Robot Chefs as Both a Threat and an Experience: The Roles of Anthropomorphism, Job Insecurity, and Culinary Students’ Experience Intentions

Robot Chefs as Both a Threat and an Experience: The Roles of Anthropomorphism, Job Insecurity, and Culinary Students’ Experience Intentions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robot Chefs as Both a Threat and an Experience: The Roles of Anthropomorphism, Job Insecurity, and Culinary Students’ Experience Intentions Nurgül BOZ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8678823/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid proliferation of service robots and artificial intelligence is reshaping tourism sector jobs and redefining future workforce expectations. Regarding robot chefs, research has largely investigated the perceptions of customers and existing employees rather than those of emerging hospitality professionals. Accordingly, this study draws on the anthropomorphism framework to examine how culinary students’ perceptions of robot chefs affect experience intention and willingness to pay a premium, negative attitudes, and perceived job insecurity. Data were collected from university students studying gastronomy to test the hypotheses using partial least squares structural equation modeling (PLS-SEM). The findings showed that robot chef anthropomorphism directly and positively affects experience intention and reduces negative attitudes toward robots. Perceived innovation directly strengthens experience intention, while perceived job insecurity increases negative attitudes. Negative attitudes toward robots do not significantly affect experience intention, suggesting that students remain open to interacting with robot chefs in terms of their professional learning motivation and future job roles. Finally, experience intention strongly predicts greater willingness to pay a premium. These findings contribute to the relevant literature by indicating that robot chefs should be positioned in culinary education not only as a technological innovation but as an element creating experience-based value. Robot chefs Anthropomorphism Job insecurity Experience intentions Culinary students Willingness to pay a premium Figures Figure 1 Figure 2 1 Introduction The hospitality industry has long been conceptualized as a high-contact, labor-intensive service sector (Seyitoğlu et al. 2021 ). However, the increasing prevalence of artificial intelligence (AI)-powered service robots has reopened discussions about how to organize service production and professional role boundaries. The global market is expected to grow from $ 3.87 billion in 2024 to over $ 11 billion by 2037 (Nester 2025 ). Service robots offer significant advantages for the hospitality industry regarding service reliability, hygiene, operational consistency, and managerial innovation (Kumar et al. 2020 ; Wang et al. 2026 ). This technological transformation is increasingly reshaping food production, particularly where production processes are central, such as restaurants and professional kitchens (Hu, & 2025 ). Pizam et al. 2024 ). A striking example is robot chefs, which not only automate service but also directly participate in food preparation and cooking. Modern cooking robots have evolved from simple robotic systems to AI-powered smart kitchen solutions (Shin et al. 2025 ). Unlike traditional food industry automation, robot chefs can apply multiple recipes, make decisions based on sensory input, maintain hygiene standards, and accelerate operations (Seyitoğlu et al. 2025 ; Sochacki et al. 2024). However, these developments represent not only an increase in technical capacity but a turning point that may redefine the human dimension of the gastronomy profession, the understanding of mastery, and the boundaries of the chef-labor relationship. Research has primarily investigated robot-based restaurant applications from the consumer’s perspective, regarding behavioral outcomes like attitudes toward technology, intention to experience, and perceived value (Kazakova et al. 2025 ; Lu et al. 2019 ; Tuomi et al. 2021 ). Fewer studies have investigated the perceptions and psychological responses of the sector’s future workforce, including gastronomy students, who are expected to be directly affected by these technologies (Cifci et al. 2024 ; Seyitoğlu et al. 2022; Wakelin-Theron, 2021 ). For students, the issue goes beyond simply accepting or rejecting workplace robots to interaction with them, reconfiguration of professional roles, and post-graduation employment prospects. Thus, gastronomy students are a critical sample group that can simultaneously experience both curiosity and perceived opportunities regarding robot chefs, as well as concerns about future job insecurity. Their assessments can generate evidence-based conclusions regarding how gastronomy education should be adapted to work environments transformed by automation. This study adopts the Computers Are Social Actors (CASA) theoretical approach to explain culinary art students’ perceptions of robot chefs. The CASA approach argues that individuals tend to evaluate technology as a “social actor” when interacting with it, especially when human-like cues are present, which can trigger social-emotional responses like trust, closeness, or discomfort (Nass and Moon, 2000 ). More specifically, AI-powered humanoid service robots can shape users’ emotional evaluations through language use, interactivity, and the ability to adopt social roles (Gambino et al. 2020 ). These anthropomorphic features can significantly influence emotional responses and interaction intentions (Song et al. 2024 ; Song and Kim, 2022 ). Consequently, an important starting point for explaining students’ intentions to experience robot chefs is anthropomorphism. Combined with job displacement concerns, this concept suggests a dual dynamic that can also promote negative attitudes toward robots. A key factor determining the direction of these attitudes is innovativeness, meaning openness and willingness to try new technologies, thereby influencing individuals’ perceptions of robots as opportunities or threats. Individuals high in innovativeness perceive robot technologies more as opportunities and are therefore less resistant (Lu et al. 2019 ; Parvez et al. 2022 ). For gastronomy students, high innovativeness includes both openness and the capacity to accept the transformation of their profession while identifying new roles within it. Students low in innovativeness may perceive robot chefs as threatening their professional identity and employment. The proliferation of robot technologies in the hospitality sector may cause significant uncertainty for gastronomy students regarding future employment due to the risk of human labor displacement (Brougham and Haar 2018 ; Cifci et al. 2024 ). These perceptions are an important cognitive and emotional basis for developing negative attitudes toward robots. Studies of tourism and hospitality students’ post-graduation employment intentions have examined numerous determinants, including employability, entrepreneurial motivation, salary expectations, self-efficacy, and perceived education quality (Kahraman and Demirdelen Alrawadieh 2021; Luong and Lee 2021 ). However, they have not systematically examined how the economic transformation shaped by robotic technologies and job insecurity concerns affects these students’ professional commitment and post-graduation intentions. Finally, intention to experience robot chefs may not only indicate psychological curiosity; rather, it may indicate a valuation signal promoting economic behaviors willingness to pay a premium behavior. Consumer behavior research has revealed a meaningful relationship between perceived value and payment behavior in technology-mediated service experiences, suggesting that this relationship is worth examining regarding robotic service encounters (Chuah et al. 2022 ; Hong et al. 2025 ). Accordingly, the present study: positions gastronomy students as actors who understand their professional future within this transformation rather than remaining passive observers of automation; tests the dual dynamic of robot chef anthropomorphism, associated with both intention to experience and negative attitudes; and examines how these psychological mechanisms relate to economic outcomes like willingness to pay a premium. 2 Literature review and hypotheses development 2.1 Service robots, gastronomy, and the future workforce By integrating robotic technologies with culinary arts converge, robotic gastronomy is significantly transforming processes like food preparation and customer service (Ma et al. 2023). In particular, robots could account for approximately 25% of tourism sector employees by 2030 (Yıldız and Hökelekli 2025 ). Research has primarily investigated the impact of service robots in the hospitality and gastronomy sectors in terms of customer acceptance and employees’ attitudes. Customer attitudes and experience intentions toward robot-based service experiences are shaped by elements like perceived benefit, entertainment, trust, and anthropomorphism (Belanche et al. 2020 ; Lu et al. 2019 ; Kazakova et al. 2025 ). In contrast, employees associate robotization with negative outcomes like perceived job insecurity, skill devaluation, and occupational stress (Brougham and Haar, 2018 ; Frey and Osborne, 2017 ). The proliferation of robot chefs that can partially or fully automate preparation and cooking processes, particularly in restaurant kitchens, has made these issues more visible (Ivanov et al. 2020; Seyitoğlu et al. 2025 ). Fewer studies have investigated the views of gastronomy students regarding robot chefs, who are neither customers nor employees. Nevertheless, as the sector’s future workforce, it is critical to examine this group’s perceptions regarding the potential effects of automation on their professional future, given that perceptions shaped by education can affect longer term professional identity construction, career expectations, and adaptability to technology (Brougham and Haar 2018 ). Indeed, robotization may reduce students’ motivation to seek tourism and hospitality careers by reducing their long-term commitment to the sector (Seyitoğlu et al. 2022) and increasing perceptions that robot technologies threaten human employment opportunities (Wakelin-Theron 2021 ). Such findings indicate that gastronomy students’ evaluations of robot chefs do not simply reflect general attitudes toward technology; rather, they relate to students’ professional and employment expectations. 2.2 Robot chef anthropomorphism and intention to experience Anthropomorphism refers to the degree that individuals perceive robots as possessing human-like characteristics like intelligence, conscious movement, warmth, and interest (Martin et al. 2020 ). Human-robot interaction researchers consider anthropomorphism to be a fundamental psychological mechanism shaping attitudes toward robots (Epley et al. 2007 ). The CASA approach proposes that technological entities exhibiting human-like cues encourage positive emotional responses by triggering users’ unconscious interpersonal communication norms (Fox and Gambino 2021 ). Service robots exhibiting anthropomorphic features are perceived as more social, interactive, and trustworthy, which then strengthens intention to interact with and experience robots (Wirtz et al. 2018 ). Similarly, perceiving restaurant and service robots as having human-like characteristics significantly increases intention to experience them personally (Belanche et al. 2020 ; Lu et al. 2019 ). Thus, anthropomorphism is both a cognitive evaluation and important determinant of behavioral intentions. For gastronomy education students, anthropomorphic perception of robot chefs involves encountering a new technological application and an opportunity to experience future kitchen practices opportunity to experience the future kitchen practices of the future. This suggests the following hypothesis: H1. Robot chef anthropomorphism is positively associated with gastronomy students’ intention to experience robot chefs. 2.3 Robot chef anthropomorphism and negative attitudes toward robots Research shows that perceiving robot chefs as exhibiting human-like characteristics influences whether individuals evaluate them just as tools or as professional actors that may compete with human labor (Belanche et al. 2020 ; Lu et al. 2019 ). Individuals who consider mastery, creativity, and human labor as fundamental components of the professional gastronomy identity while perceiving robot chefs anthropomorphically may experience threat and replacement anxiety. That is, they evaluate robot chefs both for their functional contributions and symbolic meanings regarding the profession’s future. These uncertainties regarding anthropomorphic robot chefs and the future of human labor may be heightened for gastronomy students, who are still forming their professional identity. In particular, individuals perceiving that robots are approaching human capabilities may have stronger fears of job replacement and loss of control (Cifci et al. 2024 ). In short, while anthropomorphism may sometimes support technology acceptance, it can also promote negative attitudes toward robots in fields like gastronomy with strong professional identities. This suggests the following hypothesis: H2. Robot chef anthropomorphism is positively associated with gastronomy students’ negative attitudes toward robots. 2.4 Innovativeness, anthropomorphism, and intention to experience Innovativeness is a fundamental personal tendency to adopt new technologies (Agarwal and Prasad 1998 ). Hence, it significantly shapes perceptions of robot technologies in that individuals high in innovativeness perceive them as providing learning and development opportunities rather than causing uncertainty or threat. This in turn makes them less resistant (Lu et al. 2019 ; Parvez et al. 2022 ). For gastronomy students, innovativeness goes beyond openness to technology to include the tendency to embrace professional transformation and develop new roles accordingly. Regarding complex, uncertain technologies like robot chefs, innovative individuals try to make sense of and evaluate them within a social framework, which may make them more likely to perceive robot chefs as having human-like characteristics (Lu et al. 2019 ; Seyitoğlu et al. 2025 ). In contrast, individuals low in innovativeness tend to evaluate robot chefs as more threatening to professional identity and employment (Cifci et al. 2024 ). Thus, innovativeness may be an individual difference factor influencing how intensely gastronomy students attribute anthropomorphic characteristics to robot chefs. This suggests the following hypothesis: H3. Gastronomy students’ innovativeness is positively associated with their perceptions of robot chef anthropomorphism. In addition, innovativeness increases intention to interact with new technologies. For example, innovative individuals are more willing to interact with robot chefs because they consider this to be valuable for professional learning and career development (Belanche et al. 2020 ; Lu et al. 2019 ). Thus, innovativeness may also strengthen gastronomy students’ intentions to directly experience robot chefs, which suggests the following hypothesis: H4 . Gastronomy students’ innovativeness is positively associated with their intention to experience robot chefs. 2.5 Robot-induced job insecurity, negative attitudes, and behavioral outcomes The proliferation of robot technologies in the service sector increases uncertainty regarding future employment opportunities. Particularly in areas like kitchens, where human labor, skill, and creativity are central, robot chefs can increase concerns regarding job replacement (Brougham and Haar 2018 ). Gastronomy students may show higher perceived job insecurity due to fears regarding fewer post-graduation employment opportunities and the profession’s non-sustainability. Indeed, individuals perceiving robot technologies as threatening employment have stronger negative attitudes toward them (Frey and Osborne 2017 ; Parvez et al. 2022 ). In gastronomy and tourism specifically, perceived job insecurity is a fundamental driver of negative attitudes towards robots (Brougham and Haar 2018 ; Cifci et al. 2024 ; Koo et al. 2021 ; Wakelin-Theron, 2021 ). This suggests the following hypothesis: H5. Perceived job insecurity is negatively associated with gastronomy students’ intention to experience robot chefs. Behavioral intention research demonstrates that attitudes toward a technology determine intention to interact with and experience that technology (Venkatesh et al. 2012 ) by framing robot-based services as risky, alien, or avoidable experiences. This weakens intention to interact with them (Lu et al. 2019 ; Wakelin-Theron, 2021 ). Regarding gastronomy students, this suggests the following hypothesis: H6 . Perceived job insecurity is positively associated with gastronomy students’ negative attitudes toward robots. 2.6 Intention to experience and willingness to pay a premium Intention to experience is an important indicator of the perceived value attributed to services or experiences. Regarding robot restaurants, individuals who strongly desire to experience something evaluate robot-based services as more innovative, differentiated, and exclusive, which is also associated with higher willingness to pay a premium (Belanche et al. 2020 ; Kazakova et al. 2025 ). This suggests that gastronomy students who intend to experience robot chefs are more likely to interpret experiences as both consumption activities and opportunities for professional learning and preparation. The experience economic approach posits that individuals are willing to pay higher prices, not only for services offering functional benefits, but also for experiences offering symbolic and experiential value (Pine and Gilmore 1999 ). This suggests that gastronomy students who intend to experience robot chefs will also associate this experience with higher value, and hence be more willing to pay, as in the following hypothesis: H7. Intention to experience robot chefs increases gastronomy students’ willingness to pay a premium. 3 Methodology 3.1 Measurement The measurement tools were adapted from scales whose validity and reliability have been previously demonstrated in the gastronomy, tourism, and service robot literature. Innovativeness was measured using three items from Agarwal and Prasad ( 1998 ). Robot-induced job insecurity was measured using four items from Vander Elst et al. (2014). Negative attitudes toward robots, specifically anxiety and interaction resistance, were measured using four items from Nomura et al. ( 2006 ). Robot chef anthropomorphism was measured using five items from Zhu and Chang ( 2020 ). Intention to experience robot chefs, specifically robot-based gastronomic experiences, was measured using three items from Dodds et al. ( 1991 ). Finally, willingness to pay a premium for robot chef restaurant experiences was measured using three items from Kazakova et al. ( 2025 ). To ensure the survey items’ linguistic accuracy and conceptual equivalence back-translation was applied (Huang et al. 2021 ). First, two bilingual tourism academics translated the initial English-language survey into Turkish. Then, to minimize potential translation errors and shifts in meaning, by two gastronomy experts proficient in both Turkish and English and a tourism academic retranslated the Turkish questionnaire into English. This iterative process continued until both language versions were conceptually and linguistically consistent. The final Turkish-language questionnaire, which was administered in Türkiye, had two sections: items about demographic characteristics (gender, age, and previous experience interacting with robots); scales for the research variables, with responses measured using a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). 3.2 Sample and data collection The study population was undergraduate gastronomy students, selected because this group represents future culinary arts professionals whose perceptions and expectations regarding robot chef technologies are being shaped early in their professional careers, thereby enabling examination of the preliminary effects of technology-driven professional transformations. As of 2025, 95 universities in Türkiye offer gastronomy programs (61 state run; 34 foundation run, YÖK, 2025 ). Eight universities were selected using purposive and convenience sampling to ensure institutional and geographical diversity and feasible data collection. The study was conducted with the approval of the Ethics Committee of ... University and in accordance with ethical principles. Data collection, which was conducted between December 10–25, 2025, via an online survey using Google Forms, obtained 391 valid responses. Participation was voluntary, no personal data were requested, and it was clearly stated that participants could withdraw at any time. Sample size adequacy was calculated using G*Power v3.1.9.7 software (Faul et al. 2009 ). The result indicated that a minimum of 77 participants was sufficient to test a medium effect size (f²=0.15) at 80% power and a 5% significance level. However, to obtain more reliable results in partial least squares structural equation modeling (PLS-SEM) analyses, it is recommended to triple this number (Ringle et al. 2014 ). Accordingly, the minimum sample size was set at 231 participants; hence, the sample of 391 participants more than met these methodological requirements for reliably testing the research model. 3.3 Sample profile Regarding the participants’ demographic characteristics, 64.2% were female and 35.8% male. The participants were mostly young students in the early or middle stages of their higher education programs, with 44.2% aged 18–20, 42.5% aged 21–23, and 13.3% aged 24–26. Regarding educational level, about two thirds (66.8%) were enrolled in undergraduate programs and one third (33.2%) in associate degree programs. 4 Results Data analysis was performed using PLS-PLS-SEM because it provides robust and flexible results, especially when normal distribution assumptions cannot be met (Hair et al. 2019 ). To assess potential common method bias (CMB), Harman’s single-factor test and the full collinearity test were applied together (Kock, 2017 ). The results indicated that CMB was not a significant issue. Data analysis followed the recommended two-stage approach (Hair et al. 2017 ): analysis of the measurement model to assess its reliability and validity of the research model; testing of the structural model and hypotheses. 4.1 Assessment of the measurement model The measurement model included six latent constructs measured reflectively: robot chef ANTH, EI, INN, NAR, RIJI, and WTPP. All indicator loadings exceeded the recommended threshold of 0.70, ranging from 0.735 to 0.943, indicating satisfactory reliability of the indicators (Table 1 ). One item (RIJI2) was removed for low loading (− 0.033), and the measurement model was re-estimated. All the remaining indicators exhibited strong loadings and were statistically significant at the 0.001 level. Harman’s one-factor test for CMB determined that a single factor explained less than 50% of the total variance, indicating that CMB did not systematically affect the research results (Podsakoff et al. 2003 ). The internal consistency reliability of the measurement model constructs was evaluated using Cronbach’s alpha (α) and composite reliability (CR) values. Cronbach’s alpha values ranged from 0.804 to 0.940, while CR values ranged from 0.811 to 0.944, all exceeding the recommended minimum value of 0.70, thus confirming strong internal consistency across all constructs (Hair et al. 2019 ). Convergent validity was assessed by examining average variance extracted (AVE). AVE values ranged from 0.630 to 0.887, exceeding the recommended threshold of 0.50 for all constructs (Table 1 ). That is, each construct’s indicators explained over half of their variance, thus confirming convergent validity (Fornell and Larcker, 1981 ). Distinctive validity was assessed using the heterotrait-monotrait (HTMT) correlation ratio. All HTMT values were below the 0.85 threshold, indicating that each construct was empirically distinct from the others (Table 2 ) and strongly confirming discriminant validity among the latent constructs (Hair et al. 2019 ). Overall, the findings indicate that the measurement model exhibited convergent and discriminant validity with satisfactory reliability, thereby supporting the model’s suitability for subsequent structural model analysis. 4.3 Assessment of the structural model Before conducting PLS-SEM, the measurement model’s reliability and validity were comprehensively assessed. Multicollinearity was tested for using the variance inflation factor (VIF) values, which were well below the threshold value of 3.0 (Table 1 ), indicating that no risk of multicollinearity in the model (Hair et al. 2019 ). Table 1 Measurement model results Construct/associated items FL α CR AVE Robot chef anthropomorphism (ANTH) 0.940 0.944 0.807 ANTH1 0.892 ANTH2 0.918 ANTH3 0.928 ANTH4 0.859 ANTH5 0.894 Experience intention (EI) 0.829 0.836 0.751 EI1 0.752 EI2 0.922 EI3 0.915 Innovativeness (INN) INN1 0.865 0.812 0.842 0.720 INN2 0.848 INN3 0.833 Negative attitudes toward robots (NAR) 0.804 0.811 0.630 NAR1 0.818 NAR2 0.735 NAR3 0.829 NAR4 0.790 Robot-induced job insecurity (RIJI) 0.883 0.925 0.809 RIJI1 0.832 RIJI3 0.927 RIJI4 0.935 Willingness to pay a premium (WTPP) 0.936 0.938 0.887 WTPP1 0.941 WTPP2 0.941 WTPP3 0.943 Note(s): All item loadings are significant at p < 0.001 level; FL: factor loading; α: Cronbach’s alpha, CR: composite reliability (rho_a); AVE: average variance extracted Table 2 Discriminant validity (HTMT criterion) Construct ANTH EI INN NAR RIJI WTPP ANTH EI 0.410 INN 0.188 0.378 NAR 0.382 0.151 0.198 RIJI 0.214 0.284 0.300 0.492 WTPP 0.523 0.573 0.206 0.333 0.180 Notes : INN= innovativeness; ANTH= robot chef anthropomorphism; NAR= negative attitudes toward robots; RIJI= Robot-induced job insecurity; EI= experience intention; WTPP= willingness to pay a premium The structural model’s explanatory power was assessed using the coefficient of determination (R²), which indicated that it explained 19.7%, 24.9%, and 25.7% of the variance in EI, NAR, and WTPP, respectively, at a moderate level of explanatory power (Hair et al. 2019 ). The model’s predictive relevance was assessed using the Stone–Geisser Q² criterion. The Q² values for EI (0.082), NAR (0.178), and WTPP (0.032) were all above zero, confirming the model’s predictive relevance. Table 4 reports the hypothesis test results. Table 4 Structural model results Hypotheses β 95% Bc CI t-value VIF ƒ 2 Decision H1 ANTH→EI 0.331*** [0.237, 0.426] 6.944 1.135 0.120 Supported H2 ANTH→NAR 0.256*** [0.156, 0.355] 5.066 1.039 0.084 Supported H3 EI→WTPP 0.506*** [0.425, 0.585] 12.304 1.000 0.345 Supported H4 INN→ANTH 0.157* [-0.026, 0.284] 1.934 1.000 0.025 Not supported H5 INN→EI 0.260*** [0.137, 0.367] 4.283 1.041 0.081 Supported H6 NAR→EI -0.027* [-0.149, 0.095] 0.440 1.140 0.001 Not supported H7 RIJI→NAR 0.382*** [0.282, 0.481] 7.515 1.039 0.187 Supported Notes : * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001; Bc CI = bias-corrected confidence interval Anthropomorphism of robot chefs had a significant positive effect on experience intention (β = 0.331, p < 0.001), supporting H1. Anthropomorphism had a significant positive effect on negative attitudes toward robots (β = 0.256, p < 0.01), supporting H2. Experience intention had a significant positive effect on WTTP (β = 0.506, p < 0.001), supporting H3. Innovativeness had a significant positive effect on robot chef anthropomorphism (β = 0.157, p < 0.05) and experience intention (β = 0.260, p 0.05), not supporting H6. Finally, robot-induced job insecurity had a significant positive effect on negative attitudes toward robots (β = 0.382, p < 0.001), supporting H7. Overall, the results indicate that the proposed structural model is free from multicollinearity issues and possesses acceptable explanatory and predictive power. The results confirm the central role of robot chef anthropomorphism and experience intention in shaping willingness to pay a premium, while also revealing that negative attitudes toward robots do not directly reduce experience intention. 5 Discussion and conclusions This study contributes to service robot and gastronomy education research by examining how gastronomy students’ perceptions of robot chefs influence their intention and willingness to pay a premium to experience them, within a comprehensive model incorporating anthropomorphism, innovativeness, attitudes toward robots, and perceived job insecurity. The findings reveal that gastronomy students’ perceptions of robot chefs cannot be explained as a unidirectional process of technology acceptance; rather, they are shaped by complex, contradictory psychological mechanisms that simultaneously involve attraction and threat. A key finding is that anthropomorphism of robot chefs directly increases intention to experience them. This is consistent with the CASA approach and anthropomorphism studies suggesting that human-like cues in robots increase individuals’ desire for social closeness and interaction (Belanche et al. 2020 ; Epley et al. 2007 ; Nass and Moon 2000 ). That is, the gastronomy students desire to experience robot chefs was strengthened because they perceive them as interactive experience actors not just technical systems. Thus, in educational contexts, robot chefs are part of experiential learning processes beyond being merely automation tools. However, these perceptions do not indicate a one-dimensional acceptance mechanism because anthropomorphism also significantly increases negative attitudes toward robots. That is, while robot chefs’ human-like features can trigger curiosity and desire for interaction, their visibility as professional actors can strengthen replacement and identity threats. This finding aligns with claims that anthropomorphism can generate conflicting responses, particularly in fields with strong professional identities (Fusté-Forné 2021 ). Indeed, previous studies have shown that human-like features can both increase acceptance and decrease it due to perceived threats (Goudey and Bonnin 2016 ; Lu et al. 2019 ). The findings regarding innovativeness indicate a more selective effect structure. More specifically, innovativeness does not affect anthropomorphism; rather, it directly increases intention to experience robot chefs. That is, gastronomy students open to innovation are more inclined to try these technologies, regardless of whether they perceive robot chefs as human-like. Thus, innovativeness operates through behavioral openness rather than symbolic perceptions, consistent with previous studies showing that high innovativeness individuals view technologies as opportunities for learning and development rather than threats (Lu et al. 2019 ; Parvez et al. 2022 ). Another noteworthy finding is that negative attitudes toward robots have no significant effect on experience intention, thereby not confirming previous findings of a negative attitude–avoidance relationship (Nomura et al. 2008 ). That is, culinary arts students who perceive robot chefs as a professional threat retain their experiential curiosity. This supports claims that individuals can develop ambivalent attitudes toward new technologies, containing both anxiety and curiosity (Venkatesh et al. 2012 ). In contrast, perceived job insecurity significantly increases negative attitudes toward robots, which clearly indicates that these culinary arts students have internalized automation-related anxieties although they have not yet fully entered the labor market. That is, job insecurity perceptions can be shaped in the early stages of professional socialization. This is consistent with Seyitoğlu et al. (2022), who reported that robotization may reduce students’ motivation for tourism sector careers, and studies showing that automation-related employment anxieties are not limited to current employees (Brougham and Haar 2018 ; Ivanov and Webster 2023 ). Therefore, given increasing automation, it is important for gastronomy programs to develop students’ digital fluency, adaptability, and creative resilience (Shin et al. 2025 ). Finally, intention to experience robot chefs significantly increases willingness to pay a premium, indicating that this experience has both functional and symbolic and experiential value for these students. This supports the experience economic approach, which argues that experiences are converted into economic value (Pine and Gilmore 1999 ) and indicates that robot chefs can become a premium experience element in both culinary education and experience-focused restaurant applications (Belanche et al. 2020 ; Kazakova et al. 2025 ). 5.1 Theoretical implications This study contributes to the literature on gastronomy and human–robot interaction by presenting a process-based theoretical framework to explain how culinary arts students’ perceptions of robot chefs translate into experiential and economic outcomes. The findings show that evaluations of robot chefs is not a linear technology acceptance process; instead, evaluations are shaped by multi-layered, simultaneous psychological mechanisms involving both attraction and threat. Hence, this study goes beyond existing approaches for interpreting service robots’ roles in gastronomy. The strong effect of experiential intention on greater willingness to pay a premium reveal that robot chefs are perceived not only as functional technologies but also as actors that produce experience-based value. This finding theoretically supports the experience economic approach in gastronomy education, suggesting that economic value is generated through experiential intent rather than direct technology perception (Pine and Gilmore 1999 ). Robot chef anthropomorphism’s direct effect on intention to experience demonstrates that the CASA approach can be extended to education and vocational preparation (Nass and Moon 2000 ). Conversely, the lack of a significant effect of negative attitudes toward robots on experience intention highlights the contextual limitations of the negative attitude–avoidance relationship often assumed in the literature (Nomura et al. 2008 ). That is, culinary arts students may view robot chefs as an inevitable component of their professional future rather than an absolute threat. Although perceived job insecurity increases negative attitudes, the fact that these attitudes do not translate into intention to experience suggests that automation-related anxieties may not translate into behavioral resistance in educational contexts. This aligns with approaches suggesting that early-career concerns about technology may be balanced by learning and adaptation motivations (Frey and Osborne 2017 ). Finally, by adopting a holistic process perspective on how individuals attribute value to robot chefs, this study addressed anthropomorphism, innovativeness, negative attitudes, job insecurity, intention to experience, and willingness to pay a premium in a sequential model. Positioning experience intention to experience as a central mechanism in this process indicates that human–robot interactions need to be reevaluated in relation to value creation in gastronomy and hospitality literature (Ivanov and Webster 2023 ). 5.2 Practical implications This study offers actionable insights for educators, technology developers, and industry practitioners aiming to integrate robotic technologies into kitchen contexts. Regarding education, instead of focusing narrowly on imparting technical skills, integration of robot chefs into culinary education should be structured within a holistic pedagogical framework that considers students’ professional concerns, perceived job insecurity, and career uncertainty. Including topics like human-robot collaboration, professional transformation, and future-oriented career planning in curricula can help reduce negative attitudes toward robot technologies. From a sectoral perspective, the strong positive impact of experience intention on willingness to pay a premium indicates that robot chefs can be positioned as a strategic value element in innovative and experience-oriented gastronomy concepts. However, effectively leveraging this potential requires presenting robot chefs as technologies that complement human creativity and craftsmanship rather than replace human labor. This can both enrich customer experience while making future gastronomy professionals more willing to work alongside technology. For robot technology developers, the findings show that anthropomorphic features can increase user interest but also trigger professional concerns. Therefore, the level and presentation of anthropomorphic elements in robot chef designs must be carefully balanced, while robots should be clearly framed as supportive and collaborative. Similarly, robotic systems for culinary schools should not be introduced solely as technical tools but integrated into experiential learning environments. Project-based assignments, kitchen simulations, and applied course modules can enable students to engage practically with robot technologies. 5.3 Limitations and future research This study has certain limitations. First, the sample included gastronomy students so the findings cannot be directly generalized to industry workers. However, this was a deliberate theoretical decision to provide initial insights into the future workforce’s perceptions. Future studies can examine how these perceptions evolve by comparing students with early-career professionals and using longitudinal designs. Experimental designs based on cultural context, ethical perceptions, and real robot chef experiences would further deepen the knowledge base in this field. Declarations Funding Not Funded Data availability Data will be made available on request. Conflict of interests The authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article. Ethical statement The aforementioned project and informed consent have been reviewed and approved by the Ethics Committee (….). Author Contribution N.B. conceived and designed the study. N.B. conducted the literature review, collected the data, and performed the data analysis. 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Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88 (5), 879–903. https://doi.org/10.1037/00219010.88.5.879. Ringle, C. M., da Silva, D., Bido, D. 2014. Structural equation modeling with the SmartPLS. Braz. J. Mark.,13(2), 56–73. https://doi.org/10.5585/remark.v13i2.2717 Seyitoğlu, F., Fusté-Forné, F., Yiğit, S., Engin, S. 2025. Robot chefs: The impacts, compatibility and suitability. Brit. Food J. 127(1), 307–323. https://doi.org/10.1108/BFJ-07-2024-0705 Seyitoğlu, F., Ivanov, S., Atsız, O., Çifçi, ˙ I., 2021. Robots as restaurant employees-a double-barrelled detective story. Technol. Soc. 67, 101779. https://doi.org/10.1016/j.techsoc.2021.101779 Shin, Y., Min, B., Hwang, J., Ham, S. 2025. Cooking with robots? Exploring culinary arts students’ acceptance through perceived value and the technology acceptance model. J. Cul. Sci. Technol. 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Illustrating the perception of students towards autonomous service robots in the tourism industry: An exploratory study. Tour. Hosp. Man. 27(2), 385–406. https://doi.org/10.20867/thm.27.2.7 Wang, J., Ren, L., Chen, J.,Su, X. 2026. Paying premiums for humanoid service robots in hospitality: What are the key determinants? Int. J. Contemp. Hosp. Manag. 134, 104525. https://doi.org/10.1016/j.ijhm.2025.104525 Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., Martins, A., 2018. Brave new world: service robots in the frontline. J. Serv. Manag. 29 (5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119. Yıldız, E., Hökelekli, N. A. 2025. Why do customers intend to dine at robot-chef restaurants? The roles of entertainment, consistency, authenticity, and food quality. Int. J. Gastro. Food Sci. Article 101321. https://doi.org/10.1016/j.ijgfs.2025.101321 YÖK. (2025). Council of higher education. number of H&T programs offered by universities in Türkiye. https://yokatlas.yok.gov.tr/lisans-bolum.php?b=10208 Zhu, D.H., Chang, Y.P., 2020. Robot with humanoid hands cooks food better? Effect of robotic chef anthropomorphism on food quality prediction. Int. J. Contemp. Hosp. Manag. 32 (3), 1367–1383. https://doi.org/10.1108/IJCHM-10-2019-0904 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8678823","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579787040,"identity":"4d1f15cb-f7c7-4745-98d1-4469addca875","order_by":0,"name":"Nurgül BOZ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYNCCAoYEBgbmAwwPoHwJwloMQFrYEkAkTIsBMVp4DIjTYi6R+/g1UHEeP/+Zjx8Sau4k9jcwH7zNw/AnH5cWyxnpZtZALcWSDWc3SyQce5Y44wBbsjUPg4FlAy4n3UhjMwZqSdxwsHeDRALb4cSGAzxm0kAtOF2G0HKY5/GPhH+HE+cf4P9GSAvzY7CWYzxsEolthxM3HOBhw6/lzDM2xjkGEsWSPWxmFol9h403HmYztpxjYIxby/E05g9vKmyAIXb48Y0P3w7Lzjve/PDGmwo5fBHDJoEc3Y4NzGCj8GgAJpQPyDx7vGpHwSgYBaNgRAIAAXtS0UV7RcYAAAAASUVORK5CYII=","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":true,"prefix":"","firstName":"Nurgül","middleName":"","lastName":"BOZ","suffix":""}],"badges":[],"createdAt":"2026-01-23 11:39:19","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8678823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8678823/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101226220,"identity":"3273c0c7-cfeb-4a17-b9d9-cc219801637a","added_by":"auto","created_at":"2026-01-27 12:48:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37606,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8678823/v1/d2727feb72a7f41df278467c.png"},{"id":101226221,"identity":"0d911726-66f9-4875-9abf-35c0914ff1e6","added_by":"auto","created_at":"2026-01-27 12:48:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69557,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model results\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e INN= innovativeness; \u0026nbsp;\u0026nbsp;ANTH= robot chef anthropomorphism; NAR= negative attitudes toward robots; \u0026nbsp;\u0026nbsp;RIJI= Robot-induced job insecurity; EI= experience intention; WTPP= willingness \u0026nbsp;\u0026nbsp;to pay a premium n.s.= non-significant. *p\u0026lt;0.05, *a*p\u0026lt;0.01\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8678823/v1/2e8df26402932575fcf19ca1.png"},{"id":101943395,"identity":"c5a5c648-4f5d-42a0-9079-b8fbfaa027cf","added_by":"auto","created_at":"2026-02-05 09:41:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1027682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8678823/v1/e8dde9a1-81cb-44be-af95-d0a4db0b7fa7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robot Chefs as Both a Threat and an Experience: The Roles of Anthropomorphism, Job Insecurity, and Culinary Students’ Experience Intentions","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe hospitality industry has long been conceptualized as a high-contact, labor-intensive service sector (Seyitoğlu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the increasing prevalence of artificial intelligence (AI)-powered service robots has reopened discussions about how to organize service production and professional role boundaries. The global market is expected to grow from \u003cspan\u003e$\u003c/span\u003e3.87\u0026nbsp;billion in 2024 to over \u003cspan\u003e$\u003c/span\u003e11\u0026nbsp;billion by 2037 (Nester \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Service robots offer significant advantages for the hospitality industry regarding service reliability, hygiene, operational consistency, and managerial innovation (Kumar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This technological transformation is increasingly reshaping food production, particularly where production processes are central, such as restaurants and professional kitchens (Hu, \u0026amp; \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Pizam et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A striking example is robot chefs, which not only automate service but also directly participate in food preparation and cooking. Modern cooking robots have evolved from simple robotic systems to AI-powered smart kitchen solutions (Shin et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Unlike traditional food industry automation, robot chefs can apply multiple recipes, make decisions based on sensory input, maintain hygiene standards, and accelerate operations (Seyitoğlu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sochacki et al. 2024). However, these developments represent not only an increase in technical capacity but a turning point that may redefine the human dimension of the gastronomy profession, the understanding of mastery, and the boundaries of the chef-labor relationship.\u003c/p\u003e \u003cp\u003eResearch has primarily investigated robot-based restaurant applications from the consumer\u0026rsquo;s perspective, regarding behavioral outcomes like attitudes toward technology, intention to experience, and perceived value (Kazakova et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tuomi et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fewer studies have investigated the perceptions and psychological responses of the sector\u0026rsquo;s future workforce, including gastronomy students, who are expected to be directly affected by these technologies (Cifci et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Seyitoğlu et al. 2022; Wakelin-Theron, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For students, the issue goes beyond simply accepting or rejecting workplace robots to interaction with them, reconfiguration of professional roles, and post-graduation employment prospects.\u003c/p\u003e \u003cp\u003eThus, gastronomy students are a critical sample group that can simultaneously experience both curiosity and perceived opportunities regarding robot chefs, as well as concerns about future job insecurity. Their assessments can generate evidence-based conclusions regarding how gastronomy education should be adapted to work environments transformed by automation. This study adopts the Computers Are Social Actors (CASA) theoretical approach to explain culinary art students\u0026rsquo; perceptions of robot chefs. The CASA approach argues that individuals tend to evaluate technology as a \u0026ldquo;social actor\u0026rdquo; when interacting with it, especially when human-like cues are present, which can trigger social-emotional responses like trust, closeness, or discomfort (Nass and Moon, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). More specifically, AI-powered humanoid service robots can shape users\u0026rsquo; emotional evaluations through language use, interactivity, and the ability to adopt social roles (Gambino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These anthropomorphic features can significantly influence emotional responses and interaction intentions (Song et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Song and Kim, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, an important starting point for explaining students\u0026rsquo; intentions to experience robot chefs is anthropomorphism. Combined with job displacement concerns, this concept suggests a dual dynamic that can also promote negative attitudes toward robots. A key factor determining the direction of these attitudes is innovativeness, meaning openness and willingness to try new technologies, thereby influencing individuals\u0026rsquo; perceptions of robots as opportunities or threats. Individuals high in innovativeness perceive robot technologies more as opportunities and are therefore less resistant (Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Parvez et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For gastronomy students, high innovativeness includes both openness and the capacity to accept the transformation of their profession while identifying new roles within it. Students low in innovativeness may perceive robot chefs as threatening their professional identity and employment.\u003c/p\u003e \u003cp\u003eThe proliferation of robot technologies in the hospitality sector may cause significant uncertainty for gastronomy students regarding future employment due to the risk of human labor displacement (Brougham and Haar \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cifci et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These perceptions are an important cognitive and emotional basis for developing negative attitudes toward robots. Studies of tourism and hospitality students\u0026rsquo; post-graduation employment intentions have examined numerous determinants, including employability, entrepreneurial motivation, salary expectations, self-efficacy, and perceived education quality (Kahraman and Demirdelen Alrawadieh 2021; Luong and Lee \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, they have not systematically examined how the economic transformation shaped by robotic technologies and job insecurity concerns affects these students\u0026rsquo; professional commitment and post-graduation intentions. Finally, intention to experience robot chefs may not only indicate psychological curiosity; rather, it may indicate a valuation signal promoting economic behaviors willingness to pay a premium behavior. Consumer behavior research has revealed a meaningful relationship between perceived value and payment behavior in technology-mediated service experiences, suggesting that this relationship is worth examining regarding robotic service encounters (Chuah et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hong et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Accordingly, the present study: positions gastronomy students as actors who understand their professional future within this transformation rather than remaining passive observers of automation; tests the dual dynamic of robot chef anthropomorphism, associated with both intention to experience and negative attitudes; and examines how these psychological mechanisms relate to economic outcomes like willingness to pay a premium.\u003c/p\u003e"},{"header":"2 Literature review and hypotheses development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Service robots, gastronomy, and the future workforce\u003c/h2\u003e \u003cp\u003eBy integrating robotic technologies with culinary arts converge, robotic gastronomy is significantly transforming processes like food preparation and customer service (Ma et al. 2023). In particular, robots could account for approximately 25% of tourism sector employees by 2030 (Yıldız and H\u0026ouml;kelekli \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research has primarily investigated the impact of service robots in the hospitality and gastronomy sectors in terms of customer acceptance and employees\u0026rsquo; attitudes. Customer attitudes and experience intentions toward robot-based service experiences are shaped by elements like perceived benefit, entertainment, trust, and anthropomorphism (Belanche et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kazakova et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, employees associate robotization with negative outcomes like perceived job insecurity, skill devaluation, and occupational stress (Brougham and Haar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Frey and Osborne, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The proliferation of robot chefs that can partially or fully automate preparation and cooking processes, particularly in restaurant kitchens, has made these issues more visible (Ivanov et al. 2020; Seyitoğlu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFewer studies have investigated the views of gastronomy students regarding robot chefs, who are neither customers nor employees. Nevertheless, as the sector\u0026rsquo;s future workforce, it is critical to examine this group\u0026rsquo;s perceptions regarding the potential effects of automation on their professional future, given that perceptions shaped by education can affect longer term professional identity construction, career expectations, and adaptability to technology (Brougham and Haar \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Indeed, robotization may reduce students\u0026rsquo; motivation to seek tourism and hospitality careers by reducing their long-term commitment to the sector (Seyitoğlu et al. 2022) and increasing perceptions that robot technologies threaten human employment opportunities (Wakelin-Theron \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such findings indicate that gastronomy students\u0026rsquo; evaluations of robot chefs do not simply reflect general attitudes toward technology; rather, they relate to students\u0026rsquo; professional and employment expectations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Robot chef anthropomorphism and intention to experience\u003c/h2\u003e \u003cp\u003eAnthropomorphism refers to the degree that individuals perceive robots as possessing human-like characteristics like intelligence, conscious movement, warmth, and interest (Martin et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Human-robot interaction researchers consider anthropomorphism to be a fundamental psychological mechanism shaping attitudes toward robots (Epley et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The CASA approach proposes that technological entities exhibiting human-like cues encourage positive emotional responses by triggering users\u0026rsquo; unconscious interpersonal communication norms (Fox and Gambino \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eService robots exhibiting anthropomorphic features are perceived as more social, interactive, and trustworthy, which then strengthens intention to interact with and experience robots (Wirtz et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, perceiving restaurant and service robots as having human-like characteristics significantly increases intention to experience them personally (Belanche et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, anthropomorphism is both a cognitive evaluation and important determinant of behavioral intentions. For gastronomy education students, anthropomorphic perception of robot chefs involves encountering a new technological application and an opportunity to experience future kitchen practices opportunity to experience the future kitchen practices of the future. This suggests the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1.\u003c/b\u003e Robot chef anthropomorphism is positively associated with gastronomy students\u0026rsquo; intention to experience robot chefs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Robot chef anthropomorphism and negative attitudes toward robots\u003c/h2\u003e \u003cp\u003eResearch shows that perceiving robot chefs as exhibiting human-like characteristics influences whether individuals evaluate them just as tools or as professional actors that may compete with human labor (Belanche et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Individuals who consider mastery, creativity, and human labor as fundamental components of the professional gastronomy identity while perceiving robot chefs anthropomorphically may experience threat and replacement anxiety. That is, they evaluate robot chefs both for their functional contributions and symbolic meanings regarding the profession\u0026rsquo;s future. These uncertainties regarding anthropomorphic robot chefs and the future of human labor may be heightened for gastronomy students, who are still forming their professional identity. In particular, individuals perceiving that robots are approaching human capabilities may have stronger fears of job replacement and loss of control (Cifci et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In short, while anthropomorphism may sometimes support technology acceptance, it can also promote negative attitudes toward robots in fields like gastronomy with strong professional identities. This suggests the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2.\u003c/b\u003e Robot chef anthropomorphism is positively associated with gastronomy students\u0026rsquo; negative attitudes toward robots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Innovativeness, anthropomorphism, and intention to experience\u003c/h2\u003e \u003cp\u003eInnovativeness is a fundamental personal tendency to adopt new technologies (Agarwal and Prasad \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Hence, it significantly shapes perceptions of robot technologies in that individuals high in innovativeness perceive them as providing learning and development opportunities rather than causing uncertainty or threat. This in turn makes them less resistant (Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Parvez et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For gastronomy students, innovativeness goes beyond openness to technology to include the tendency to embrace professional transformation and develop new roles accordingly. Regarding complex, uncertain technologies like robot chefs, innovative individuals try to make sense of and evaluate them within a social framework, which may make them more likely to perceive robot chefs as having human-like characteristics (Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Seyitoğlu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, individuals low in innovativeness tend to evaluate robot chefs as more threatening to professional identity and employment (Cifci et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, innovativeness may be an individual difference factor influencing how intensely gastronomy students attribute anthropomorphic characteristics to robot chefs. This suggests the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3.\u003c/b\u003e Gastronomy students\u0026rsquo; innovativeness is positively associated with their perceptions of robot chef anthropomorphism.\u003c/p\u003e \u003cp\u003eIn addition, innovativeness increases intention to interact with new technologies. For example, innovative individuals are more willing to interact with robot chefs because they consider this to be valuable for professional learning and career development (Belanche et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, innovativeness may also strengthen gastronomy students\u0026rsquo; intentions to directly experience robot chefs, which suggests the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4\u003c/b\u003e. Gastronomy students\u0026rsquo; innovativeness is positively associated with their intention to experience robot chefs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Robot-induced job insecurity, negative attitudes, and behavioral outcomes\u003c/h2\u003e \u003cp\u003eThe proliferation of robot technologies in the service sector increases uncertainty regarding future employment opportunities. Particularly in areas like kitchens, where human labor, skill, and creativity are central, robot chefs can increase concerns regarding job replacement (Brougham and Haar \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Gastronomy students may show higher perceived job insecurity due to fears regarding fewer post-graduation employment opportunities and the profession\u0026rsquo;s non-sustainability. Indeed, individuals perceiving robot technologies as threatening employment have stronger negative attitudes toward them (Frey and Osborne \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Parvez et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In gastronomy and tourism specifically, perceived job insecurity is a fundamental driver of negative attitudes towards robots (Brougham and Haar \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cifci et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Koo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wakelin-Theron, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5.\u003c/b\u003e Perceived job insecurity is negatively associated with gastronomy students\u0026rsquo; intention to experience robot chefs.\u003c/p\u003e \u003cp\u003eBehavioral intention research demonstrates that attitudes toward a technology determine intention to interact with and experience that technology (Venkatesh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) by framing robot-based services as risky, alien, or avoidable experiences. This weakens intention to interact with them (Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wakelin-Theron, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regarding gastronomy students, this suggests the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6\u003c/b\u003e. Perceived job insecurity is positively associated with gastronomy students\u0026rsquo; negative attitudes toward robots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Intention to experience and willingness to pay a premium\u003c/h2\u003e \u003cp\u003eIntention to experience is an important indicator of the perceived value attributed to services or experiences. Regarding robot restaurants, individuals who strongly desire to experience something evaluate robot-based services as more innovative, differentiated, and exclusive, which is also associated with higher willingness to pay a premium (Belanche et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kazakova et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This suggests that gastronomy students who intend to experience robot chefs are more likely to interpret experiences as both consumption activities and opportunities for professional learning and preparation. The experience economic approach posits that individuals are willing to pay higher prices, not only for services offering functional benefits, but also for experiences offering symbolic and experiential value (Pine and Gilmore \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This suggests that gastronomy students who intend to experience robot chefs will also associate this experience with higher value, and hence be more willing to pay, as in the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH7.\u003c/b\u003e Intention to experience robot chefs increases gastronomy students\u0026rsquo; willingness to pay a premium.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Measurement\u003c/h2\u003e \u003cp\u003eThe measurement tools were adapted from scales whose validity and reliability have been previously demonstrated in the gastronomy, tourism, and service robot literature. Innovativeness was measured using three items from Agarwal and Prasad (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Robot-induced job insecurity was measured using four items from Vander Elst et al. (2014). Negative attitudes toward robots, specifically anxiety and interaction resistance, were measured using four items from Nomura et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Robot chef anthropomorphism was measured using five items from Zhu and Chang (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Intention to experience robot chefs, specifically robot-based gastronomic experiences, was measured using three items from Dodds et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Finally, willingness to pay a premium for robot chef restaurant experiences was measured using three items from Kazakova et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo ensure the survey items\u0026rsquo; linguistic accuracy and conceptual equivalence back-translation was applied (Huang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). First, two bilingual tourism academics translated the initial English-language survey into Turkish. Then, to minimize potential translation errors and shifts in meaning, by two gastronomy experts proficient in both Turkish and English and a tourism academic retranslated the Turkish questionnaire into English. This iterative process continued until both language versions were conceptually and linguistically consistent. The final Turkish-language questionnaire, which was administered in T\u0026uuml;rkiye, had two sections: items about demographic characteristics (gender, age, and previous experience interacting with robots); scales for the research variables, with responses measured using a seven-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sample and data collection\u003c/h2\u003e \u003cp\u003eThe study population was undergraduate gastronomy students, selected because this group represents future culinary arts professionals whose perceptions and expectations regarding robot chef technologies are being shaped early in their professional careers, thereby enabling examination of the preliminary effects of technology-driven professional transformations. As of 2025, 95 universities in T\u0026uuml;rkiye offer gastronomy programs (61 state run; 34 foundation run, Y\u0026Ouml;K, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Eight universities were selected using purposive and convenience sampling to ensure institutional and geographical diversity and feasible data collection. The study was conducted with the approval of the Ethics Committee of ... University and in accordance with ethical principles. Data collection, which was conducted between December 10\u0026ndash;25, 2025, via an online survey using Google Forms, obtained 391 valid responses. Participation was voluntary, no personal data were requested, and it was clearly stated that participants could withdraw at any time. Sample size adequacy was calculated using G*Power v3.1.9.7 software (Faul et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The result indicated that a minimum of 77 participants was sufficient to test a medium effect size (f\u0026sup2;=0.15) at 80% power and a 5% significance level. However, to obtain more reliable results in partial least squares structural equation modeling (PLS-SEM) analyses, it is recommended to triple this number (Ringle et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Accordingly, the minimum sample size was set at 231 participants; hence, the sample of 391 participants more than met these methodological requirements for reliably testing the research model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sample profile\u003c/h2\u003e \u003cp\u003eRegarding the participants\u0026rsquo; demographic characteristics, 64.2% were female and 35.8% male. The participants were mostly young students in the early or middle stages of their higher education programs, with 44.2% aged 18\u0026ndash;20, 42.5% aged 21\u0026ndash;23, and 13.3% aged 24\u0026ndash;26. Regarding educational level, about two thirds (66.8%) were enrolled in undergraduate programs and one third (33.2%) in associate degree programs.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003eData analysis was performed using PLS-PLS-SEM because it provides robust and flexible results, especially when normal distribution assumptions cannot be met (Hair et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To assess potential common method bias (CMB), Harman\u0026rsquo;s single-factor test and the full collinearity test were applied together (Kock, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results indicated that CMB was not a significant issue. Data analysis followed the recommended two-stage approach (Hair et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e): analysis of the measurement model to assess its reliability and validity of the research model; testing of the structural model and hypotheses.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Assessment of the measurement model\u003c/h2\u003e \u003cp\u003eThe measurement model included six latent constructs measured reflectively: robot chef ANTH, EI, INN, NAR, RIJI, and WTPP. All indicator loadings exceeded the recommended threshold of 0.70, ranging from 0.735 to 0.943, indicating satisfactory reliability of the indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). One item (RIJI2) was removed for low loading (\u0026minus;\u0026thinsp;0.033), and the measurement model was re-estimated. All the remaining indicators exhibited strong loadings and were statistically significant at the 0.001 level. Harman\u0026rsquo;s one-factor test for CMB determined that a single factor explained less than 50% of the total variance, indicating that CMB did not systematically affect the research results (Podsakoff et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The internal consistency reliability of the measurement model constructs was evaluated using Cronbach\u0026rsquo;s alpha (α) and composite reliability (CR) values. Cronbach\u0026rsquo;s alpha values ranged from 0.804 to 0.940, while CR values ranged from 0.811 to 0.944, all exceeding the recommended minimum value of 0.70, thus confirming strong internal consistency across all constructs (Hair et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Convergent validity was assessed by examining average variance extracted (AVE). AVE values ranged from 0.630 to 0.887, exceeding the recommended threshold of 0.50 for all constructs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). That is, each construct\u0026rsquo;s indicators explained over half of their variance, thus confirming convergent validity (Fornell and Larcker, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Distinctive validity was assessed using the heterotrait-monotrait (HTMT) correlation ratio. All HTMT values were below the 0.85 threshold, indicating that each construct was empirically distinct from the others (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and strongly confirming discriminant validity among the latent constructs (Hair et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overall, the findings indicate that the measurement model exhibited convergent and discriminant validity with satisfactory reliability, thereby supporting the model\u0026rsquo;s suitability for subsequent structural model analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Assessment of the structural model\u003c/h2\u003e \u003cp\u003eBefore conducting PLS-SEM, the measurement model\u0026rsquo;s reliability and validity were comprehensively assessed. Multicollinearity was tested for using the variance inflation factor (VIF) values, which were well below the threshold value of 3.0 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating that no risk of multicollinearity in the model (Hair et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement model results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct/associated items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobot chef anthropomorphism (ANTH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience intention (EI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInnovativeness (INN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative attitudes toward robots (NAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobot-induced job insecurity (RIJI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIJI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIJI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIJI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWillingness to pay a premium (WTPP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWTPP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWTPP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWTPP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote(s): All item loadings are significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level; FL: factor loading; α: Cronbach\u0026rsquo;s alpha, CR: composite reliability (rho_a); AVE: average variance extracted\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity (HTMT criterion)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANTH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRIJI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWTPP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRIJI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWTPP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNotes\u003c/b\u003e: INN= innovativeness; ANTH= robot chef anthropomorphism; NAR= negative attitudes toward robots; RIJI= Robot-induced job insecurity; EI= experience intention; WTPP= willingness to pay a premium\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe structural model\u0026rsquo;s explanatory power was assessed using the coefficient of determination (R\u0026sup2;), which indicated that it explained 19.7%, 24.9%, and 25.7% of the variance in EI, NAR, and WTPP, respectively, at a moderate level of explanatory power (Hair et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The model\u0026rsquo;s predictive relevance was assessed using the Stone\u0026ndash;Geisser Q\u0026sup2; criterion. The Q\u0026sup2; values for EI (0.082), NAR (0.178), and WTPP (0.032) were all above zero, confirming the model\u0026rsquo;s predictive relevance.\u003c/p\u003e\u003cp\u003eTable 4 reports the hypothesis test results.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural model results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% Bc CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eƒ\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANTH\u0026rarr;EI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.331***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.237, 0.426]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANTH\u0026rarr;NAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.256***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.156, 0.355]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEI\u0026rarr;WTPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.506***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.425, 0.585]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINN\u0026rarr;ANTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.157*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[-0.026, 0.284]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINN\u0026rarr;EI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.260***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.137, 0.367]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAR\u0026rarr;EI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[-0.149, 0.095]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIJI\u0026rarr;NAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.382***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.282, 0.481]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: \u003csup\u003e*\u003c/sup\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.001; Bc CI\u0026thinsp;=\u0026thinsp;bias-corrected confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnthropomorphism of robot chefs had a significant positive effect on experience intention (β\u0026thinsp;=\u0026thinsp;0.331, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H1. Anthropomorphism had a significant positive effect on negative attitudes toward robots (β\u0026thinsp;=\u0026thinsp;0.256, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting H2. Experience intention had a significant positive effect on WTTP (β\u0026thinsp;=\u0026thinsp;0.506, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H3. Innovativeness had a significant positive effect on robot chef anthropomorphism (β\u0026thinsp;=\u0026thinsp;0.157, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and experience intention (β\u0026thinsp;=\u0026thinsp;0.260, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting H4 and H5, respectively. In contrast, negative attitudes toward robots had no significant effect on experience intention (β=\u0026minus;0.027, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), not supporting H6. Finally, robot-induced job insecurity had a significant positive effect on negative attitudes toward robots (β\u0026thinsp;=\u0026thinsp;0.382, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H7. Overall, the results indicate that the proposed structural model is free from multicollinearity issues and possesses acceptable explanatory and predictive power. The results confirm the central role of robot chef anthropomorphism and experience intention in shaping willingness to pay a premium, while also revealing that negative attitudes toward robots do not directly reduce experience intention.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion and conclusions","content":"\u003cp\u003eThis study contributes to service robot and gastronomy education research by examining how gastronomy students\u0026rsquo; perceptions of robot chefs influence their intention and willingness to pay a premium to experience them, within a comprehensive model incorporating anthropomorphism, innovativeness, attitudes toward robots, and perceived job insecurity. The findings reveal that gastronomy students\u0026rsquo; perceptions of robot chefs cannot be explained as a unidirectional process of technology acceptance; rather, they are shaped by complex, contradictory psychological mechanisms that simultaneously involve attraction and threat. A key finding is that anthropomorphism of robot chefs directly increases intention to experience them. This is consistent with the CASA approach and anthropomorphism studies suggesting that human-like cues in robots increase individuals\u0026rsquo; desire for social closeness and interaction (Belanche et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Epley et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Nass and Moon \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). That is, the gastronomy students desire to experience robot chefs was strengthened because they perceive them as interactive experience actors not just technical systems. Thus, in educational contexts, robot chefs are part of experiential learning processes beyond being merely automation tools.\u003c/p\u003e \u003cp\u003eHowever, these perceptions do not indicate a one-dimensional acceptance mechanism because anthropomorphism also significantly increases negative attitudes toward robots. That is, while robot chefs\u0026rsquo; human-like features can trigger curiosity and desire for interaction, their visibility as professional actors can strengthen replacement and identity threats. This finding aligns with claims that anthropomorphism can generate conflicting responses, particularly in fields with strong professional identities (Fust\u0026eacute;-Forn\u0026eacute; \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, previous studies have shown that human-like features can both increase acceptance and decrease it due to perceived threats (Goudey and Bonnin \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The findings regarding innovativeness indicate a more selective effect structure. More specifically, innovativeness does not affect anthropomorphism; rather, it directly increases intention to experience robot chefs. That is, gastronomy students open to innovation are more inclined to try these technologies, regardless of whether they perceive robot chefs as human-like. Thus, innovativeness operates through behavioral openness rather than symbolic perceptions, consistent with previous studies showing that high innovativeness individuals view technologies as opportunities for learning and development rather than threats (Lu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Parvez et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another noteworthy finding is that negative attitudes toward robots have no significant effect on experience intention, thereby not confirming previous findings of a negative attitude\u0026ndash;avoidance relationship (Nomura et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). That is, culinary arts students who perceive robot chefs as a professional threat retain their experiential curiosity. This supports claims that individuals can develop ambivalent attitudes toward new technologies, containing both anxiety and curiosity (Venkatesh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, perceived job insecurity significantly increases negative attitudes toward robots, which clearly indicates that these culinary arts students have internalized automation-related anxieties although they have not yet fully entered the labor market. That is, job insecurity perceptions can be shaped in the early stages of professional socialization. This is consistent with Seyitoğlu et al. (2022), who reported that robotization may reduce students\u0026rsquo; motivation for tourism sector careers, and studies showing that automation-related employment anxieties are not limited to current employees (Brougham and Haar \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ivanov and Webster \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, given increasing automation, it is important for gastronomy programs to develop students\u0026rsquo; digital fluency, adaptability, and creative resilience (Shin et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, intention to experience robot chefs significantly increases willingness to pay a premium, indicating that this experience has both functional and symbolic and experiential value for these students. This supports the experience economic approach, which argues that experiences are converted into economic value (Pine and Gilmore \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and indicates that robot chefs can become a premium experience element in both culinary education and experience-focused restaurant applications (Belanche et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kazakova et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Theoretical implications\u003c/h2\u003e \u003cp\u003eThis study contributes to the literature on gastronomy and human\u0026ndash;robot interaction by presenting a process-based theoretical framework to explain how culinary arts students\u0026rsquo; perceptions of robot chefs translate into experiential and economic outcomes. The findings show that evaluations of robot chefs is not a linear technology acceptance process; instead, evaluations are shaped by multi-layered, simultaneous psychological mechanisms involving both attraction and threat. Hence, this study goes beyond existing approaches for interpreting service robots\u0026rsquo; roles in gastronomy. The strong effect of experiential intention on greater willingness to pay a premium reveal that robot chefs are perceived not only as functional technologies but also as actors that produce experience-based value. This finding theoretically supports the experience economic approach in gastronomy education, suggesting that economic value is generated through experiential intent rather than direct technology perception (Pine and Gilmore \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRobot chef anthropomorphism\u0026rsquo;s direct effect on intention to experience demonstrates that the CASA approach can be extended to education and vocational preparation (Nass and Moon \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Conversely, the lack of a significant effect of negative attitudes toward robots on experience intention highlights the contextual limitations of the negative attitude\u0026ndash;avoidance relationship often assumed in the literature (Nomura et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). That is, culinary arts students may view robot chefs as an inevitable component of their professional future rather than an absolute threat. Although perceived job insecurity increases negative attitudes, the fact that these attitudes do not translate into intention to experience suggests that automation-related anxieties may not translate into behavioral resistance in educational contexts. This aligns with approaches suggesting that early-career concerns about technology may be balanced by learning and adaptation motivations (Frey and Osborne \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Finally, by adopting a holistic process perspective on how individuals attribute value to robot chefs, this study addressed anthropomorphism, innovativeness, negative attitudes, job insecurity, intention to experience, and willingness to pay a premium in a sequential model. Positioning experience intention to experience as a central mechanism in this process indicates that human\u0026ndash;robot interactions need to be reevaluated in relation to value creation in gastronomy and hospitality literature (Ivanov and Webster \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Practical implications\u003c/h2\u003e \u003cp\u003eThis study offers actionable insights for educators, technology developers, and industry practitioners aiming to integrate robotic technologies into kitchen contexts. Regarding education, instead of focusing narrowly on imparting technical skills, integration of robot chefs into culinary education should be structured within a holistic pedagogical framework that considers students\u0026rsquo; professional concerns, perceived job insecurity, and career uncertainty. Including topics like human-robot collaboration, professional transformation, and future-oriented career planning in curricula can help reduce negative attitudes toward robot technologies. From a sectoral perspective, the strong positive impact of experience intention on willingness to pay a premium indicates that robot chefs can be positioned as a strategic value element in innovative and experience-oriented gastronomy concepts. However, effectively leveraging this potential requires presenting robot chefs as technologies that complement human creativity and craftsmanship rather than replace human labor. This can both enrich customer experience while making future gastronomy professionals more willing to work alongside technology. For robot technology developers, the findings show that anthropomorphic features can increase user interest but also trigger professional concerns. Therefore, the level and presentation of anthropomorphic elements in robot chef designs must be carefully balanced, while robots should be clearly framed as supportive and collaborative. Similarly, robotic systems for culinary schools should not be introduced solely as technical tools but integrated into experiential learning environments. Project-based assignments, kitchen simulations, and applied course modules can enable students to engage practically with robot technologies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations and future research\u003c/h2\u003e \u003cp\u003eThis study has certain limitations. First, the sample included gastronomy students so the findings cannot be directly generalized to industry workers. However, this was a deliberate theoretical decision to provide initial insights into the future workforce\u0026rsquo;s perceptions. Future studies can examine how these perceptions evolve by comparing students with early-career professionals and using longitudinal designs. Experimental designs based on cultural context, ethical perceptions, and real robot chef experiences would further deepen the knowledge base in this field.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e Not Funded\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Data will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e The authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e The aforementioned project and informed consent have been reviewed and approved by the Ethics Committee (….).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.B. conceived and designed the study. N.B. conducted the literature review, collected the data, and performed the data analysis. N.B. wrote the original draft of the manuscript and revised it critically for important intellectual content. N.B. approved the final version of the manuscript and agrees to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgarwal, R., Prasad, J., 1998. A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf. Syst. Res. 9 (2), 204\u0026ndash;215. https://doi.org/10.1287/isre.9.2.204\u003c/li\u003e\n\u003cli\u003eBelanche, D., Casal\u0026acute;o, L.V., Flavi\u0026acute;an, C., Schepers, J., 2020. Robots or frontline employees? Exploring customers\u0026rsquo; attributions of responsibility and stability after service failure or success. J. Serv. 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Council of higher education. number of H\u0026amp;T programs offered by universities in T\u0026uuml;rkiye. https://yokatlas.yok.gov.tr/lisans-bolum.php?b=10208 \u003c/li\u003e\n\u003cli\u003eZhu, D.H., Chang, Y.P., 2020. Robot with humanoid hands cooks food better? Effect of robotic chef anthropomorphism on food quality prediction. Int. J. Contemp. Hosp. Manag. 32 (3), 1367\u0026ndash;1383. https://doi.org/10.1108/IJCHM-10-2019-0904\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Robot chefs, Anthropomorphism, Job insecurity, Experience intentions, Culinary students, Willingness to pay a premium","lastPublishedDoi":"10.21203/rs.3.rs-8678823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8678823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid proliferation of service robots and artificial intelligence is reshaping tourism sector jobs and redefining future workforce expectations. Regarding robot chefs, research has largely investigated the perceptions of customers and existing employees rather than those of emerging hospitality professionals. Accordingly, this study draws on the anthropomorphism framework to examine how culinary students\u0026rsquo; perceptions of robot chefs affect experience intention and willingness to pay a premium, negative attitudes, and perceived job insecurity. Data were collected from university students studying gastronomy to test the hypotheses using partial least squares structural equation modeling (PLS-SEM). The findings showed that robot chef anthropomorphism directly and positively affects experience intention and reduces negative attitudes toward robots. Perceived innovation directly strengthens experience intention, while perceived job insecurity increases negative attitudes. Negative attitudes toward robots do not significantly affect experience intention, suggesting that students remain open to interacting with robot chefs in terms of their professional learning motivation and future job roles. Finally, experience intention strongly predicts greater willingness to pay a premium. These findings contribute to the relevant literature by indicating that robot chefs should be positioned in culinary education not only as a technological innovation but as an element creating experience-based value.\u003c/p\u003e","manuscriptTitle":"Robot Chefs as Both a Threat and an Experience: The Roles of Anthropomorphism, Job Insecurity, and Culinary Students’ Experience Intentions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 12:48:52","doi":"10.21203/rs.3.rs-8678823/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e93fd4ed-38d2-44fd-af95-5f17d857273e","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-05T00:54:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 12:48:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8678823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8678823","identity":"rs-8678823","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}