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Virtual Culinary Influence: Assessing the Technological Impact of Food Vlogs on Viewer Preferences and Visit Behavior

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26 November 2024

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28 November 2024

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Abstract
Emerging as a pivotal trend on social media, food review vlogs not only narrate culinary experiences but also boost local cuisine and economic growth. This study delves into how such vlogs and vlogger traits affect viewer attitudes and restaurant visit intentions, guided by the stimulus-organism-response paradigm. Hypotheses were quantitatively assessed through an online survey with 347 participants from Amazon’s Mechanical Turk (MTurk), analyzed using SPSS 22 and AMOS 22 for structural equation modeling (SEM). The results indicated that informativeness, entertainment, and vividness influence viewers’ engagement with food review vlogs, while attractiveness and homophily are major predictors of para-social relationships. Content engagement and para-social relationships exerted positive influences on attitudes and visiting intentions toward the reviewed restaurants. The findings contribute to the domain knowledge regarding foodservice communication by identifying desirable characteristics of food review vlogs and vloggers that drive viewers’ attitudes and behavioral intentions. This study offers content creators, foodservice marketers, and restaurant industry stakeholders a better understanding of the psychological process in food review vlog consumption to plan proper content marketing strategies that bolster foodservice businesses and maintain their competitive advantage in the growing restaurant industry.
Keywords: 
Subject: Social Sciences  -   Media studies

1. Introduction

Since 2009, U.S. YouTube has significantly shaped user-generated content (UGC) through video blogging (vlogging), with food vlogs emerging as a top engaging category, experiencing a 170% growth in viewer engagement annually [1]. Research by Millward Brown Digital, Firefly, and Google highlighted YouTube's influence on culinary culture, noting millennials' predominant viewership, which led to a 280% increase in food channel subscriptions [2]. Generation Z's engagement with food content also spiked to 80% by 2021 [3]. The popularity of food vlogs underscores their dual role in entertaining and providing food and beverage (F&B) insights [4], enhancing the commercial appeal of showcased food [5]. These vlogs meet viewers' food cravings through visual and auditory engagement, with the vlogger’s persona amplifying viewer attraction [5,6].
In the evolving landscape of foodservice marketing, consumers have transitioned from passive recipients to active contributors of information on social media, utilizing user-generated content (UGC) as a form of electronic word-of-mouth (eWOM). UGC, a critical influencer of hospitality services, significantly affects purchase behaviors and online reputations [7]. Food review vlogs, a niche in video-based UGC, emerge as potent tools in foodservice marketing, with vloggers shaping viewer preferences and acting as opinion leaders [8]. This research delves into how food review vlogs and vlogger characteristics influence viewers’ dining intentions, filling the research void on their role in consumer decision-making [9,10]. Employing the stimulus-organism-response (S-O-R) framework [11], the study analyzes the impact of vlog-related stimuli on viewer engagement and para-social interactions, and how these factors drive restaurant visitation decisions.

2. Literature Review

2.1. Food Review Vlogs

Frobenius [12] defines a vlog as computer-mediated communication (CMC) or a medium through which the primary contributor can create and share their knowledge and opinions in video format. Due to their audiovisual illustrations and highly interactive features, vlogs are currently the most popular platform for reviews [13] and are considered the best medium to capture facial expressions and transmit accurate messages [14]. In food vlogs, videos’ visual and audio effects illustrate food features appealingly, attracting viewers’ attention and strengthening their desire to try the reviewed food product.
Social media has revolutionized the way consumers access dining information, favoring user-generated content (UGC) for its authenticity and personal touch over business-generated content, with UGC's trustworthiness rooted in real experiences [15]. Consumers leverage social media for peer recommendations, evaluating factors like service quality and price to select top dining spots [16,17]. Food vlogs, offering detailed narratives on cuisine and eateries, integrate critiques with insider views and expert interactions, enriching the gastronomic discourse and enticing viewer interest [10]. They stand out for their depth in culinary aesthetics and cultural storytelling, positioning as a preferred medium for immersive food experiences on social platforms [10].
While research has touched on food shows for information dissemination [18] and the roles of host appeal, novelty, and social norms in vlog viewership [19], the gap in understanding food vlogs' influence on actual dining behavior remains. Addressing this, our study leverages the S-O-R model to empirically assess how food review vlogs shape dining intentions, contributing to the nuanced understanding of digital culinary influence.

2.2. The S-O-R Model

The S-O-R model [11] guides our analysis, offering a framework to predict consumer intentions to visit restaurants after viewing food vlogs. This model suggests that environmental stimuli (S) impact an individual's organism (O), leading to a behavioral response (R). The organism encompasses cognitive processes, informed by information [20], and affective states, reflecting emotional experiences [21]. Responses are the actions or intentions triggered by the stimuli [22]. The S-O-R model is extensively utilized across marketing [23], retailing [24], and information systems [25] to analyze how various stimuli influence individual behavioral responses. Within social media, it elucidates how user motivations for selecting specific products or services translate into behavioral actions [26]. Gogan et al. [26] demonstrated this in their examination of social media continuation, where hedonic, utilitarian, and social gratifications serve as stimuli, emotional states as the organism, and usage intentions as the response, showcasing the S-O-R model's effectiveness in decoding user behavior on social platforms.

2.2.1. Food Vlog Attributes as Stimuli: Informativeness, Vividness, and Social Interactivity

Informativeness can be defined as the extent to which a specific medium offers users helpful and resourceful information [27]. In terms of internet usage, an informative source will satisfy consumers’ information-seeking and self-education needs [28]. Among various online platforms, the vlog is an effective medium for disseminating information about a service or product by its nature as interpersonal information exchange [29]. The availability of online information such as food reviews on social media allows consumers to easily access and compare the prices and benefits of different food services before visiting and making purchase decisions [10]. Because audiences are interested in the fresh experiences, the up-to-dateness of information can be a significant influence on decisions to dine out as a characteristic of social media [30]. In media consumption studies, information acquisition refers to critical stimulation of an individual’s engagement with media sources [31].
Entertainment, as the enjoyment and interest derived from media use [32], enhances engagement with video content and commentary on platforms like YouTube [33]. Viewers' enjoyment of vlog content increases their focus and fosters a sense of connection with the vlogger and community [34]. Food vlogs entertain viewers by sharing intriguing narratives and insights into culinary locales, thus sustaining viewer interest and engagement. Additionally, vividness in media appeals to the senses, enriching the experiential and hedonic aspects of consumption [35,36], making video an immersive format that surpasses text and images in sensory interaction [37]. Such immersion allows viewers to vicariously enjoy the culinary experience, enhancing their willingness to engage with content and visit featured locations [38]. Song [5] found that vicarious pleasure in mukbang streaming notably affects viewers’ positive perceptions and purchase intentions for the food presented.
Social interactivity is defined as the capacity of communication technology to foster a mediated environment for reciprocal messaging, whether one-to-one, one-to-many, or many-to-many [39] (p. 372). Lim [30] demonstrated that on Instagram, the interactivity associated with restaurant information notably boosts the intention to utilize the platform. Social media facilitates engagement with vloggers' narratives and user opinions via comments, enabling viewers to exchange information and connect over shared interests, such as favorite cuisines or dining spots.
H1. 
Informativeness positively impacts viewers’ engagement with food vlogs.
H2a. 
Entertainment positively impacts viewers’ engagement with food vlogs.
H2b. 
Vividness positively impacts viewers’ engagement with food vlogs.
H3. 
Social interactivity positively impacts viewers’ engagement with food vlogs.

2.2.2. Food Vlogger Attributes as Stimuli: Credibility, Physical Attractiveness, and Attitude Homophily

Drawing on the source credibility construct [40], credibility is defined as the reliability of the source and indicates the quality of the information disseminated by the communicator. The perceived credibility of the vlogger (PCV) is the extent to which audiences trust vloggers who communicate endorsement messages [41] when they review or present products effectively and reliably [42]. Source credibility is associated with the source’s trustworthiness and expertise [40]. Food vloggers provide credible reviews, leveraging their expertise in diverse cuisines, thereby fostering viewer trust and credibility in their recommendations. When audiences trust a vlogger, they are likely to hold favorable attitudes toward that vlogger. Sokolova and Perez [43] found that followers revisited reliable sources and maintained para-social relationships with them more than untrustworthy sources.
Physical attractiveness refers to the appealing or pleasing physical features and aesthetic beauty of a person [44]. In online relationships, physical attractiveness can promote para-social interactions with digital influencers [45]. Frequent vlog viewers may be familiar with the influencers’ appearances and find them more attractive; consequently, they are more likely to form para-social relationships with the influencers compared to other visitors [44]. In food review vlogs, besides the food and restaurant background, vloggers’ appearance in front of the camera is a decisive element. Influencers are also the protagonists who share their reactions to and opinions about the food and services.
Attitude homophily is characterized by the belief that “I am not alone. There is someone who thinks like me,” which evokes viewers’ favorable feelings and provides a foundation for para-social relationships. Consumers tend to trust performers with similar opinions, thoughts, and conditions because they believe that these individuals experience things and act in the same way as they do [13]. Sokolova and Kefi [44] also found that parasocial interactions between Instagrammers and YouTubers and their audiences are positively related to their attitude homophily. In food review vlogs, vloggers express their points of view, ideas, and interests in diverse food experiences and circumstances. Once the viewers perceive these shared attitudes and values, they tend to develop an affinity for vloggers’ content and subsequently maintain engagement with it, ultimately developing para-social relationships. The literature and empirical findings lead to the following hypothetical arguments.
H4. 
Credibility positively impacts para-social relationships between food vloggers and their viewers.
H5. 
Food vloggers’ physical attractiveness positively impacts para-social relationships between them and their viewers.
H6. 
Attitude homophily positively impacts para-social relationships between food vloggers and their viewers.

2.2.3. Organism: Content Engagement and Para-social Relationships

In studies on information systems, engagement has been defined as users’ physical and psychological investment to satisfy specific psychological needs such as acquisition, socialization, and enjoyment [46]. In the online environment, users can decide their purpose for engagement with online sources. They can choose to remain passive by simply consuming content or play an active role by taking actions such as commenting, liking, or sharing, and they may even reset content goals to meet their needs [33]. This study focused particularly on viewers’ engagement with food content, namely in their immersion in vloggers’ reviews and their virtual experiences.
Para-social relationships are defined as unilaterally psychological connections that users have with media figures (e.g., vloggers) through the virtual environment [47]. In contrast to one-way communication of traditional media channels, social media channels facilitate media personalities to actively interact with their fans [48], collect viewers’ reactions, and reply to comments about the content [45]. Consequently, para-social relationships likely influence perceptions and behavioral outcomes. Cohen and Holbert [49] demonstrated that para-social relationships inspire audience loyalty in terms of continuous watching. Viewers are more likely to stay engaged with content created by vloggers with whom they have strong bonds [50]. This leads to the following hypothesis.
H7. 
Para-social relationships positively influence viewers’ engagement with food review vlogs.

2.2.4. Responses: Attitudes and Visit Intentions

Attitudes refers to an individual's opinions or perceptions of something, such as the positive or negative feelings about a product or brand [51]. In this study, we focused on viewers’ favorable attitudes toward restaurants endorsed in food vlogs. Yu and colleagues [52] have demonstrated that travel consumers’ engagement with social media significantly influences their attitudes toward destination brands. In the context of food vlogs, when audiences are willing not only to watch but also to engage strongly with vloggers’ content, they may adopt positive attitudes toward the reviewed restaurants.
The affection transfer perspective holds that favorable relationships with and appraisals of an endorser are transferred to the endorsement message and eventually to the endorsed brand [41]. Prior research has examined the influence of para-social relationships between influencers or public figures and their subscribers or fans on attitudes and behaviors, finding that stronger para-social relationships between users and Facebook influencers evoke stronger interest in the promoted products among users [48]. Based on these literature reviews, this study proposed the hypotheses below.
H8. 
Engagement with food vlogs positively influences attitudes toward the reviewed restaurants.
H9. 
Para-social relationships positively influence viewers’ attitudes toward reviewed restaurants.
In tourism research, visiting intentions are a subtype of behavioral intentions concerning one’s commitment to visiting a destination [53]. Visit intentions combine with a rational evaluation of the perceived costs and benefits among all alternatives as an outcome of a mental process that converts desire into behavior [53,54]. Kim and colleagues [55] showed that food tourism videos can generate positive perceptions of a destination’s attributes, ultimately strengthening intentions to visit the destination. In the same vein, we assumed that viewers’ attitudes toward restaurants reviewed in the vlogs would significantly impact their intentions to visit these restaurants.
H10. 
Attitudes of viewers toward reviewed restaurants positively influences visit intentions.
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Figure 1. Research model.
Figure 1. Research model.
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3. Materials and Methods

3.1. Sample and Data Collection

The study's hypotheses were quantitatively tested via an online survey conducted on May 5, 2021, with 347 participants recruited from Amazon’s Mechanical Turk (MTurk), recognized for its validity and demographic diversity [56]. After discarding 27 responses due to lack of food vlog viewing experience or repetitive answers, 320 responses were considered valid for analysis. The demographic breakdown showed 220 male (68.8%) and 100 female participants (31.2%), predominantly Caucasian (57.5%), followed by Asian (35.3%), and smaller percentages of other ethnicities. The age distribution was broad, yet predominantly 25-34 years old (58.4%). Educational levels were high, with 67.5% holding bachelor’s degrees and 2.9% with master’s degrees. Employment status revealed 90.6% were full-time employed. Regarding food vlog engagement, 50.6% viewed vlogs weekly, 25% more than thrice a week, 14.1% rarely, and 10.3% daily. Subscription habits showed 50.9% followed one or two channels, 31.9% three to four, 10.6% over five, and the rest were non-subscribers.

3.2. Measures

The survey was structured into three parts: screening questions, main variable items, and demographic queries. Screening questions assessed frequency of food vlog viewership, subscription counts, and preferred vloggers. Hypotheses were tested using scales adapted from prior research, operationalizing constructs like informativeness [9,33], entertainment [33,57], vividness [58], and social interactivity [33,57,59]. Content engagement was gauged via Flavián et al. [60], vlogger credibility with Sokolova and Perez [43], physical attractiveness via Lee and Watkins [45], and para-social relationships from Sokolova and Perez [43]. Attitude and visit intention measures were based on Hsiao et al. [61] and Popy and Bappy [62]. A 5-point Likert scale from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”) was used. Demographic data collected included age, race, gender, education, and employment status.

3.3. Statistical Produces

The data collected was analyzed by using SPSS 22 and AMOS 22. Structural equation modeling (SEM) was adopted as the primary analytic technique. First, the reliability of each construct was assessed by examining Cronbach’s alpha coefficients. Second, exploratory factor analysis (EFA) was applied to assess the measurement of the constructs, using maximum likelihood estimation with rotation. Third, confirmatory factor analysis (CFA) was performed to determine the constructs’ convergent validity and discriminant validity before testing our hypotheses. Finally, to test the hypotheses, SEM was used to empirically verify the correlations among the constructs and the causal relationships in the structural model.

4. Results

4.1. Analysis Results of Reliability and Validity

Cronbach’s alpha coefficients were evaluated for construct reliability, with Hair [63] recommending a threshold of 0.70 for acceptable internal consistency. All constructs in this study exhibited Cronbach's alpha values above 0.70, indicating reliable measurements (Table 1). Instrument-corrected item-total correlations were analyzed to assess the construct coherence, where Francis and White [64] suggest a benchmark of 0.50 for acceptability. Most items surpassed this threshold, except for two credibility items, leading to their exclusion from further analysis. Exploratory Factor Analysis (EFA) was conducted using maximum likelihood extraction and Promax rotation, showing no cross-loadings and affirming the anticipated 11-factor structure with all factor loadings above 0.50, supporting both convergent and discriminant validity (Table 1). The EFA elucidated that the 11 factors accounted for 70.175% of the total variance, exceeding the 50% criterion for adequate explanatory power and all factors had eigenvalues above 1.00, signifying meaningful contributions [65]. Subsequently, Confirmatory Factor Analysis (CFA) was performed to ascertain the measurement validity within the structural model, indicating a good fit with the data (χ2/df = 1.47, p < .001, CFI = 0.96, GFI = .85, TLI = 0.95, RMSEA = 0.04), aligning with Hu and Bentler’s [66] fit criteria and confirming the appropriateness of the structural model for the study's data.
Table 1. Results of EFA and CFA.
Table 1. Results of EFA and CFA.
Factors/ Items EFA CFA
Factor loading Eigenvalue Alpha Factor loading CR AVE
Factor 1: Informativeness (INF) 9.89 0.94 0.94 0.76
The food review vlogs provide useful information about the food. 0.83 0.89
The food review vlogs are informative about restaurant recommendations. 0.82 0.88
The food review vlogs provide in-depth food-related reviews. 0.79 0.79
The food review vlogs help me discover new restaurants. 0.90 0.89
The food review vlogs help me learn more about local cuisine, culture, and other things around reviewed food. 0.96 0.91
Factor 2: Entertainment (ENT) 1.41 0.88 0.87 0.63
The food review vlogs are entertaining. 0.78 0.75
The food review vlogs are exciting. 0.68 0.85
The food review vlogs are fun to watch. 0.68 0.76
The food review vlogs help me to pass the time when I was bored. 0.94 0.82
Factor 3: Vividness (VIV) 2.76 0.92 0.93 0.76
The food review vlogs provide me with detailed videos of the restaurants. 0.78 0.84
The food review vlogs make the food and restaurants vivid to me. 0.92 0.91
The food review vlogs make information about the restaurants vivid to me. 0.87 0.87
The food review vlogs help me to visualize the food and restaurants in the real world. 0.89 0.87
Factor 4: Social interactivity (SI) 2.64 0.90 0.90 0.70
The food review vlogs enable me to know what others said about restaurants. 0.77 0.77
The food review vlogs facilitate the exchange of ideas and information about food among viewers, including me. 0.80 0.85
The food review vlogs make me feel a sense of belonging to the cuisine fan community. 0.84 0.79
The food review vlogs enable me to connect with people who have the same interests as me. 0.96 0.93
Factor 5: Credibility (CRE) 3.95 0.72 0.93 0.71
The food vlogger is a food review expert. 0.77 0.79
The food vlogger is skilled. * - -
The food vlogger is knowledgeable. 0.83 0.82
The food vlogger is qualified. * - -
I trust in the information provided by the food vlogger. 0.77 0.77
Videos of the food vlogger are reliable. 0.92 0.91
Overall, I recommend videos of the food vlogger. 0.91 0.92
Factor 6: Physical attractiveness (PA) 1.32 0.82 0.82 0.60
The food vlogger is good-looking. 0.63 0.68
The food vlogger is attractive physically. 0.93 0.84
The food vlogger is sexy. 0.74 0.80
Factor 7: Attitude homophily (AH) 2.43 0.91 0.91 0.71
The food vlogger thinks like me. 0.79 0.80
The food vlogger shares my values. 0.88 0.86
The food vlogger has a lot in common with me. 0.76 0.77
The food vlogger behaves like me. 0.94 0.94
Factor 8: Content engagement (CE) 4.55 0.92 0.93 0.71
I was absorbed in watching the food review vlogs. 0.71 0.75
Watching the food review vlogs was worthwhile. 0.89 0.87
The time I spent watching the food review vlogs just slipped away. 0.86 0.85
I felt interested in watching the food review vlogs. 0.85 0.87
Watching the food review vlogs was rewarding. 0.89 0.87
Factor 9: Para-social relationship (PSR) 3.05 0.89 0.89 0.62
The food vlogger makes me feel comfortable, as if I am with friends 0.69 0.74
If my favorite food vlogger appeared on another media, I would watch/ read it to know more. 0.73 0.75
I look forward to watching the last video uploaded by my favorite food vlogger. 0.77 0.76
I miss seeing my favorite food vlogger when he or she is not publishing videos. 0.82 0.83
I want to meet my favorite food vlogger in person. 0.91 0.86
Factor 10: Attitude (AT) 2.26 0.87 0.88 0.65
I like the restaurants reviewed in the vlogs. 0.79 0.73
I have positive impressions of restaurants reviewed in the vlogs. 0.88 0.81
The restaurants reviewed in the vlogs seem good. 0.76 0.74
The restaurants reviewed in the vlogs seem pleasant. 0.94 0.93
Factor 11: Visit intention (VI) 1.22 0.85 0.86 0.66
I intend to visit the restaurants in the upcoming days. 0.63 0.75
I would visit the restaurants recommended in the vlogs rather than other restaurants that serve the same kind of food. 0.86 0.82
I predict that I will visit the restaurants in the vlogs in the future. 0.81 0.87
* This item was eliminated from further structural equation modeling analysis.
Table 2. Goodness-of-fit indices.
Table 2. Goodness-of-fit indices.
χ2   2/df GFI RMSEA CFI TLI
CFA 1370.12, df = 934, p < .001 1.47 0.85 0.04 0.96 0.95
SEM 11592.80, df = 957, p < .001 1.66 0.83 0.05 0.93 0.93
Note: GFI (goodness-of-fit-index), RMSEA (root mean square error of approXimation), CFI (comparative fit index), TLI (Tucker–Lewis Index).
According to Fornell and Larcker [67], average variance extracted (AVE) values exceeding 0.50 indicate good compound reliability and appropriate factors. As shown in Table 1, all items’ factor loadings exceeded .60, and the AVE values for all 11 constructs exceeded .50, which indicated adequate evidence of convergent validity. Moreover, as the squared AVE was larger than the squared correlation coefficient between each pair of variables (Table 3), the constructs achieved adequate discriminant validity. Therefore, all items are concluded to be sufficiently reliable and suitable for the measurement model to conduct the subsequent SEM analysis.

4.2. Hypothesis Testing

SEM performed to analyze the relationships among the variables in the research model. As shown in Table 2, the fit indices of the model were χ2/df = 1.66, RMSEA = 0.05, CFI = 0.93, GFI=0.83, TLI = 0.93, indicating that the proposed model fit the data well [66]. Table 4 presents the results of testing the proposed SEM model. Nine of the 11 direct paths were significant. However, the paths from social interactivity to content engagement and from credibility to para-social relationships were not significant.
Regarding vlog attributes, informativeness positively correlated with content engagement (β = 0.18, p < .05), affirming hypothesis 1. Similarly, entertainment and vividness exhibited positive associations with content engagement (β = 0.24, p < 0.05; β = 0.12, p < 0.05), supporting hypotheses 2 and 3. Conversely, social interactivity's impact on engagement was not significant (β = -0.02, p > 0.05), leading to the rejection of hypothesis 4. For vlogger attributes, physical attractiveness (β = 0.12, p < 0.05) and attitude homophily (β = 0.13, p < 0.05) showed positive relationships with para-social relationships, corroborating hypotheses 6 and 7. However, the influence of credibility on para-social relationships was negligible (β = -0.01, p > 0.05), negating hypothesis 5. The positive link between para-social relationships and engagement (β = 0.12, p < .05) further upheld hypothesis 7. Engagement and para-social relationships significantly influenced attitudes towards reviewed restaurants (β = 0.37, p < 0.001; β = 0.21, p < 0.001), confirming hypotheses 8 and 9. Finally, the strong effect of attitudes on visit intentions validated hypothesis 10.

5. Conclusions

In the digital age, user-generated content such as food review vlogs is a credible and influential medium to communicate information. The present study investigated the influences of food vlogs on viewers’ attitudes and intentions to visit restaurants based on the theoretical framework of the S-O-R paradigm. Particularly, this study sought to determine the desirable attributes of food review vlogs and vloggers that act as stimuli prompting viewers’ content engagement and para-social relationships, and subsequently on attitudes and visit intentions. Ten hypotheses were conducted to verify this process using a structural equation model. Overall, the results confirmed most of the relationships among the main variables (i.e., attributes of food vlogs and vloggers, content engagement, para-social relationship, attitude, and visit intention).

5.1. Theoretical Implications

The current study contributes to the domain knowledge regarding foodservice communication by identifying desirable characteristics of food review vlogs and vloggers that drive viewers’ attitudes and behavioral intentions. To the best of our knowledge, this study is among the first ones to apply the S-O-R model as appropriate to describe content consumption processes in the food review vlog context. Recently, the S-O-R model has also proved effective in demonstrating the relationship between watching live streaming videos and purchase intention in the context of mukbang [8]. Accordingly, the research developed a conceptual and structural model to predict consumers’ visit intentions based on their food vlog consumption.
This study clarifies constructs in the extant literature vlog and vlogger factors as stimuli driving viewers’ attitudes and behaviors. First, we identified three aspects of vlog stimuli— informativeness, entertainment, and vividness are powerful incentives in the vlog environment. As patterns of utilitarian and hedonic gratifications, the informativeness and entertainment value of food vlogs have the most substantial effects on content engagement. These findings are consistent with those of previous studies [10,30,68]. In the form of hedonic experience, the vividness of food vlogs also positively affects viewers’ content engagement. However, in contrast to prior studies identifying the decisive role of interactivity in the social media environment [69], our analysis revealed an insignificant relationship between social interactivity and content engagement, which may have been caused by the measurement scale for social interactivity. Additionally, it can be deduced that since content engagement emphasizes the passive absorption of food vlogs, interactive needs such as online comments or conversations are less necessary during content consumption in comparison to other active engagement activities.
Second, this study yielded novel results concerning the characteristics of food vloggers and para-social relationships. While credibility has been considered as a major factor explaining para-social relationships [43], the current study’s results suggest the opposite direction of causality. The hypothesis about the credibility variable was rejected, and credibility did not have a significant effect on para-social relationships, which contradicts prior studies that showed vlogger credibility can increase perceived social presence and strongly predict intention to visit a destination [70]. The nonsignificant impact of credibility can be explained by the inadequate measurement structure (with two eliminated items) and the sample of non-fans. In the context of this study, despite the contribution of food vloggers’ credibility to content consumption, it is less influential for fostering intimate relationships with their audiences.
On the other hand, physical attractiveness and homophily attitude were found to be strong predictors of para-social relationships. Unlike different vlog content categories such as fitness vlogs [44] or beauty vlogs [71], physical features of food vloggers have not been considered to the same extent in previous studies of food content because viewers apparently only focus on food and background appearances during food vlog content consumption. Our study addressed this gap by discovering that food vloggers’ physical attractiveness has a significant effect on para-social relationships. Indeed, viewers evaluate not only the food and restaurant background but also the vloggers’ appearance. In an aesthetic sense, the physical appeal of food vloggers thus may encourage viewers to build para-social relationships with them. Moreover, among food vlogger attributes, attitude homophily is the strongest determinant of para-social relationships. Similarly, Song et al. [8] found that the attractiveness of mukbang (Korean live streaming eating programs) vloggers has a positive influence on para-social relationships, which in turn had an impact on the viewers’ intention to watch mukbang. In line with social comparison theory, once viewers perceive vloggers’ attitude similarities in values, opinions, and behavioral tendencies, they are likely to form good impressions of the food vloggers, generating solid para-social relationships.
In the second stage of the S-O-R process, the findings confirmed that para-social relationships improve engagement with food vlogs. Viewers’ intimate relationships with their favorite food vloggers encourage continued interest and engagement with vloggers’ content. Furthermore, this study stresses causal links among content engagement, para-social relationships, and attitudes toward recommended restaurants. Both content engagement and para-social relationships have direct influences on viewers’ attitudes toward reviewed restaurants. In line with affection transfer theory, viewers’ positive emotions generated by their para-social relationships with food vloggers shift toward the recommended restaurants. Furthermore, investing more time and effort in watching vloggers’ content and being highly engaged with it generate favorable attitudes toward the reviewed restaurants. In the final stage of the S-O-R process, attitudes and behavioral intentions were direct outcomes of food vlog consumption. Our research also confirmed that the favorable attitudes toward the restaurants reviewed in the food vlogs influence the viewers’ intentions to visit these restaurants.
Overall, the results confirmed the crucial influence of informative, entertaining, and vivid reviews and marketing messages in prompting affective and behavioral responses. Triggered by vlog and vlogger stimuli, organisms produce responses of attitude and visiting intentions via enhanced content engagement and para-social relationships. Furthermore, the current study extends the use of the S-O-R model as appropriate for describing the influential factors, perceptions, and behaviors to enrich the restaurant communication literature in the vlog’s context.

5.2. Practical Implications

This study elucidates the impact of food review vlogs on dining intentions, highlighting their potential as alternative marketing tools in the F&B sector. Particularly post-COVID-19, these vlogs can promote local cuisines and support economic recovery. For vloggers and food service providers, understanding viewer perceptions and the determinants of their engagement is crucial for devising effective strategies to attract and retain customers. Content creators must prioritize engagement and nurture audience relationships to stand out digitally. Tailored content that balances informative and entertaining elements, detailing aspects like food quality, pricing, and services, can enhance viewer interaction and dining choices.
Visual appeal in vlog production is essential; aesthetically pleasing and accurate portrayals of culinary experiences can stimulate viewers' interest and visit intentions. Personal branding, encompassing appearance and personality, also plays a significant role in building para-social relationships and viewer loyalty. Given millennials' predominant viewership, vloggers and marketers should cater to their preferences for creativity, authenticity, and visual engagement. Leveraging UGC, brands can collaborate with vloggers mirroring their target demographics to foster positive brand perceptions and drive consumption. In summary, the findings offer actionable insights for leveraging food vlogs and vlogger influence in marketing strategies to enhance the foodservice industry's appeal and customer base.

5.3. Limitations and Future Research

This study faces limitations that suggest avenues for further research. The generalizability of our results is constrained due to the potentially non-representative sample of 320, primarily comprising Caucasian or Asian males, sourced via MTurk. Despite balanced survey responses across genders and varied demographics, the ethnicity and gender distribution raises questions about the broad applicability of the findings. The use of MTurk, constrained by time and budget, may have captured a general rather than a dedicated audience, possibly affecting the perceived impact of social interactivity and credibility. Future research should target specific, engaged food vlog communities to uncover nuanced insights. Additionally, the reliance on self-reported data may introduce biases, affecting data reliability and validity. Future studies should also investigate other food-related determinants of viewer attitudes and visit intentions, such as vicarious satisfaction, restaurant types, vlogging styles, and vlogger personality traits. Considering the diversity of food vlog audiences, further segmentation by personality, preferences, and consumption habits could enrich understanding and strategic focus.

Author Contributions

Conceptualization, T.A.T.; methodology, S.C.Y. and T.A.T.; writing—original draft preparation, T.A.T.; writing—review and editing, D.P. and S.M.K.; supervision, S.C.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Tubular Labs. The Growth of Food Content on YouTube. Published 2015. Available online: https://tubularinsights.com/growth-food-youtube-report/ (accessed on 04 March 2021).
  2. Delgado, J.; Johnsmeyer, B.; Balanovskiy, S. Millennials Eat Up YouTube Food Videos. Think with Google. Published 2014. Available online: https://www.thinkwithgoogle.com/marketing-strategies/video/millennials-eat-up-youtube-food-videos/ (accessed on 01 December 2021).
  3. Stefanski, R. How to Start a Blog Quickly and Easily to Become Your Own Boss. OneHourProfessor. Published 14 August 2023. Available online: https://onehourprofessor.com/start-blog/ (accessed on 21 August 2023).
  4. Kim, Y. Eating as a transgression: Multisensorial performativity in the carnal videos of mukbang (eating shows). Int. J. Cult. Stud. 2021, 24, 107–122. [Google Scholar] [CrossRef]
  5. Song, H.G. Understanding social media users’ mukbang content watching: Integrating TAM and ECM. Sustainability 2023, 15, 4013. [Google Scholar] [CrossRef]
  6. Kang, E.; Lee, J.; Kim, K.H.; Yun, Y.H. The popularity of eating broadcast: Content analysis of “mukbang” YouTube videos, media coverage, and the health impact of “mukbang” on public. Health Inform. J. 2020, 26, 2237–2248. [Google Scholar] [CrossRef] [PubMed]
  7. Anagnostopoulou, S.C.; Buhalis, D.; Kountouri, I.L.; Manousakis, E.G.; Tsekrekos, A.E. The impact of online reputation on hotel profitability. Int. J. Contemp. Hosp. Manag. 2020, 32, 20–39. [Google Scholar] [CrossRef]
  8. Song, H.G.; Kim, Y.S.; Hwang, E. How attitude and para-social interaction influence purchase intentions of Mukbang users: A mixed-method study. Behav. Sci. 2023, 13, 214. [Google Scholar] [CrossRef]
  9. Brewer, P. The impact of restaurant review website attributes on consumers' internal states and behavioral responses. Ph.D. Dissertation, University of Tennessee, Knoxville, TN, USA, 2017. [Google Scholar]
  10. Briliana, V.; Ruswidiono, W.; Deitiana, T. Do millennials believe in food vlogger reviews? A study of food vlogs as a source of information. J. Mgt. Mkt. Review 2020, 5, 170–178. [Google Scholar] [CrossRef]
  11. Mehrabian, A.; Russell, J.A. An Approach to Environmental Psychology; The MIT Press: Cambridge, MA, USA, 1974. [Google Scholar]
  12. Frobenius, M. Audience design in monologues: How vloggers involve their viewers. J. Pragmat. 2014, 72, 59–72. [Google Scholar] [CrossRef]
  13. Abd Razak, N.I.; Zulkifly, M.I. The impacts of food vlog attributes on para-social interaction and customers’ response behaviours. J. Tour. Hosp. Culin. Arts 2020, 12, 1–11. [Google Scholar]
  14. Dedman, J.; Paul, J. Videoblogging; Wiley: New Jersey, NJ, USA, 2006. [Google Scholar]
  15. Wang, Z.; Sun, L.; Zhu, W.; Yang, S.; Li, H.; Wu, D. Joint social and content recommendation for user-generated videos in online social network. IEEE Trans. Multimed. 2012, 15, 698–709. [Google Scholar] [CrossRef]
  16. Liu, C.H.S.; Lee, T. Service quality and price perception of service: Influence on word-of-mouth and revisiting intention. J. Air Transp. Manag. 2016, 52, 42–54. [Google Scholar] [CrossRef]
  17. Lee, W.; Xiong, L.; Hu, C. The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. Int. J. Hosp. Manag. 2012, 31, 819–827. [Google Scholar] [CrossRef]
  18. Lee, S.W.; Sung, H.J.; Jeon, H.M. Determinants of continuous intention on food delivery apps: Extending UTAUT2 with information quality. Sustainability 2019, 11, 3141. [Google Scholar] [CrossRef]
  19. Pereira, B.; Sung, B.; Lee, S. I like watching other people eat: A cross-cultural analysis of the antecedents of attitudes towards Mukbang. Australas. Mark. J. 2019, 27, 78–90. [Google Scholar] [CrossRef]
  20. Shaver, J.M. Accounting for endogeneity when assessing strategy performance: Does entry mode choice affect FDI survival? Manag. Sci. 1998, 44, 571–585. [Google Scholar] [CrossRef]
  21. Benlian, A. Web personalization cues and their differential effects on user assessments of website value. J. Mgt. Inf. Syst. 2015, 32, 225–260. [Google Scholar] [CrossRef]
  22. Davis, T.R.; Luthans, F. A social learning approach to organizational behavior. Acad. Manag. Rev. 1980, 5, 281–290. [Google Scholar] [CrossRef]
  23. Wang, L.C.; Baker, J.; Wagner, J.A.; Wakefield, K. Can a retail website be social? J. Mark. 2007, 71, 143–157. [Google Scholar] [CrossRef]
  24. Peng, C.; Kim, Y.G. Application of the stimulus-organism-response (SOR) framework to online shopping behavior. J. Internet Commer. 2014, 13, 159–176. [Google Scholar] [CrossRef]
  25. Gu, R.; Oh, L.B.; Wang, K. Developing user loyalty for social networking sites: A relational perspective. J. Electron. Commer. Res. 2016, 17, 1. [Google Scholar]
  26. Gogan, I.C.W.; Zhang, Z.; Matemba, E.D. Impacts of gratifications on consumers’ emotions and continuance use intention: An empirical study of Weibo in China. Sustainability 2018, 10, 3162. [Google Scholar] [CrossRef]
  27. Chen, Q.; Wells, W.D. Attitude toward the site. J. Advert. Res. 1999, 39, 27–38. [Google Scholar]
  28. Papacharissi, Z.; Rubin, A.M. Predictors of Internet use. J. Broadcast. Electron. Media 2000, 44, 175–196. [Google Scholar] [CrossRef]
  29. Xiang, L.; Zheng, X.; Lee, M.; Zhao, D. Exploring consumers’ impulse buying behavior on social commerce platform: The role of para-social interaction. Int. J. Inf. Manag. 2016, 36, 333–347. [Google Scholar] [CrossRef]
  30. Lim, S.Y. Effects of Instagram eating-out information characteristics on satisfaction and intention to use. J. Korea Hotel Resort Assoc. 2019, 18, 241–260. [Google Scholar]
  31. Palmgreen, P.; Wenner, L.A.; Rayburn, J.D. Relations between gratifications sought and obtained: A study of television news. Commun. Res. 1980, 7, 161–192. [Google Scholar] [CrossRef]
  32. Dehghani, M.; Niaki, M.K.; Ramezani, I.; Sali, R. Evaluating the influence of YouTube advertising for attraction of young customers. Comput. Hum. Behav. 2016, 59, 165–172. [Google Scholar] [CrossRef]
  33. Khan, M.L. Social media engagement: What motivates user participation and consumption on YouTube? Comput. Hum. Behav. 2017, 66, 236–247. [Google Scholar] [CrossRef]
  34. Liu, M.T.; Liu, Y.; Zhang, L.L. Vlog and brand evaluations: The influence of para-social interaction. Asia Pac. J. Mark. Logist. 2019, 31, 419–436. [Google Scholar] [CrossRef]
  35. Chua, A.Y.; Banerjee, S. How businesses draw attention on Facebook through incentives, vividness, and interactivity. Int. J. Comput. Sci. 2015, 42, 275–281. [Google Scholar]
  36. Batra, R.; Ahtola, O.T. Measuring the hedonic and utilitarian sources of consumer attitudes. Mark. Lett. 1991, 2, 159–170. [Google Scholar] [CrossRef]
  37. Fortin, D.R.; Dholakia, R.R. Interactivity and vividness effects on social presence and involvement with a web-based advertisement. J. Bus. Res. 2005, 58, 387–396. [Google Scholar] [CrossRef]
  38. Lee, J.; Hong, I. Predicting positive user responses to social media advertising: The roles of emotional appeal, informativeness, and creativity. Int. J. Inf. Manag. 2016, 36, 360–373. [Google Scholar] [CrossRef]
  39. Kiousis, S. Public trust or mistrust? Perceptions of media credibility in the information age. Mass Commun. Soc. 2001, 4, 381–403. [Google Scholar]
  40. Ohanian, R. Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. J. Advert. 1990, 19, 39–52. [Google Scholar] [CrossRef]
  41. Goldsmith, R.E.; Lafferty, B.A.; Newell, S.J. The impact of corporate credibility and celebrity credibility on consumer reaction to advertisements and brands. J. Advert. 2000, 29, 43–53. [Google Scholar] [CrossRef]
  42. Batra, R.; Keller, K.L. Integrating marketing communications: New findings, new lessons, and new ideas. J. Mark. 2016, 80, 122–145. [Google Scholar] [CrossRef]
  43. Sokolova, K.; Perez, C. You follow fitness influencers on YouTube. But do you actually exercise? How para-social relationships, and watching fitness influencers, relate to intentions to exercise. J. Retail. Consum. Serv. 2021, 58, 102276. [Google Scholar] [CrossRef]
  44. Sokolova, K.; Kefi, H. Instagram and YouTube bloggers promote it, why should I buy? How credibility and para-social interaction influence purchase intentions. J. Retail. Consum. Serv. 2020, 53, 101742. [Google Scholar] [CrossRef]
  45. Lee, J.E.; Watkins, B. YouTube vloggers' influence on consumer luxury brand perceptions and intentions. J. Bus. Res. 2016, 69, 5753–5760. [Google Scholar] [CrossRef]
  46. Ruggiero, T.E. Uses and gratifications theory in the 21st century. Mass Commun. Soc. 2000, 3, 3–37. [Google Scholar] [CrossRef]
  47. Rubin, A.M.; Step, M.M. Impact of motivation, attraction, and para-social interaction on talk radio listening. J. Broadcast. Electron. Media 2000, 44, 635–654. [Google Scholar] [CrossRef]
  48. Yuan, S.; Lou, C. How social media influencers foster relationships with followers: The roles of source credibility and fairness in para-social relationship and product interest. J. Interact. Advert. 2020, 20, 133–147. [Google Scholar] [CrossRef]
  49. Cohen, J.; Holbert, R.L. Assessing the predictive value of para-social relationship intensity in a political context. Commun. Res. 2018, 48, 501–526. [Google Scholar] [CrossRef]
  50. Ladhari, R.; Massa, E.; Skandrani, H. YouTube vloggers’ popularity and influence: The roles of homophily, emotional attachment, and expertise. J. Retail. Consum. Serv. 2020, 54, 102027. [Google Scholar] [CrossRef]
  51. Chen, C.C.; Lin, Y.C. What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telemat. Inform. 2018, 35, 293–303. [Google Scholar] [CrossRef]
  52. Yu, G.; Carlsson, C.; Zou, D. Exploring the influence of user-generated content factors on the behavioral intentions of travel consumers. In Proceedings of the 25th Australasian Conference on Information Systems (ACIS), Auckland, New Zealand, 8–10 December 2014. [Google Scholar]
  53. Jang, S.; Bai, B.; Hu, C.; Wu, C.M.E. Affect, travel motivation, and travel intention: A senior market. J. Hosp. Tour. Res. 2009, 33, 51–73. [Google Scholar] [CrossRef]
  54. Chen, Y.C.; Shang, R.A.; Li, M.J. The effects of perceived relevance of travel blogs’ content on the behavioral intention to visit a tourist destination. Comput. Hum. Behav. 2014, 30, 787–799. [Google Scholar] [CrossRef]
  55. Kim, S.; Choe, J.Y.; Lee, S. How are food value video clips effective in promoting food tourism? Generation Y versus non–Generation Y. J. Travel Tour. Mark. 2018, 35, 377–393. [Google Scholar] [CrossRef]
  56. Buhrmester, M.; Kwang, T.; Gosling, S.D. Amazon’s mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspect. Psychol. Sci. 2011, 6, 3–5. [Google Scholar] [CrossRef]
  57. Aluri, A.; Slevitch, L.; Larzelere, R. The effectiveness of embedded social media on hotel websites and the importance of social interactions and return on engagement. Int. J. Contemp. Hosp. Manag. 2015, 27, 670–689. [Google Scholar] [CrossRef]
  58. Sun, Y.; Shao, X.; Li, X.; Guo, Y.; Nie, K. How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electron. Commer. Res. Appl. 2019, 37, 100886. [Google Scholar] [CrossRef]
  59. Chiang, H.S.; Hsiao, K.L. YouTube stickiness: The needs, personal, and environmental perspective. Internet Res. 2015, 25, 85–106. [Google Scholar] [CrossRef]
  60. Flavián, C.; Ibáñez-Sánchez, S.; Orús, C. Integrating virtual reality devices into the body: Effects of technological embodiment on customer engagement and behavioral intentions toward the destination. J. Travel Tour. Mark. 2019, 36, 847–863. [Google Scholar] [CrossRef]
  61. Hsiao, K.L.; Lu, H.P.; Lan, W.C. The influence of the components of storytelling blogs on readers’ travel intentions. Internet Res. 2013, 23, 160–182. [Google Scholar] [CrossRef]
  62. Popy, N.N.; Bappy, T.A. Attitude toward social media reviews and restaurant visit intention: A Bangladeshi perspective. South Asian J. Bus. Stud. 2020, 11, 20–44. [Google Scholar] [CrossRef]
  63. Hair Jr, J.F.; Anderson, R.E.; Tatham, R.L.; Black, W.C. Multivariate Data Analysis with Reading; Prentice-Hall: Englewood Cliffs, NJ, USA, 1995. [Google Scholar]
  64. Francis, J.E.; White, L. PIRQUAL: A scale for measuring customer expectations and perceptions of quality in internet retailing. In Proceedings of the 2002 AMA Winter Educators' Conference: Marketing Theory and Applications, Austin, TX, USA, 22–25 February 2002; pp. 263–270. [Google Scholar]
  65. Savci, M.; Ercengiz, M.; Aysan, F. Turkish adaptation of the Social Media Disorder Scale in adolescents. Arch. Neuropsychiatry 2018, 55, 248. [Google Scholar] [CrossRef]
  66. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  67. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  68. Arviansyah, Dhaneswara, A.P.; Hidayanto, A.N.; Zhu, Y.Q. Vlogging: Trigger to impulse buying behaviors. In Proceedings of the 22nd Pacific Asia Conference on Information Systems (PACIS), Yokohama, Japan, 26–30 June 2018; pp. 249.
  69. Wang, W.; Chen, R.R.; Ou, C.X.; Ren, S.J. Media or message, which is the king in social commerce? An empirical study of participants' intention to repost marketing messages on social media. Comput. Hum. Behav. 2019, 93, 176–191. [Google Scholar] [CrossRef]
  70. Silaban, P.H.; Chen, W.K.; Nababan, T.S.; Eunike, I.J.; Silalahi, A.D.K. How travel vlogs on YouTube influence consumer behavior: A use and gratification perspective and customer engagement. Hum. Behav. Emerg. Technol. 2022, 1, 4432977. [Google Scholar] [CrossRef]
  71. Rasmussen, L. Parasocial interaction in the digital age: An examination of relationship building and the effectiveness of YouTube celebrities. J. Soc. Media Soc. 2018, 7, 280–294. [Google Scholar]
Table 3. Measurement model assessment.
Table 3. Measurement model assessment.
1 2 3 4 5 6 7 8 9 10 11
Physical attractiveness 0.78*
Informativeness 0.09 0.88*
Content engagement 0.13 0.36 0.85*
Credibility 0.04 0.01 0.03 0.85*
Para-social relationship 0.13 0.13 0.23 -0.01 0.79*
Vividness 0.24 0.17 0.24 -0.04 0.28 0.88*
Social interactivity -0.15 -0.34 -0.21 -0.11 -0.05 -0.02 0.84*
Attitude homophily 0.15 0.03 0.08 0.01 0.14 0.1 0.02 0.85*
Attitude 0.41 0.27 0.4 0.07 0.29 0.31 -0.01 0.15 0.81*
Entertainment 0.23 0.59 0.41 0.05 0.24 0.25 -0.48 0.06 0.32 0.80*
Visit intention 0.18 0.6 0.47 -0.05 0.23 0.13 -0.35 0.02 0.34 0.59 0.82*
* The square-root of average variance extracted by each variable.
Table 4. Results of the hypothesis test.
Table 4. Results of the hypothesis test.
Structural path Std. estimate C.R. Result
H1: Informativeness → Content engagement 0.20** 2.59 Supported
H2a: Entertainment → Content engagement 0.29** 2.89 Supported
H2b: Vividness → Content engagement 0.11* 2.15 Supported
H3: Social interactivity → Content engagement -0.04 -0.38 Rejected
H4: Credibility → Para-social relationships -0.01 -0.21 Rejected
H5: Physical attractiveness → Para-social relationships 0.16* 2.06 Supported
H6: Attitude homophily → Para-social relationships 0.13* 1.98 Supported
H7: Para-social relationships → Content engagement 0.10* 2.22 Supported
H8: Content engagement → Attitudes 0.42*** 6.42 Supported
H9: Para-social relationships → Attitudes 0.19*** 3.64 Supported
H10: Attitudes → Visit intentions 0.24*** 5.58 Supported
Note: All path estimates are standardized. *p < 0.05, **p < 0.01, ***p < 0.001.
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