its role. Evidence also points to geographic variation in alternative-protein consumption: Zaleskiewicz et al. (2024) report regional differences within European countries, as well as rural-urban disparities in food-related choices. Urban consumers generally show stronger acceptance of alternative proteins than rural consumers, likely due to greater exposure to innovation and weaker ties to traditional livestock production (Hoek et al., 2011; Slade, 2018).

3. Methodology

3.1. Survey instrument and data collection

Data were collected through an online survey, yielding 956 valid responses. Participant recruitment and data collection were carried out by a professional research company. The target population consisted of Romanian residents aged 18 years or older. All participants provided written informed consent prior to participation, which was administered by the data collection agency. The research received Ethical approval no 7151/24.05.2024 from Babes-Bolyai University, Cluj-Napoca, Romania. The sample was designed to be nationally representative with respect to gender, age, and geographical distribution (across Romania's nine development regions). The mean age of respondents was 46.4 years. In terms of gender identity, 48.1% of participants identified as women, 51.7% as men, and 0.2% as non-binary or other. The questionnaire was administered in Romanian, as the study targeted a Romanian-speaking population. Face validity was assessed and confirmed by a panel of three subject-matter experts. Prior to full deployment, the survey instrument was pilot-tested with 21 individuals to evaluate the clarity and appropriateness of wording. Based on participant feedback, minor revisions were made to improve comprehension. The pilot study included specific questions inviting respondents to report unclear items and propose improvements. To ensure a shared understanding of key concepts, the questionnaire provided respondents with a definition of cultured meat before answering the questions. The definition was as neutral as possible, focusing on the technical description of the production process, as follows: “Cultured meat aims to imitate conventionally produced meat (referred to here as conventional meat, i.e., ordinary meat from animals such as pigs, chickens, cattle, etc.). Specifically, lab-cultured meat is produced by culturing a small number of cells (stem cells and tissue-derived cells) harvested from animals. These cells are multiplied in a bioreactor over a period of 2-8 weeks, in a growth medium rich in oxygen and nutrients, amino acids, glucose, vitamins, inorganic salts, growth factors, and other proteins.” The survey included closed-end questions related to DoI constructs, adapted after Flight et al. (2011) – Information (which was adapted as Knowledge and salience in the present study), Risk and complexity, Relative advantage, Compatibility, and Intention – and demographics (as detailed in the last part of section 2.1 and Table A.1).

3.2. Data analysis and measurement model

Data analysis proceeded in two stages. Descriptive analyses were carried out in SPSS to summarize sample characteristics, followed by PLS-SEM using SmartPLS to test the proposed research model and hypotheses.

Partial least squares structural equation modeling (PLS-SEM) was used to estimate the proposed model. PLS-SEM is well-suited for studies that emphasize prediction and explanation of adoption Intentions for emerging technologies, rather than strict theory confirmation, which aligns with the exploratory application of Diffusion of Innovation theory to cultured meat (Chin, 1998; Hair et al., 2019; Rogers, 2003). PLS-SEM was preferred over covariance-based SEM (CB-SEM) because several core innovation attributes (Knowledge and salience, Risk and complexity, Relative advantage, and Compatibility) were specified as formative constructs, for which CB-SEM poses identification and estimation constraints (Hair et al., 2017). Consistent with prior research on alternative proteins and sustainability-oriented food innovations, PLS-SEM enables robust modeling of complex, early-stage adoption processes characterized by heterogeneous consumer perceptions (Rombach et al., 2022). Moreover, PLS-SEM is particularly appropriate when the analysis adopts a predictive perspective and the structural model is complex, involving multiple constructs, indicators, and relationships (Hair and Alamer, 2022).

4. Results

4.1. Descriptive analysis

Table A.1 reports mean scores for all measured variables. Overall, respondents exhibited generally low to moderate scores across diffusion of innovation variables. Within the Knowledge and salience construct, Observability was lowest (1.39), and 40.40% of participants reported correct Knowledge about cultured meat, which is relatively high for a new product category. Perceived impacts of cultured meat were unclear (M = 2.63). Although Triability was low in absolute terms (M = 2.30), its level was noteworthy given the product's unavailability on the market. For Risk and complexity, average perceived Risk was moderate (M = 3.58), while Complexity in use and design scored 3.26 (2.74, original, not reversed value) and 3.62, respectively. Discontinuity had the highest mean (3.96) but was not significant in the model and was excluded, along with Customizability and Social advantage. Relative advantage and Product performance had similar scores (2.62 and 2.58). Within Compatibility, Personal (M = 2.15) and Social compatibility (M = 2.30) were low, while Volition scored highest among all items (3.96). The Intention to adopt cultured meat (to replace some part of conventional meat) was low (M = 2.17).

4.2. The measurement model

The measurement model (Fig. 1) was specified as a hierarchical component model. Four higher-order latent independent constructs (Risk and complexity, Knowledge and salience, Compatibility, and Relative advantage) were modeled as formative constructs, each formed by multiple lower-order components. These lower-order components (e.g., Knowledge and Observability within the Knowledge and salience construct) were measured reflectively using multiple questionnaire items. The dependent construct, Intention to adopt cultured meat, was measured using a single indicator. Following established PLS-SEM guidelines, the adequacy of the formative measurement model was assessed prior to evaluating the structural model (Hair et al., 2017). Model fit was evaluated using the standardized root mean square residual (SRMR), which was 0.05, indicating an acceptable model fit. Multicollinearity among formative indicators was examined using variance inflation factors (VIF), which ranged from 1.026 to 3.220 and remained below recommended thresholds, indicating that multicollinearity was not a concern. All formative outer weights were statistically significant (Table 1), supporting the relevance of the indicators forming the higher-order constructs. After establishing the adequacy of the measurement model, the structural model was subsequently assessed.

Fig. 1.

Measurement model.

Table 1.

Assessment of the formative measurement model.

Formative Construct	Formative Indicator	Outer Weight	T-Value	P-Value	Multicolinearity (VIF)
Risk and complexity	Complexity	0.785	20.338	0.001	1.170
	Risk	0.389	8.295	0.001	1.170
Knowledge and salience	Knowledge	0.145	2.854	0.004	1.026
	Observability	0.198	3.146	0.002	1.517
	Perceived impact	0.174	3.144	0.002	1.177
	Triability	0.823	17.945	0.001	1.376
Compatibility	Personal compatibility	0.597	13.771	0.001	3.220
	Social compatibility	0.422	9.745	0.001	3.190
	Volition	−0.081	3.163	0.002	1.081
Relative advantage	Relative advantage	0.467	13.068	0.001	1.530
	Product performance	0.651	19.43	0.001	1.530

The formative higher-order constructs were assessed based on the magnitude and statistical significance of their outer weights and indicator collinearity. All formative dimensions had statistically significant outer weights (p ≤ .004), supporting their contribution to the respective higher-order constructs. Collinearity was not problematic, with VIF values ranging from 1.026 to 3.220 (below the commonly applied threshold of 5).

4.3. Structural model

The results of the structural model are presented in Fig. 1, Fig. 2. Fig. 1 reports the estimated path coefficients between constructs, the outer weights of the formative indicators, and the coefficient of determination (R²) for the dependent construct.

Fig. 2.

Path coefficients and P-values in the structural model.

Fig. 2 presents the path coefficients together with their levels of statistical significance. Path significance was assessed using the bootstrapping procedure with 500 resamples. To evaluate the robustness of the results, the analysis was repeated using 2000 bootstrap samples, which produced consistent path coefficients and significance levels, confirming the stability of the estimates.

The results provide support for most of the proposed hypotheses (Table 2). Innovation awareness and perception-related attributes played a central role in shaping perceptions of other innovation characteristics. Knowledge and salience had a negative and significant effect on Risk and complexity (β = −0.488, sig. = 0.001), supporting H1. Knowledge and salience had a positive and significant effect on Relative advantage (β = 0.087, sig. = 0.001), supporting H2, and on Compatibility (β = 0.238, sig. = 0.001), supporting H3. Compatibility exerted a positive and significant effect on Relative advantage (β = 0.586, sig. = 0.001), supporting H4. Risk and complexity negatively affected key adoption-related perceptions. Specifically, Risk and complexity had negative and significant effects on Relative advantage (β = −0.223, sig. = 0.001), Compatibility (β = −0.580, sig. = 0.001), and Intention to adopt (β = −0.120, sig. = 0.001), supporting H5–H7. Relative advantage, in turn, had a positive and significant effect on Intention to adopt (β = 0.338, sig. = 0.001), providing support for H8. Compatibility had a positive and significant effect on Intention to adopt cultured meat (β = 0.369, p = 0.001), supporting H9. With respect to sociodemographic variables, Income had a positive and significant effect (β = 0.056, sig. = 0.001), supporting H10c, while Education had a negative and significant effect on Intention to adopt (β = −0.070, sig. = 0.003), partially supporting H10d (the relationship exists, but the direction is different from the hypothesized one). The effects of other sociodemographic variables were not statistically significant (H10a, b, e). Overall, the relationships among the core constructs were generally consistent with the theoretically specified nomological network, providing support for nomological validity.

Table 2.

Evaluation of the path coefficients.

Pathway From → to	Path coefficient	T-Value	P-Value	Hypothesis
Age → Intention	0.004	0.191	0.849	Not Supported
Compatibility → Intention	0.369	6.654	0.001	Supported
Compatibility → Relative advantage	0.586	15.81	0.001	Supported
Education → Intention	−0.07	2.982	0.003	Partially Supporteda
Gender → Intention	0.005	0.25	0.803	Not Supported
Income → Intention	0.056	2.58	0.01	Supported
KaSb → Compatibility	0.238	7.584	0.001	Supported
KaS → Relative advantage	0.087	3.365	0.001	Supported
KaS → Risk and complexity	−0.488	16.453	0.001	Supported
Living environment → Intention	0.007	0.351	0.726	Not supported
Relative advantage → Intention	0.338	7.666	0.001	Supported
Risk and complexity → Compatibility	−0.58	22.563	0.001	Supported
Risk and complexity → Intention	−0.12	3.212	0.001	Supported
Risk and complexity → Relative advantage	−0.223	6.252	0.001	Supported

^aThe relationship exists, but the direction is different from the hypothesized one.

^bKaS = Knowledge and salience.

A supplementary PLS multi-group (PLS-MGA) analysis comparing respondents with and without tertiary education revealed significant differences in three structural relationships. Following the conventional PLS-MGA criterion, between-group differences were considered statistically significant when the PLS-MGA p-value was below .05 or above .95. Compatibility was more strongly associated with Intention among respondents with tertiary education than among those without tertiary education (β = 0.446 vs. .192; PLS-MGA p = .008), whereas Relative advantage was more strongly associated with Intention among respondents without tertiary education (β = 0.536 vs. .252; PLS-MGA p = .997). The relationship between Risk and complexity and Relative advantage was also stronger among respondents with tertiary education (β = −.274 vs. −.118; PLS-MGA p = .978).

A further PLS-MGA compared urban (n = 849) and rural (n = 107) respondents. Significant urban–rural differences were observed for several core structural relationships. Knowledge and salience was more strongly associated with Compatibility among rural than urban respondents (β = 0.446 vs. .192; PLS-MGA p = .996), while Relative advantage was more strongly associated with Intention among rural respondents (β = 0.589 vs. .305; p = .977). Risk and complexity was more strongly negatively associated with Compatibility among urban respondents (β = −.609 vs. −.469; p = .968) and with Relative advantage (β = −.253 vs. −.015; p = .980). No significant urban–rural difference was observed for the direct relationship between Risk and complexity and Intention (p = .696). MICOM supported compositional invariance for the constructs except Risk and complexity; therefore, the between-group differences involving Risk and complexity should be interpreted cautiously.

In addition to path coefficients, the predictive relevance and explanatory power of the structural model were assessed using Stone–Geisser's Q², R², and adjusted R² (Table 3). All endogenous constructs exhibited positive Q² values, ranging from 0.141 to 0.567, indicating that the model demonstrates predictive relevance (Henseler et al., 2009). The R² values further suggest that the model explains a substantial proportion of variance in the endogenous constructs, indicating an acceptable level of explanatory power (Chin and Newsted, 1999; Hair et al., 2017). The adjusted R² indicates that the model accounts for approximately 59% of the variance in Intention to adopt cultured meat, which can be considered satisfactory for behavioral research. To avoid overstating predictive claims, the results were interpreted with a clear distinction between explanation and prediction. R² and adjusted R² were used to assess the extent to which the model explains variance in adoption Intention within the sample, while Stone–Geisser's Q² was employed to evaluate predictive relevance through a resampling procedure. The positive Q² values indicate that the model retains predictive relevance beyond purely in-sample estimation, supporting the robustness of the findings.

Table 3.

The values of Stone – Geisser's Q², R², and R²_adj.

Constructs	SSO	SSE	Q2 (=1-SSE/SSO)	R2	R2Adj.
Intention	956	414.112	0.567	0.586	0.583
Compatibility	2868.00	1937.40	0.324	0.527	0.526
Relative advantage	1912.00	959.218	0.498	0.653	0.652
Risk and complexity	1912.00	1643.09	0.141	0.238	0.237

A sensitivity analysis was conducted by modeling Risk and complexity separately. The separated model had a higher estimated-model SRMR than the combined model (0.071 versus 0.053) (indicating a weaker model from this point of view), but slightly higher adjusted R² values for Compatibility and Intention (indicating a stronger model from this perspective). It also explained 27.4% of the variance in Complexity, whereas Risk was only weakly explained (adjusted R² = 0.013). The explained variance for Relative advantage was virtually unchanged. The separated structural paths showed that Complexity was negatively associated with Compatibility and Relative advantage, but not significantly associated with Intention. Risk was negatively associated with Compatibility, Intention, and Relative advantage. Thus, the combined Risk and complexity construct may conceal differences in the strength and significance of the two mechanisms. The results involving the combined construct should consequently be interpreted as a general association with perceived uncertainty and difficulty, rather than as evidence that Risk and complexity are psychologically identical. The combined model was retained for the main analysis because it produced the lower SRMR, while the separated model is reported as a robustness analysis.

5. Discussion

5.1. Descriptive patterns of consumer perceptions

The descriptive findings align with broader evidence that consumer familiarity and perceptions of cultured meat remain modest, even as interest in novel protein technologies grows. Prior research consistently documents that awareness of cultured meat is limited, and perceptions are often ambivalent, with many consumers uncertain about the product's benefits and risks (Bryant and Barnett, 2020). The relatively high proportion of respondents with correct Knowledge (40.4%) in your sample may reflect increasing exposure to discussions about alternative proteins, yet the low Observability and uncertainty about impacts mirror documented barriers such as perceived unnaturalness and safety uncertainty that constrain acceptance in many populations (Rosenfeld and Tomiyama, 2023). Moderate scores for Risk and complexity also resonate with findings that perceived risk and technological unfamiliarity are central to consumer hesitation about cultured meat, often driving concerns about health, naturalness, and food technology distrust (Pakseresht et al., 2022). Likewise, relatively low Compatibility and adoption Intention parallel studies showing that consumers' established food habits and identities contribute to hesitation about integrating cultured meat into conventional diets, particularly where taste and familiarity are salient drivers of meat choice (Pakseresht et al., 2022). Overall, the results highlight the importance of information provision, awareness, and framing in influencing attitudes toward this disruptive food innovation.

5.2. Discussion of hypotheses and structural model

This study examined consumer Intention to adopt cultured meat through the lens of DoI theory, emphasizing the interrelationships among information, perceived Risk and complexity, Compatibility, Relative advantage, and behavioral Intention. The empirical support for the proposed framework provides nuanced insights into how consumers cognitively process highly novel food technologies. The observed relationships are consistent with the theoretically proposed direction, but they should be interpreted as associations rather than confirmed causal effects. Because the data were collected cross-sectionally, the analysis cannot establish temporal precedence. Consumers with favorable attitudes may actively seek information or perceive the impacts of cultivated meat more positively.

5.2.1. Knowledge and salience, risk and complexity, relative advantage, and compatibility perceptions (H1-H3)

Knowledge and salience-related attributes played an indirect but critical role in the adoption process. The findings support H1, demonstrating that a significant negative effect of Knowledge and salience on perceived Risk and complexity. Rather than simply increasing familiarity, Knowledge and salience appears to function as a sense-making mechanism that enables consumers to organize the innovation cognitively. This result extends prior findings on halal supply chain adoption (Qader et al., 2023) and takaful adoption (Ali et al., 2019), in which awareness reduced ambiguity regarding compliance and operational feasibility. Multiple studies reveal that providing information can partially improve acceptance, but with important caveats. Rolland et al. (2020) found that information increased acceptance, with personal benefits being most effective. However, Wilks et al. (2021) suggest that acceptance is more driven by emotional responses such as disgust and fear than by analytical reasoning, thereby limiting the impact of information. In the context of cultured meat, information likely demystifies production processes and mitigates anxieties related to safety and unnaturalness, which are dominant concerns in the acceptance of novel foods.

Support for H2 and H3 indicates positive effects of Knowledge and salience on perceived Relative advantage and Compatibility, suggesting that greater awareness and salience of cultured meat may be linked to more favorable perceptions of the innovation's potential benefits and fit with existing lifestyles. The findings underscore the importance of communication and transparency in facilitating the diffusion of cultured meat. Importantly, these effects suggest that information can do more than communicate benefits. It activates evaluative frameworks through which consumers assess whether cultured meat fits their moral values, dietary routines, and sustainability concerns. Similar patterns were observed in studies of organic food (Li et al., 2021) and 3D food printing (Guaqueta-Garcia et al., 2025), in which knowledge enabled consumers to recognize advantages that would otherwise be cognitively inaccessible. Thus, Knowledge and salience emerges as a foundational factor associated with downstream perceptions rather than being directly associated with Intention.

5.2.2. Interplay between Compatibility and Relative advantage (H4)

The significant positive relationship between Compatibility and Relative advantage (H4) indicates that greater perceived alignment with existing values is associated with greater perceived benefits. This finding is consistent with DoI-based research on food and logistics systems, in which innovations perceived as compatible are more readily evaluated as useful and superior (Qader et al., 2023). For cultured meat, Compatibility likely reduces psychological resistance by framing the product as an extension of existing meat consumption rather than a disruptive substitute. A pre-registered UK study with 1094 participants (Fidder and Graça, 2023) found that framing cultivated meat around familiar consumption practices significantly improved attitudes. This “consumption frame” increased perceptions of naturalness, familiarity, and taste. This is particularly important given the strong cultural attachment to meat-centered diets, which may otherwise impede recognition of benefits, even when sustainability advantages are acknowledged.

5.2.3. Risk and complexity and their downstream effects (H5-H7)

The results provide strong support for H5, H6, and H7, highlighting perceived Risk and complexity as central barriers to adoption. Risk and complexity negatively affected Relative advantage, Compatibility, and Intention, underscoring its broad relationship with adoption-related perceptions and Intention. These findings echo prior work on food technologies, in which uncertainty eclipses perceived benefits (Siegrist and Hartmann, 2020; Wilks and Phillips, 2017).

Notably, Risk and complexity appear to undermine adoption Intention. When an innovation is perceived as complicated or risky, consumers struggle to integrate it into their existing belief systems. Studies on edible insects and genetically modified foods showed that complexity reduced perceived fit with everyday practices (Cox and Evans, 2008; McDade and Collins, 2019). In the meat sector, Krings et al. (2022) found that food technology neophobia leads consumers, particularly omnivores, to evaluate clean meat more negatively. Woelken et al. (2024) noted that some cell innovations may pose cognitive barriers due to complex technologies that are difficult for consumers to understand. In practice, given the present findings, technological unfamiliarity may prevent consumers from translating abstract sustainability claims into personally relevant advantages.

5.2.4. Positive drivers of adoption intention: relative advantage and compatibility (H8-H9)

The positive effects of Relative advantage (H8) and Compatibility (H9) on Intention reaffirm their status as core predictors of adoption within DoI theory. Consumers are more willing to consider cultured meat when it is perceived to offer meaningful benefits over conventional meat. However, the comparatively weaker effect of Relative advantage relative to Compatibility suggests that Relative advantage alone is insufficient when perceived Risk remains high, or the product is misaligned with personal or social norms. Consumers may acknowledge environmental or ethical benefits while still rejecting the product due to unresolved concerns. This pattern parallels findings in cultured meat and organic food research, where perceived benefits motivate interest but do not guarantee adoption without perceived fit and safety (Castellani et al., 2025; Li et al., 2021).

Compatibility, by contrast, appears to play a stabilizing role by reducing perceived threats to identity and routine. When cultured meat is perceived as congruent with existing consumption practices, Intention increases even under residual uncertainty. This finding resonates with research showing that innovations framed as identity-consistent are more readily accepted (Donato et al., 2024). Thus, the structural model results provide strong support for the central role of perceived Compatibility in shaping consumers' Intention to adopt cultured meat. Compatibility emerged as the strongest predictor of adoption Intention, highlighting the importance of alignment between cultured meat and consumers’ existing values, dietary habits, and social norms. This finding is consistent with Diffusion of Innovations theory, which emphasizes Compatibility as a key driver of adoption, particularly in the early stages of diffusion when uncertainty about the innovation remains high.

5.2.5. Demographic effects and their implications (H10)

Among demographic variables, Education and Income significantly influenced Intention, while Age, Gender, and Living environment did not. The negative association between Education and Intention should, however, be interpreted cautiously. The supplementary multi-group analysis indicated education-related heterogeneity in the adoption process: Compatibility was more strongly associated with Intention among respondents with tertiary education, whereas Relative advantage was more strongly associated with Intention among those without tertiary education. The relationship between Risk and complexity and Relative advantage also differed between the two groups. These findings suggest that education may be associated with differences in how innovation attributes relate to adoption Intention, but they do not establish the mechanism underlying the negative direct association between Education and Intention. The positive effect of Income on Intention may reflect greater openness to novel foods among higher-income consumers. The non-significant effects of Age, Gender, and Living environment suggest that resistance to cultured meat is less about sociodemographic segmentation and more about shared cultural meanings and cognitive evaluations. This aligns with recent work emphasizing that acceptance of novel foods is shaped by symbolic and normative considerations rather than simple demographic profiles (Castellani et al., 2025; van Dijk et al., 2023). Lewisch and Riefler (2023) further reinforces this by highlighting that societal and cultural factors are more influential in explaining consumer responses to cultured meat than simple demographic categorizations. Consistent with Diffusion of Innovations theory, adopter characteristics influence adoption heterogeneity; however, in this study, the strength of the effects of innovation attributes was generally greater (higher βs) than those of sociodemographic variables.

5.3. Theoretical and practical implications

Theoretical implications. This study makes several theoretical contributions to the literature on innovation diffusion and sustainable food systems. First, it extends DoI theory to the emerging and highly contested domain of cultured meat, demonstrating that DoI remains a robust framework even when innovations challenge deeply embedded cultural and moral norms in food. While prior research has applied DoI to food-related innovations such as organic food, halal supply chains, and novel food technologies (Ali et al., 2019; Li et al., 2021; Qader et al., 2023), this study shows that DoI attributes operate through interdependent perception chains rather than isolated effects in the context of radical food innovations.

Second, the findings refine the DoI theory by highlighting the mediating role of cognitive coherence. Knowledge and salience does not have a significant direct effect on adoption Intention. Instead, its effects are observed through Risk and complexity, Compatibility, and Relative advantage. This suggests that DoI attributes should be conceptualized as a system of interrelated cognitive evaluations rather than independent predictors. Such a perspective complements and extends prior DoI-based food studies, which often emphasize direct effects on Intention without unpacking these internal perceptual mechanisms (Guaqueta-Garcia et al., 2025). By empirically identifying these indirect pathways, the study contributes to a more process-oriented understanding of innovation adoption in sustainability contexts.

Third, the strong and consistent negative effects of perceived Risk and complexity underscore the importance of uncertainty as a central theoretical construct in food innovation adoption. Unlike incremental innovations, cultured meat evokes epistemic, ethical, and symbolic uncertainty simultaneously. The findings suggest that future DoI applications in food systems should explicitly distinguish between functional complexity (the difficulty of understanding production) and symbolic complexity (perceived unnaturalness and value conflicts). Integrating these dimensions may enhance DoI's explanatory power in contexts where innovations disrupt cultural meanings rather than merely consumption habits.

Finally, the negative association between Education and Intention, together with the education-related differences identified in the multi-group analysis, suggests that the role of education in innovation adoption may be more nuanced than a uniformly facilitating socioeconomic effect. This finding invites future research to examine the mechanisms underlying education-related differences in food innovation diffusion and to integrate insights from risk perception and science communication literature into DoI theory.

Practical implications. From a practical perspective, the findings offer actionable guidance for policymakers, industry stakeholders, and communication professionals seeking to facilitate consumer acceptance of cultured meat. Effective policy design should account not only for technological and economic factors but also for stakeholder perceptions and acceptance, as implementation outcomes often depend on how policies are experienced and evaluated by target groups (Boa et al., 2025; Petrescu et al., 2025). First, communication strategies should prioritize reducing perceived complexity rather than merely amplifying sustainability or animal-welfare benefits. The results suggest that benefits are unlikely to persuade consumers if the technology is perceived as opaque or risky. Accordingly, stakeholders should provide clear, standardized explanations of production processes using non-technical language, supported by visual tools such as diagrams or short videos. Avoiding excessive scientific jargon is particularly important, as it may increase perceived complexity. This aligns with innovation diffusion research, which shows that complexity is a primary barrier to adoption, particularly in early stages of diffusion (Rogers, 2003).

Second, experiential exposure should be expanded to mitigate perceived Risk. Given the strong negative effect of Risk on Intention, policymakers and firms could support tasting events, pilot restaurants, or food innovation showcases that enable consumers to evaluate sensory attributes directly. Such initiatives enhance Trialability and Observability, which are known to accelerate diffusion and reduce uncertainty in food innovations (Guaqueta-Garcia et al., 2025).

Third, communication and marketing efforts should emphasize compatibility with existing food practices. Rather than framing cultured meat as a disruptive alternative, it should be positioned as “real meat produced differently”, embedded in familiar dishes and culinary traditions. Collaborations with chefs and food professionals may further normalize the product within established gastronomic contexts, leveraging the strong positive effect of Compatibility on adoption Intention. Prior studies on acceptance of cultured meat indicate that perceived misalignment with food norms and notions of naturalness remains a major source of resistance (Bryant and Barnett, 2018; Siegrist and Hartmann, 2020)

Finally, although living environment was not directly associated with Intention in the full-sample model, the supplementary multi-group analysis indicates that some structural relationships differ between urban and rural respondents. This heterogeneity suggests that segmentation strategies should consider living environment alongside consumers’ values, food identities, and openness to innovation when tailoring communication strategies. Given that cultured meat remains in an early diffusion stage, long-term adoption will depend less on rapid commercialization and more on building social legitimacy through transparent governance, public dialogue, and gradual normalization. Consumer research reinforces this, showing that adoption is nuanced. Szejda et al. (2021), in a study of US and UK consumers, found that 80% were open to trying cultured meat; however, preferences varied by generation and depended on factors such as terminology, perceived health benefits, and government assurances. Moreover, Dueñas-Ocampo et al., 2023 argue that fully realizing the promise of alternative proteins depends on sustained public funding, long-term strategic planning, and coordinated efforts to strengthen regulation and social acceptance.

Limitations and future research directions. Although the study is based on a representative national sample, its cross-sectional design limits causal inference. Findings rely on self-reported intentions, which may not fully translate into actual consumption behavior once cultured meat enters the market. The analysis focuses primarily on cognitive perceptions, while emotional, sensory, and moral responses were not directly examined. The Romanian context, despite its relevance, may limit transferability to countries with different food cultures and innovation exposure. Future research should employ longitudinal and experimental designs to track changes in attitudes over time. Cross-national comparisons would help test the robustness of the DoI framework. Incorporating sensory trials and framing experiments could further clarify the mechanisms of adoption. Perceived impact may overlap conceptually with perceived benefits, risks, and compatibility, and, therefore, the results should not be interpreted as demonstrating that these evaluations arise solely from Knowledge and salience alone. The construct should be understood as a context-specific composite capturing the conditions under which respondents evaluate an unfamiliar innovation. The Intention variable does not assess the potentially distinct intentions to try, purchase, recommend, or consume cultivated meat, and future research should examine these dimensions using a validated multi-item scale. Moreover, although the definition provided to respondents was constructed as a neutral, technical description of the production process rather than an appeal to sustainability or animal-welfare benefits, its effect was not tested. Future studies could adopt a between-subjects design comparing neutral, positively framed, and critically framed definitions to formally test framing effects on perceived Risk and Relative advantage ratings. The model does not include potentially relevant predictors such as food neophobia, trust in food technology, meat consumption frequency, and environmental consciousness. Because these factors may be associated both with the innovation perceptions included in the model and with adoption Intention, their omission may introduce unobserved confounding. Consequently, some estimated relationships may partly capture variance associated with these unmeasured factors, potentially leading to over- or underestimation of individual path coefficients. The direction and magnitude of such bias cannot be determined from the present data because these variables were not measured. Future research should incorporate these factors to assess the robustness of the estimated relationships. The sample was predominantly urban (88.81%), with a substantially smaller rural subsample (n = 107), limiting the representativeness of rural Romanian consumers. The supplementary PLS-MGA identified urban–rural differences in several structural relationships, indicating that some associations in the model may vary by living environment. However, these findings should be interpreted cautiously because of the unequal group sizes and the lack of compositional invariance for Risk and complexity. The findings should therefore not be generalized unconditionally to rural Romania or to Central European populations. A further methodological limitation concerns the assessment of the formative higher-order constructs. Because the questionnaire did not include independent global criterion measures for these constructs, their convergent validity could not be formally assessed through redundancy analysis. Consequently, the assessment of the formative measurement models was limited to the magnitude and significance of the outer weights and to the collinearity among their dimensions. The measurement quality of these higher-order constructs should therefore be interpreted with this qualification. Future studies should include independent global criterion measures at the questionnaire design stage to enable formal redundancy analysis. Finally, the cross-sectional design limits causal interpretation. Longitudinal or experimental research is needed to establish temporal precedence and distinguish the proposed pathway from reverse or reciprocal relationships.

6. Conclusion

This study examined consumer acceptance of cultured meat through the lens of the DoI theory, focusing on the relationships among Knowledge and salience, Risk and complexity, Compatibility, Relative advantage, and adoption Intention. The findings demonstrate that consumer responses to cultured meat are primarily perception-driven rather than benefit-driven. Knowledge and salience plays a foundational role in the proposed model, with a negative effect on Risk and complexity and positive effects on Compatibility and Relative advantage. Information reduces perceived Risk and complexity while enhancing perceptions of Compatibility and Relative advantage. However, perceived Risk and complexity emerge as important barriers, exerting negative effects not only on Intention but also on the perceptions through which consumers recognize benefits and value alignment. These results confirm that, for highly novel food technologies, unresolved uncertainty can overshadow sustainability and ethical advantages. The combined Risk and complexity construct should nevertheless be interpreted with caution. Sensitivity analysis showed that Risk and complexity exhibit distinct structural patterns when modeled separately, including a significant direct association between Risk and Intention but not between Complexity and Intention. The combined construct therefore captures a broader domain of perceived Risk and difficulty but may mask meaningful differences between these mechanisms and should not be interpreted as evidence that they are psychologically equivalent.

The study further shows that Compatibility and Relative advantage are key proximal drivers of Intention, but their effects depend on prior reductions in perceived Risk and complexity. Demographic influences were limited. Thus, Income, and Education mattered, while Age, Gender and Living environment did not, suggesting that resistance to cultured meat is rooted more in cultural meanings and cognitive evaluations than in simple sociodemographic differences. Overall, the findings reinforce the relevance of DoI theory for understanding emerging food technologies while demonstrating the need to adapt it to contexts characterized by ethical ambiguity, technological opacity, and strong food identities. From a broader perspective, the results indicate that the contribution of cultured meat to sustainable food system transformation will depend less on technological readiness alone and more on social legitimacy, communication strategies, and the gradual normalization of cultured meat within everyday food practices.

Ethics declaration

This study was performed in compliance with relevant laws, regulatory frameworks and guidelines where the research took place. This study was approved by the Babes-Bolyai University (Approval No. 7151/24.05.2024).

Consent to participate

Survey participants gave their informed consent to participate.

Consent to publish

All authors agree with the publication of the present paper. No other publishing consents are applicable.

Ethical statement

The research received Ethical approval no 7151/24.05.2024 from Babes-Bolyai University, Cluj-Napoca, Romania.

CRediT

Ruxandra Malina Petrescu-Mag: Conceptualization, Methodology, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing; Hamid Rastegari: Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing; Dacinia Crina Petrescu: Conceptualization, Methodology, Data curation, Funding acquisition, Writing – original draft, Writing – review & editing.

Funding. Acknowledgement

This work was supported by a grant from the Romanian Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI, project number ERANET-JPI-HDHL-ComMEATted, within PNCDI IV.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Handling Editor: Professor Alejandro G.Marangoni

Annex.

Table A.1.

Constructs, components, questionnaire items, response options, and descriptive results

Formative Construct	Formative Component *	Questions #	Answer options	Descriptive result (Average response value, unless otherwise stated)
Knowledge and salience	Knowledge *	Which of the following characteristics of cultured meat did you know BEFORE THIS QUESTIONNAIRE? Select all that apply to you. a)I knew beforehand that it was produced in a laboratory a)b)I knew beforehand that it was produced from a few cells harvested from animals a)c)I knew beforehand that it is not sold in Romania a)d)I knew beforehand that this cultured meat is NOT meat from animals (pork, chicken, beef, etc.) that contains preservatives, colorants, flavor enhancers, antibiotics, etc. a)e)I knew beforehand that special conditions are needed to grow meat in a lab.	Select all that apply.	Correct answer (percentage of total sample): 40.40%
	Observability *	Please show your agreement with the following statements: a)I have noticed a growing trend of people willing to try cultured meat. b)I have noticed a growing trend of people rejecting cultured meat. c)The nutritional benefits of cultured meat are obvious to me. d)The nutritional disadvantages of cultured meat are obvious to me.	1 = Total disagreement, …, 5 = Total agreement	1.39
	Perceived impact *	What is your opinion on the following issues? a)The positive impact of cultured meat on human health is evident to me. b)The negative impact of cultured meat on human health is evident to me. c)The positive impact of cultured meat on the environment is evident to me. d)The negative impact of cultured meat on the environment is evident to me. e)The positive impact of cultured meat on animal welfare is evident to me. f)The negative impact of cultured meat on animal welfare is evident to me. g)The positive impact of cultured meat on the food market in Romania is evident to me (for consumers, farmers). h)The negative impact of cultured meat on the food market in Romania is evident to me (for consumers, farmers).	1 = I consider that the impact is NOT AT ALL obvious, …, 5 = I consider that the impact is VERY obvious	2.63
	Triability *	Please show your agreement with the following statements: a)Cultured meat is available for free to be tried/tasted in Romania. b)Cultured meat can be tried/tasted at a certain price in Romania.	1 = Total disagreement, …, 5 = Total agreement	2.30
Risk and complexity	Complexity in use *	Please show your agreement with the following statements: a)The concept of cultured meat is easy to understand. b)Preparing food with cultured meat seems simple. c)I assume that cultured meat tastes the same as conventional meat. d)I assume that the texture of cultured meat is similar to that of conventional meat. e)I assume that the ingredients in cultured meat are easy to recognize and understand. f)The benefits of cultured meat are easy to understand. g)The risks of cultured meat are easy to understand.	1 = Total disagreement, …, 5 = Total agreement	3.26 (2.74, value not reversed)
	Complexity in design *	Please show your agreement with the following statements: a)Cultured meat is a product obtained through advanced technology. b)Cultured meat is scientifically and biologically complex.	1 = Total disagreement, …, 5 = Total agreement	3.62
	Risk *	Please show your agreement with the following statements: a)Cultured meat raises legal issues related to its production and consumption. b)The consumption of cultured meat poses health risks. c)The production of cultured meat poses risks to the natural environment. d)Cultured meat poses risks related to animal welfare [such as the use of fetal bovine serum (FBS), which is required for the growth of cultured meat cells. FBS is collected from bovine fetuses obtained from pregnant cows during slaughter. It is usually harvested through cardiac puncture in the unborn calf, without any form of anesthesia]. e)Cultured meat poses risks related to the disappearance of certain animal breeds that will no longer be raised for human consumption (for example, certain breeds of chickens, pigs, etc. may disappear if they are no longer needed for human consumption, when people stop buying them because they consume cultured meat). f)Cultured meat endangers the livelihoods of poultry, pig, and other livestock farmers. g)Cultured meat serves the interests of large corporations. h)Cultured meat threatens national economic security. i)Cultured meat endangers the health and natural evolution of the human species.	1 = Total disagreement, …, 5 = Total agreement	3.58
	Discontinuity	Please show your agreement with the following statements: a)Cultured meat involves new, cutting-edge production technology. b)Cultured meat is new to the world. c)Cultured meat offers radically new benefits and risks.	1 = Total disagreement, …, 5 = Total agreement	3.96
Relative advantage	Relative advantage *	Please show your agreement with the following statements: a) Cultured meat is healthier than conventional meat. b)Cultured meat offers better nutritional value (vitamins, amino acids, fats, proteins, etc.) compared to conventional meat. c)I believe that cultured meat is tastier than conventional meat. d)Cultured meat has a lower negative impact on the environment than conventional meat (in terms of resource consumption—water, energy, land, etc., CO2 emissions, etc.). e)Cultured meat ensures animal welfare better than conventional meat. f)Cultured meat will be cheaper to produce in the near future compared to conventional meat. g)Cultured meat will be cheaper to buy in the near future compared to conventional meat.	1 = Total disagreement, …, 5 = Total agreement	2.62
	Product performance *	Please show your agreement with the following statements: a)Cultured meat will have the qualities it claims to have. b)The quality of cultured meat will be reliable and stable in terms of its characteristics. c)Cultured meat will have the expected qualities.	1 = Total disagreement, …, 5 = Total agreement	2.58
	Customizability	Please show your agreement with the following statement: a)Cultured meat can be customized according to consumer requirements.	1 = Total disagreement, …, 5 = Total agreement	3.36
Compatibility	Personal compatibility *	Please show your agreement with the following statements: a)Cultured meat aligns with my dietary preferences. b)Cultured meat meets my health needs. c)Cultured meat aligns with my values regarding animal welfare. d)Cultured meat aligns with my values regarding environmental protection. e)Cultured meat fits my lifestyle. f)Cultured meat is in line with my religious beliefs.	1 = Total disagreement, …, 5 = Total agreement	2.15
	Social compatibility *	Please show your agreement with the following statements: a)The consumption of cultured meat is socially acceptable. b)If I ate cultured meat, my friends and family would approve. c)Many of my friends would want to eat cultured meat.	1 = Total disagreement, …, 5 = Total agreement	2.30
	Volition *	What do you think the future situation will be regarding your freedom to choose whether or not to consume cultured meat? a)When cultured meat becomes widely available on the market, do you think you will be influenced to consume it because it will be present everywhere (while conventional meat will be harder to obtain) and will have a better price compared to conventional meat? b)When cultured meat becomes widely available on the market, do you think you will be influenced by your friends and colleagues to consume it? c)When cultured meat becomes widely available on the market, do you think you will be influenced to consume it because it will be incorporated into many products and will be difficult to avoid?	1 = I will be very strongly influenced to consume it, …, 5 = I will have complete freedom to choose to do what I want – to consume it or to avoid it.	3.96
	Social advantage	Please show your agreement with the following statement: a)I would be viewed positively by people around me if I consumed cultured meat.	1 = Total disagreement, …, 5 = Total agreement	1.97
Intention to adopt innovation	Intention to replace conventional meat with cultured meat *	How well do the statements below reflect what you will do if the opportunity arises? a)I intend to replace some of the conventional meat I eat with cultured meat when it becomes available on the market.	1 = I'm sure I will not do this, …., 5 = I'm sure I will do this	2.17
Demographics
Age *		Open answer	Average: 46.39 years
Gender *		Answer options: F; M; Non-binary; other	F: 48.12%; M: 51.67%; Non-binary: 0.1%; other: 0.1%
Education *		Answer options: a) Up to 8 years of schooling; b) Between 9 and 12 (or 13) years of schooling; c) Bachelor's degree; d) Master's degree; e) Doctoral degree	Percentage of total sample: a) 0.94%; b) 28.24%; c) 46.13%; d) 22.70%; e) 1.99%
Average monthly net income *		Answer options: a) Up to 2500 lei/month; b) 2501–5000 lei/month; c) 5001–10,000 lei/month; d) 10,001–15,000 lei/month; e) More than 15,001 lei/month. (1 Euro = 5 lei)	Percentage of total sample: a) 16.00%; b) 47.50%; c) 31.28%; d) 4.18%; e) 1.05%
Living environment *		Answer options: Urban; Rural	U: 88.81%; R: 11.19%