Scrolling the Menu, Posting the Meal: Digital Menu Effects on Foodstagramming Continuance

Abstract

Grounded in the Stimulus–Organism–Response (SOR) framework, this study examines how digital menu design influences visitors’ continuance intention towards foodstagramming within hospitality settings. Specifically, the research investigates the direct and indirect roles of menu visual appeal (MVA) and menu informativeness (MI) as stimuli, desire for food (DoF) and cognitive fluency (CF) as organismic responses, and continuance intention towards foodstagramming (CIF) as the behavioral outcome. Data were collected through a Microsoft Forms administered via food-oriented social media platforms in Egypt that employ digital menus. A total of 404 valid responses were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings confirmed that both MVA and MI are significantly associated with DoF and CF, which in turn positively shape CIF. Moreover, DoF and CF mediate the relationships between menu design attributes (MVA and MI) and CIF, while Product design/visual design story congruency (PVC) strengthens the positive relationships between DoF and CF and CIF. Overall, the results highlighted the critical role of digital menu design in promoting customer engagement behaviours on social media. The study contributes to hospitality and tourism literature by integrating digital interface design with experiential dining research. It also offers practical implications for enhancing customer satisfaction and brand visibility through the strategic implementation of digital menus.

1. Introduction

Food reflects cultural and social values, playing a key role in shaping identity and well-being (El Mahi, 2013; Fieldhouse, 2013; Neely et al., 2014; Rounsefell et al., 2020). Eating habits represent significant components of health and social life (Neely et al., 2014; Rounsefell et al., 2020). In the restaurant sector, the expression “first camera, then forks” illustrates the widespread custom of photographing meals and sharing them on social media (Y. V. Chen et al., 2024b; Wong et al., 2019). This behaviour, known as “foodstagramming,” enables individuals to manage their online presence and engage socially (Califano & Spence, 2024). Social sharing provides pleasure and helps maintain relationships (Bazarova et al., 2015; Y. V. Chen et al., 2024b), while platforms like Instagram play a central role in spreading food images and promoting interaction (Holmberg et al., 2016; Lee et al., 2025). This trend, often referred to as “food porn” (Casales-Garcia et al., 2025; Güler et al., 2024), has gained worldwide popularity, with a notable impact on youth culture and consumer behaviour (Lee et al., 2025; Y. Wu et al., 2024; Zeeni et al., 2024). For instance, in Egypt, more than 45% of Instagram users in the 18–34 age group share food photos and follow restaurant accounts, demonstrating the rising economic and social relevance of foodstagramming (Magdy & Hassan, 2025). At the same time, technological innovation has transformed the food consumption experience. In early 2010, the iPad menu gained popularity in Australia’s food service industry after initially being used in hotel restaurants (Yepes, 2015). Consequently, decision-making, information needs, behaviors, and consumer experiences have been transformed as a result of electronic tablet-based menus (Dickinson et al., 2014; D. Wang et al., 2016). Parallel to this development, the rapid expansion of social media has reshaped consumer engagement worldwide. In Egypt, approximately 46.25 million individuals, representing 41.4% of the population, were active social media users in January 2023 (Sadek et al., 2023). Ts number increased to 45.40 million users by January 2024 (DataReportal, 2025). As “digital natives” who grew up in the digital era, most young adults incorporate social media platforms into their everyday routines due to their early exposure and familiarity with such technologies (Waring et al., 2018).

There has been little scholarly focus on how particular aspects of digital menu design affect customers’ long-term food-sharing habits, despite the restaurant industry’s quick shift from traditional paper menus to digital formats and the rise in foodstagramming as a popular social media activity. The direct relationship between design elements—such as visual appeal and informativeness—and consumers’ cognitive fluency and intention to share food online has not been thoroughly studied, despite previous studies primarily examining foodstagramming as a social and cultural phenomenon, and others examining the functional benefits of digital menus. In addition to providing useful advice for eateries looking to utilise digital technology to enhance customer engagement and increase brand awareness, filling this gap is essential, as it applies the SOR framework to theoretical ideas. Even while social media behaviour and digital technology in the food service industry have been the subject of much research, little focus has been placed on how digital menu design—its informativeness and visual appeal—increases consumers’ cognitive fluency and propensity to share meals online. This study addresses this gap by applying the SOR framework to explain how digital menu design drives food-sharing intentions, offering both theoretical and practical contributions.

Empirical evidence further underscores the impact of social media on food-related behaviours. For example, food advertising on these platforms has a significant influence on young people’s food preferences, decision-making processes, consumption behavior, and perceived social norms (Molenaar et al., 2021). Moreover, young adults actively engage with food-related content by posting food images, following bloggers and influencers, participating in food challenges, sharing recipes and advice, and engaging in online discussions about food (Leu et al., 2022). In this context, Klassen et al. (2018) reported that 59.5% of Instagram posts contained food-related content, and 60% explicitly encouraged eating. In contrast, only 37.8% of Facebook posts included food, while more than half of Instagram posts employed prompting-engagement strategies such as surveys, giveaways, and questions. Similarly, in Egypt, 64.87% of medical students reported being exposed to digital food marketing at least once a week, while 22.24% reported daily exposure and 12.89% less frequent exposure (Sadek et al., 2023). Furthermore, social media platform preferences varied, with WhatsApp accounting for 94.5% of daily use, followed by Facebook (67.4%) and Instagram (40.9%) (Sadek et al., 2023).

The popularity of foodstagramming is also reflected in the massive use of food-related hashtags, which reinforces its global visibility (Casales-Garcia et al., 2025; Y. Wu et al., 2024). Customers’ perceptions of dishes and their hedonic assessments are significantly influenced by images (Walsh & Baker, 2020; Yu et al., 2023). Indeed, individuals are more likely to engage in enjoyable activities, and entertainment factors have a strong impact on human behavior (Sokolova et al., 2024). As electronic word-of-mouth, foodstagramming fosters social engagement, self-presentation, and digital sharing economies (Chang, 2022; Y. V. Chen et al., 2024b). Prior studies confirm that images are more impactful than text reviews (Brewer & Sebby, 2021; Luo et al., 2024), and professionally taken, high-quality images significantly increase customer engagement with social media posts (Brewer & Sebby, 2021). However, exposure to different types of images is also known to affect physical and mental well-being, including changes in body image and dietary behavior (Zeeni et al., 2024).

In recent years, the desire to reduce expenses and improve service has led to an increase in the use of technology in restaurants, such as digital menus and electronic payment systems (Beldona et al., 2014; Jang et al., 2024). One way to influence customer selection is through visual signals, which play a critical role in shaping taste expectations and perceptions of food quality (Mora et al., 2023; van der Laan & Smeets, 2015). Restaurant menus, as internal advertising tools, inform consumers about what to expect from their next meal while promoting brand identity and guiding decisions (Pavesic, 2005; Yim & Yoo, 2020). Digital menus, unlike traditional paper menus, provide interactive visuals, real-time updates, and persuasive elements that influence food choices and perceptions (Cristina et al., 2025; Şahin, 2020). They also create cross-selling opportunities and can be updated easily without additional costs (Blackwood, 2013). Consequently, digital menus not only improve satisfaction and convenience but also increase excitement, enjoyment, and positive customer behaviors (Gupta et al., 2020; Şahin, 2020; Silvestre et al., 2025).

2. Theoretical Background and Hypotheses

2.1. Menu Visual Appeal and Continuance Intention Towards Foodstagramming

Behavioural psychology is the foundation of the SOR theory, which introduces the organism as a mediating factor to the traditional Stimulus–Organism model, also emphasizing the internal mechanisms that link to response (Ming et al., 2021). Accordingly, this framework has been frequently used in environmental psychology, consumer behavior, and marketing (Armawan et al., 2022). In line with Li et al. (2025), the “stimulus” in the SOR theory refers to any external event that has the power to elicit a consumer reaction. Thus, digital menu features such as visual appeal and informativeness serve as stimuli that initiate internal processes within consumers. According to Alam and Noor (2020) and Perumal et al. (2021), the “response”, or the behavioural outcome of the consumer after internal processing of stimuli, encompasses buying intentions, brand loyalty, and actual purchasing behaviour. Therefore, foodstagramming, as a form of consumer-generated content, can be viewed as a response resulting from the interaction between stimuli (e.g., menu design) and the consumer’s internal organismic state. Previous studies have shown that emotive product presentations and ads have a significant impact on consumers’ opinions in the context of general goods and online retail environments (Mishra et al., 2022), supporting the relevance of applying the SOR framework to understand this process.

According to Ibrahim et al. (2025), visual appeal encompasses dynamic content, such as photographs, graphics, and videos, that enhance product and service presentation, encouraging consumer engagement and purchases through brand pages on social media. In contrast, Qi et al. (024) contended that purchase decisions still heavily depend on verbal information, such as menu items, ratings, reviews, and food descriptions. The term “foodstagramming” refers to the activity of taking pictures of food and posting them on social media (Lin et al., 2024). Foodstagrammers are among the most active social media users, sharing food selfies and supporting a positive digital sharing economy. Although foodstagramming may not always increase restaurant visibility, it can fulfil users’ self-presentation and social interaction needs. Additionally, foodstagramming reflects individuals’ passion for food selfies and is reshaping their eating patterns. By using food images as tools for self-presentation on social media, consumers can gain social approval and experience cognitive resonance, which enhances their mood and strengthens social bonds (Lin et al., 2022). Moreover, foodstagramming serves as an effective form of electronic word-of-mouth (Chang, 2022). Consequently, the positive outcomes associated with foodstagramming increase the likelihood that consumers will continue engaging in food-related social media activities. Frequent food experiences increase the likelihood of preference and selection. In the restaurant industry, electronic digital menus with various interactive features have recently supplanted traditional menu formats (Yim & Yoo, 2020). Notably, one of the distinct benefits of digital menus is that they enable customers to peruse and select food products, in addition to viewing enhanced food images and information (Cristina et al., 2025; Şahin, 2020). Furthermore, customers’ demand for food is positively associated with the visual appeal of digital restaurant menus, which, in turn, influences their purchase intentions (Brewer & Sebby, 2021). Therefore, one of the most critical factors in consumers’ decisions to buy food products is their aesthetic evaluation of them (Lee et al., 2025). Similarly, Palcu et al. (2019) argued that customers’ actual consumption and hunger for food can be influenced by static images of eating phases, thereby emphasizing the importance of visual cues in shaping consumer behavior. Given this debate, we make the following hypothesis:

H1a. 

Menu visual appeal positively influences continuance intention towards foodstagramming.

2.2. Menu Visual Appeal and Consumers’ Desire for Food

According to SOR Theory, digital menu designs and food photos are stimuli that appeal to viewers’ senses and set off internal processes like social engagement, aesthetic appreciation, and emotional reactions, which in turn affect behavioral outcomes like food preferences, food selfie sharing, and return intentions (Gopal et al., 2024; Lie et al., 2025). Food selfies are valuable experiences that may promote positive emotional appeals, and the socio-personal advantages of sharing and uploading on social media may eventually boost a person’s inclination to return (Huang et al., 2021). According to Güler et al. (2024), the basis of foodporn is the use of colorful pictures of various completed recipes to highlight the excitement and the feeling of obscurity. As evidence, Martins et al. (2016) indicated that color is one of the most striking and pleasing features of food items, and it has a direct impact on customers’ preferences, choices, and appetites. Moreover, the meal’s color has a significant impact on customer involvement (Casales-Garcia et al., 2025). Although research indicates that image attributes like color, quality, and attractiveness influence users’ degree of engagement (Z. Chen et al., 2023), it is the effective composition of these elements that drives appeal. The phrase “menu visual appeal” (MVA) refers to the visual appeal of menus, encompassing their colour, layout, clarity, and image quality, all of which attract customers and increase their likelihood of making a purchase (Hou et al., 2017; Orth & Malkewitz, 2008).

According to Yim and Yoo (2020), restaurant menus are crucial marketing communication tools that inform consumers about their impending dining experience and expedite business transactions. In this regard, the menu design’s visual elements—background, text colours, textures, images, typefaces, dialogue boxes, menu size, item placement, and pricing give an impact on how consumers react (Brewer & Sebby, 2021). Moreover, the visual appeal of food, which includes bright and colourful dishes, appealing packaging, and presentation, is essential for encouraging consumers to choose healthy options such as salads, vegetables, and main courses (Murphy et al., 2024). Additionally, digital media offers enhanced visual features for meals (Walsh & Baker, 2020). Many people have employed visual imagery to attract interest and effectively communicate information about an organization or product (Lee et al., 2025). Likewise, visual cues influence meal choices and play a crucial role in determining how well food is regarded (van der Laan et al., 2015).

Furthermore, Califano and Spence (2024) proved that the visual display of food photos on menus increases the probability that a dish will be chosen. Consequently, food’s visual appeal is enhanced by visual aspects, including colour, symmetry, freshness, and glossiness, which increase the likelihood that people will find it appetizing and appealing (Nishida et al., 2024). In view of the above-mentioned, a hypothesis can be developed:

H1b. 

Menu visual appeal positively influences consumers’ desire for food.

2.3. Menu Informativeness and Continuance Intention Towards Foodstagramming

The availability of information and the application of communication technology can enhance consumer understanding, alter perceptions, and redirect eating habits and preferences (Plamondon et al., 2022). Accordingly, digital menus have the advantage of allowing for the examination of better food visuals and information (Cristina et al., 2025; Şahin, 2020). Furthermore, digital menus offer a multitude of information that is easily modifiable without incurring substantial extra expenses (Blackwood, 2013). Among the relevant factors, price, quantity size, health considerations, ingredients, and the intricacy of the item description are some menu elements that may influence customers’ propensity to buy (Remar et al., 2022). Additionally, menu elements that influence customer perceptions and behavioural intentions include presentation structure, font style, background colour, physical weight, and other design elements (P. M. C. Lin et al., 2023). Nevertheless, some studies did not find any appreciable variations in the impact of exposure to food photos (healthy, unhealthy, and neutral food) that displayed varying levels of social media information on the probability of eating the foods in the pictures (Y. Wu et al., 2024).

Additionally, businesses can significantly enhance customer confidence in the accuracy and reliability of the information they provide by offering thorough and relevant facts (Ngo et al., 2025). Indeed, according to Qi et al. (2024), linguistic information, such as menu items, ratings, reviews, and culinary descriptions, continues to play a major role in purchase decisions. From a behavioural standpoint, people primarily use Instagram to share information, seek out shared experiences, and track their social identity and status (Z. Chen et al., 2023). Given that food selfies are worthwhile experiences that can elicit positive emotional appeals, the socio-personal benefits of posting and sharing on social media may ultimately increase someone’s desire to return (Huang et al., 2021). The following hypothesis can be derived from this conversation:

H2a. 

Menu informativeness positively influences continuance intention towards foodstagramming.

2.4. Menu Informativeness and Consumers’ Cognitive Fluency

According to the SOR Theory, processing fluency and menu informativeness serve as stimuli that enhance perception, elicit favourable emotional and cognitive reactions, and yield results such as improved decision-making, increased purchase intention, and reduced ordering effort (Jun & Yoon, 2024; Youn, 2024). When something is simpler to understand, it generates favorable emotions that unintentionally spill over onto the target, making it more appealing (Chan & Northey, 2021). Specifically, the term “processing fluency” describes how easily consumers can detect and/or identify a target stimulus (Chan & Northey, 2021). Moreover, when it comes to human judgment and decision-making, processing fluency is another important consideration (Zhang et al., 2018). In fact, consumers use fluency to judge aspects such as truth, likability, confidence, frequency, and psychological distance, among other factors (Deng & Wang, 2020). Furthermore, insightful menu information facilitates cognitive processing and decision-making, as evidenced by its impact on customer decision-making, nutritional perception, overall meal evaluation, and purchase intention (Yoon & George, 2012). Similarly, interactive menu information displays enhance affective states and speed up cognitive processing, indicating that informativeness promotes cognitive engagement (Yim & Yoo, 2020). In addition, by reducing uncertainty, time, and effort, menu informativeness has been shown to have a significant impact on consumers’ perceptions of the ease of ordering food online (Brewer & Sebby, 2021). Hence, a crucial component of cognitive absorption is informativeness, which aids in the efficient digestion and assimilation of information by consumers (Xu et al., 2024). It is possible to construct the following hypothesis from this discussion:

H2b. 

Menu informativeness positively influences consumers’ cognitive fluency.

2.5. Desire for Food and Continuance Intention Towards Foodstagramming

Foodstagramming stimuli such as vibrant food photos and social sharing elicit emotional, social, and self-presentational processes, which in turn lead to reactions like increased online engagement, repeat business, and purchase intentions (Y. V. Chen et al., 2024a; Lian et al., 2025). The probability of preference and selection is increased by regular meal experiences, which in turn affects customers’ desire to purchase (Brewer & Sebby, 2021). According to Sabatini et al. (2025), who examined food desire as a type of pleasure, an individual develops their sense of self and identity as they define and pursue the desired thing. Given that, social media can be used to highlight behaviors that reflect one’s identity (Huang et al., 2021). In addition, the act of taking selfies has been acknowledged as a way for people to manage their image, convey their beliefs, and express their self-gaze (Lin et al., 2022). Hence, “Foodstagramming” refers to the practice of photo-graphing food and sharing it on social media (Lin et al., 2024). Furthermore, Lin et al. (2022) indicated that foodstagramming is a consumer activity that highlights people’s love of food selfies and is changing how they eat. Additionally, some consumers may use foodstagramming to document their eating experiences or make memories of their meals (Chang, 2022). Therefore, more than 45% of Instagram users followed the eateries’ social media accounts and shared pictures of their cuisine (Lin et al., 2024). After this conversation, the following hypothesis is put forth:

H3. 

Desire for food positively influences continuance intention towards foodstagramming.

2.6. Cognitive Fluency and Continuance Intention Towards Foodstagramming

According to the Stimulus-Organism-Response (SOR) theory, environmental stimuli trigger people’s reactions and behavioural intentions to approach or avoid stimuli, emerging response to these evoked emotions (Mehrabian & Russell, 1974). The response reflects an individual’s ultimate choices or behaviors in reaction to the stimuli and their internal thought process (Zafar et al., 2020). Therefore, it explains the relationship between consumer response, external environmental stimuli, and internal organism features (Ibrahim et al., 2025). For example, in the context of Instagram, certain visual components, such as photographs, are believed to function as Stimuli (S) to evoke reactions from consumers (Casales-Garcia et al., 2025). Additionally, research on experiences related to food demonstrates that the S-O-R model can be applied to gastronomic experiences and investigated as a stimulus (Casales-Garcia et al., 2025). In this context, one important factor in online menus is the percentage of visual cues, like pictures or videos (Qi et al., 2024). Furthermore, considering that food selfies are valuable experiences that may promote positive emotional appeals, the socio-personal advantages of sharing and uploading on social media may eventually raise a person’s intention to return (Huang et al., 2021). As a result, social media sharing, a popular self-presentational activity, enables users to represent themselves and derive emotional rewards from self-evaluation and self-affirmation (Lin et al., 2022). Based on the conversation above, a hypothesis can be proposed:

H4. 

Cognitive fluency positively influences continuance intention towards foodstagramming.

2.7. Desire for Food Mediates the Relationship Between Menu Visual Appeal and Continuance Intention Towards Foodstagramming

According to SOR Theory, visual representations of food stimulate feelings and mental processes that encourage interaction and purchase (Youn, 2024). Customers are more likely to make a purchase from menus with visuals than from those with only text (Qi et al., 2024). Ibrahim et al. (2025) claimed that social media platforms like Instagram give marketers access to a variety of content formats that let them interact with their followers and display perceived impact and creativity. As well, customers can now improve digital food photos in a number of ways that may improve the images’ visual appeal, thanks to a range of digital tools (Casales-Garcia et al., 2025). In addition, Instagram stimulates interest in the symbolic aspects of food and provides more opportunities to highlight the visual elements of food photography (Walsh & Baker, 2020). Moreover, sharing pictures of food can grow to be a significant means of forming and preserving relationships (Chang, 2022). It also creates mental images in viewers’ minds and serves as a reminder of pleasurable moments by carefully showcasing the cuisine and restaurant environments (Holmberg et al., 2016). As a result, visual hunger is the term used to describe how viewing pictures of food can increase appetite and desires (Califano & Spence, 2024). Hence, foodstagramming—the practice of posting pictures of meals on social media to influence potential customers—has become a popular trend (Casales-Garcia et al., 2025). Based on this discussion, we assume the following hypothesis:

H5. 

Desire for food mediates the relationship between menu visual appeal and continuance intention towards foodstagramming.

2.8. Cognitive Fluency Mediates the Relationship Between Menu Informativeness and Continuance Intention Towards Foodstagramming

It has long been understood, according to Huang et al. (2021), that consumers use items to achieve social status and presence, in addition to satisfying practical and emotional needs. Furthermore, according to certain research, simply photographing food increases enjoyment by enhancing diners’ (Y. V. Chen et al., 2024a) experience. Additionally, behavioral intention is influenced by the emotional and experiential value of eating (Brewer & Sebby, 2021). Moreover, digital presences are now an essential component of the dining experience, and social interactions in restaurants are no longer limited to the physical space (Oren et al., 2024). In this context, foodstagramming can be an alluring kind of electronic word-of-mouth (Chang, 2022). In addition, information adoption, purchase intent, brand equity, and a company’s financial success can all be impacted by e-WOM (Rasty & Filieri, 2024). However, previous studies have not acknowledged the significance of foodstagramming practices in influencing consumer interaction and improving destination restaurants’ marketing plans (Lin et al., 2024). Moreover, hedonic value is strongly influenced by informativeness (R. Wu et al., 2019). Furthermore, Ngo et al. (2025) demonstrated that efficient customer service enhances consumer confidence in the perceived richness of the information. Consequently, by generating favourable emotional reactions to product presentations, mental imagery enhances consumers’ behavioral intentions (Qi et al., 2024). Based on this discussion, we assume the following hypothesis.

H6. 

Cognitive fluency mediates the relationship between menu informativeness and continuance intention towards foodstagramming.

2.9. Product Design/Visual Design Story Congruency Moderates the Relationship Between Desire for Food and Continuance Intention Towards Foodstagramming

According to Goffman’s (1949) Impression Management Theory, people choose which characteristics of themselves to show others to project a positive image of themselves. In line with this, individuals act or behave in specific ways based on who they are speaking to, and they frequently modify their behaviour to fit what they believe others think of them (Chang, 2022). Accordingly, the presence of others is reflected in the consumption choices made by consumers, who are often concerned about how others may respond to their choices and actions (Huang et al., 2021). According to Labrecque and Milne (2013) and Seifert and Chattaraman (2020), Visual Design Story/Product Congruency (PVC) is the degree of consistency between the visual narrative or promise that the menu offers and the actual product experience, or the internal harmony of visual elements that tell a coherent design story.

There is no doubt that a food selfie is not only a picture but may be considered as far more—a tool to project the self into the virtual community (Y. V. Chen et al., 2024a). Moreover, effective social media impression management typically contributes to social support, which in turn promotes emotional well-being and increased social connection (Lin et al., 2022). Furthermore, exposure to a variety of eating-related content on social media may influence eating-related behaviour (Y. Wu et al., 2024). The visual appeal of food is therefore frequently improved by instruments that allow the viewer to picture the act of picking it up or eating it (Casales-Garcia et al., 2025). Additionally, motivational techniques that impact the food environment (such as labelling, altering portion sizes, menu design, and increasing visibility) enhance children’s and adults’ preferences for and consumption of wholesome foods (Meis-Harris et al., 2024). In the SOR framework, desire for food (O) links menu visual appeal (S) to continuance intention towards foodstagramming (R). Product design/visual design story congruency is proposed as a moderator of this O–R relationship. In light of this conversation, we make the following hypothesis:

H7a. 

Product design/visual design story congruency moderates the relationship between desire for food and continuance intention towards foodstagramming, such that the relationship is stronger when congruency is high.

2.10. Product Design/Visual Design Story Congruency Moderates the Relationship Between Cognitive Fluency and Continuance Intention Towards Foodstagramming

Identity-relevant products, including food and beverages, represent the specific identity that the customer wants to project to others as well as to themselves (Huang et al., 2021). In this context, according to Aesthetics Theory, human eyes like a certain amount of visual complexity, which rises as more visual elements are used (Lee & Lim, 2023). Furthermore, non-visual processes may also be influenced by visual perception, as viewing an image may also be used to assess non-visual aesthetics (Sauer & Sonderegger, 2022). Additionally, features such as the colour and texture of the photos may influence customer behaviour by evoking favorable feelings related to appearance (Güler et al., 2024). Consequently, it has become commonplace for individuals to take pictures of food and share them on social networking sites (Wong et al., 2019). As a result, customers frequently post pictures of their food and reviews of it on various websites, which other diners then use to choose restaurants (Yu et al., 2023). Moreover, nowadays, consumers can enhance digital photos of food in ways that make them more visually appealing, thanks to a variety of digital-enhancing technologies(Casales-Garcia et al., 2025).

Foodstagramming increases the likelihood of revisiting (Huang et al., 2021). In line with this, according to Lin et al. (2024), nearly 50% of travelers have visited a destination restaurant due to social media food images. Moreover, the benefits of foodstagramming include social connections, self-expression, enhanced dining experiences, virtual community interactions, and a record of memorable milestones (like birthdays) (Z. Chen et al., 2023). In our virtual lives, we genuinely create opinions about others based on the photos and videos they share on social media (Cavazza et al., 2020). Furthermore, social media posts that contain user-generated content create dense information networks (Huang et al., 2021). As a result, consumers can obtain social acceptance and cognitive resonance by using food photos as self-presentational resources on social media, which makes them feel good and encourages more social interaction (Lin et al., 2022). Consequently, the positive effects of foodstagramming increase the likelihood that users will continue to engage in food-related social media activities (Lin et al., 2024). In addition, sharing information, seeking common experiences, and following social identification and personal status are the main reasons individuals use Instagram (Z. Chen et al., 2023). According to the SOR framework, cognitive fluency (O) serves as the link between menu information (S) and continued intention toward foodstagramming (R). It is expected that the congruence of the visual design and product design narrative will moderate this O-R. This discussion leads us to make the following hypothesis (Figure 1):

H7b. 

Product design/visual design story congruency moderates the relationship between cognitive fluency and continuance intention towards foodstagramming, such that the relationship is stronger when congruency is high.

3. Materials and Methods

3.1. Instruments and Scales

This explanatory study examines the relationships between independent (Menu visual appeal and Menu informativeness), dependent (Continuance intention towards foodstagramming), mediating (Desire for food and cognitive fluency), and moderating (Product design/visual design story congruency) variables, employing a quantitative research design and a structured questionnaire divided into two main sections. The first section captured participants’ demographic characteristics, while the second encompassed items measuring the core constructs of the study (see Appendix A). Menu Visual Appeal (MVA) includes five items that gauge customers’ level of appeal to a digital menu (Brewer & Sebby, 2021). To assess the menu informativeness (MI) construct, a five-item scale adapted from Gopal et al. (2024) was employed. Gopal applied this scale in his study, which aimed to evaluate customers’ perceptions of MI in digital menus. The measure was originally developed based on the work of Feldman et al. (2006). Desire for Food (DoF) consists of four items borrowed from Brewer and Sebby (2021). To measure cognitive fluency (CF), three items were adapted from J. Wang (2022), which were initially developed to assess CF in consumer advertising. The scale developed by Wang was grounded in the works of Chae and Hoegg (2013). Similarly, three items from Lin et al. (2024) were utilised to evaluate continuance intention towards foodstagramming (CIF). Finally, 3 items were used to gauge the moderator variable (Product design/visual design story congruency (PVC) (Seifert & Chattaraman, 2020). Following the guidelines of Brislin (1980), the original English questionnaire was initially translated into Arabic by two bilingual experts. Subsequently, a separate team conducted a back-translation into English to verify its accuracy and conceptual equivalence. Moreover, to ensure validity, clarity of item wording, and alignment with the study objectives, the questionnaire was reviewed by five academics and six practitioners from restaurants using digital menus, as well as twelve customers from one of these restaurants. Throughout these procedures, only minor modifications were made to the wording of certain measurement items. All items of variables measured were assessed using a 5-point Likert scale, where “1” indicated the lowest rating and “5” the highest.

3.2. Sampling and Participants Selection

This study has developed a questionnaire based on Microsoft Forms to obtain the required data. The target respondents were visitors who had dined at or stayed in establishments in Egypt that utilize digital menus; a screening question was included in the survey to ensure this criterion. The questionnaire was distributed via various social media platforms focused on food and innovative dining experiences. Additionally, we engaged postgraduate students and alumni from the faculty, particularly those employed in restaurants that utilize digital menus, to assist in the distribution and collection of the survey data. Since no official government statistics are available regarding restaurants that utilize digital menus, the researchers contacted officials at the Chamber of Tourist Establishments and Restaurants, affiliated with the Egyptian Tourism Federation. Through this communication, a list was provided that included 27 tourist restaurants employing digital menus. Accordingly, the study used convenience sampling because of cost, time, and ease of access to the targeted participants. Nevertheless, this approach restricts the representativeness of the data and limits the extent to which the findings can be generalised, a limitation that has been explicitly acknowledged in the study. Still, convenience sampling is frequently adopted in hospitality research, where the use of random sampling is often constrained (Aboutaleb et al., 2025). Participants were clearly informed about the purpose of the study. They were assured that there were no right or wrong answers, that all responses would be treated with strict statistical confidentiality, and that they retained the right to withdraw from participation at any stage without any consequences. Moreover, completing and submitting the questionnaire was considered as providing informed consent. In addition, a multi-group analysis (MGA) was employed by SmartPLS, version 3, as presented in the results section. The findings revealed no statistically significant differences across visit frequency, gender, or age groups. These procedures were implemented to minimize potential response bias and ensure the integrity of the data collected. The surveys were distributed between February and July 2025; 404 visitors completed the survey. All visitors’ responses were retained, as the electronic survey was designed with a forced response setting, preventing participants from skipping any questions. As per Krejcie and Morgan (1970) and Krejcie and Morgan (1970) guidelines, based on a confidence level of 95% and a margin of error value of 5%. In cases where the exact population size is unknown, it is standard practice to refer to the next highest category in the table. For populations exceeding 100,000, 384 is generally deemed sufficient to achieve statistical power and generalizability. Accordingly, the current study’s sample of 404 respondents exceeds this threshold, thereby supporting the reliability and validity of the results. The collected study sample includes 208 males (51.5%) and 196 females (48.5%). The age groups varied from 18 to 60 years. As per the level of education, 286 participants (70.8%) hold a bachelor’s degree, followed by 41 participants (10.1%) with a secondary school level.

3.3. Statistical Methods

To assess common method bias (CMB), Harman’s single-factor test (which should be <50%) was performed (Podsakoff et al., 2012). The results showed that a single factor explained only 41.572% of the total variance, suggesting that CMB was not a significant concern. Additionally, CMB was assessed using the “marker variable” approach proposed by Lindell and Whitney (2001). We used “perceptions of airport safety” (Moon et al., 2017) as a marker variable in our model, as it is theoretically unrelated and expected to have only a minimal effect on the study variables (Lindell & Whitney, 2001). This construct was measured using three items. Example item: “I feel that the facilities of this airport are safe” (Moon et al., 2017). The results indicate that the marker variable does not significantly affect any of the model constructs. In addition, the path coefficients (β) of the structural model did not change substantially, nor did the R² values, after the inclusion of the marker variable. Furthermore, all variance inflation factors (VIF) ranged between 1.346 and 2.683 (

Table 1. Assessment of construct reliability and validity.

Dimensions and Variables	λ	[VIF]	[μ]	[σ]	[SK]	[KU]
Menu visual appeal (MVA) (α = 0.881, CR = 0.913, AVE = 0.677, Inner VIF = 1.796)
MVA_1	0.832	2.295	3.267	1.325	−0.501	−0.986
MVA_2	0.830	2.298	3.386	1.197	−0.387	−0.954
MVA_3	0.839	2.355	3.379	1.058	−0.083	−0.818
MVA_4	0.789	2.097	3.371	1.214	−0.215	−0.873
MVA_5	0.823	2.306	3.304	1.102	−0.133	−0.684
Menu informativeness (MI) (α = 0.875, CR = 0.909, AVE = 0.668, Inner VIF = 2.485)
MI_1	0.774	1.670	3.577	1.356	−0.653	−0.746
MI_2	0.870	2.683	2.918	1.331	−0.243	−1.319
MI_3	0.848	2.450	3.139	1.301	−0.300	−1.112
MI_4	0.822	2.186	3.356	1.158	−0.389	−0.600
MI_5	0.768	1.846	3.208	1.267	−0.293	−0.887
Desire for Food (DoF) (α = 0.845, CR = 0.896, AVE = 0.682, Inner VIF = 2.742)
DoF_1	0.853	2.201	3.522	1.192	−0.503	−0.586
DoF_2	0.848	2.033	3.300	1.265	−0.223	−0.948
DoF_3	0.778	1.663	3.418	1.208	−0.338	−0.871
DoF_4	0.823	1.810	3.527	1.241	−0.540	−0.730
Cognitive Fluency (CF) (α = 0.855, CR = 0.912, AVE = 0.775, Inner VIF = 2.464)
CF_1	0.866	1.925	3.500	1.201	−0.631	−0.345
CF_2	0.902	2.506	3.574	1.192	−0.711	−0.281
CF_3	0.872	2.158	3.691	1.160	−0.852	0.067
Continuance intention towards foodstagramming (CIF) (α = 0.755, CR = 0.860, AVE = 0.672)
CIF_1	0.779	1.396	3.399	1.321	−0.633	−0.767
CIF_2	0.816	1.574	3.554	1.025	−0.592	0.083
CIF_3	0.862	1.771	3.525	1.196	−0.618	−0.505
Product design/visual design story congruency (PVC) (α = 0.778, CR = 0.872, AVE = 0.694, Inner VIF = 2.663)
PVC_1	0.852	1.963	3.488	1.304	−0.520	−0.932
PVC_2	0.875	2.046	3.460	1.343	−0.543	−0.911
PVC_3	0.769	1.346	3.389	1.286	−0.480	−0.792

Note: factor loadings = λ, Cronbach’s alpha coefficients = α, composite reliability = CR, average variance extracted = AVE, Skewness = SK, Kurtosis = KU, mean = μ, standard deviation = σ.

), indicating no multicollinearity issues among the predictors (Hair et al., 2019). In addition, the skewness and kurtosis statistics were within acceptable limits (skewness between −0.852 and −0.083; kurtosis between −1.319 and 0.083; see Table 1), thereby supporting the assumption of normality in the data distribution.

Our proposed model consisted of MVA and MI as predictors, DoF and CF as mediator variables, and a dependent variable (CIF); moreover, PVC served as a moderator. We analyzed the data by operating SmartPLS to test the model hypothesis and SPSS V25 to generate descriptive statistics (Mean, standard deviation, skewness, and kurtosis).

4. Results

4.1. Construct Validity and Reliability Assessment

The quality level of the obtained data was evaluated using multiple criteria. Internal reliability for each single construct was calculated by “Cronbach’s alpha” (λ), with scores ranging from 0.755 to 0.881. These values are acceptable, as values above 0.7 showed satisfactory reliability (Nunnally, 1994). Then, “convergent validity” (CV) was calculated by analyzing “Composite Reliability” (CR should be >0.70), which ranged from 0.860 to 0.913, and “Average Variance Extracted” (AVE preferred to be >0.50), which varied between 0.668 and 0.775 (Fornell & Larcker, 1981). Therefore, the CV is acceptable (see Table 1).

Moreover, discriminant validity (DV) was assessed employing the “Fornell–Larcker matrix” and the “Heterotrait-Monotrait” (HTMT) ratio of correlations. In the “Fornell–Larcker matrix”, the √AVE for each single construct should be above its correlations with other dimensions (Fornell & Larcker, 1981). As shown in

Table 2. Fornell–Larcker criterion matrix.

	1	2	3	4	5	6
1. Cognitive fluency	0.880
2. Continuance intention towards foodstagramming	0.553	0.820
3. Desire for Food	0.364	0.571	0.826
4. Menu informativeness	0.543	0.664	0.648	0.817
5. Menu visual appeal	0.423	0.535	0.606	0.533	0.823
6. Product design/visual design story congruency	0.688	0.653	0.529	0.663	0.501	0.833

, the √AVE scores—noted in the bold diagonal values—ranged from 0.817 to 0.880, whereas the inter-construct correlations shown below them were all under the value of 0.688.

As for the HTMT, values should be <0.90 and preferably below 0.85 (Hair et al., 2019). This condition was met, as shown in

Table 3. HTMT Matrix.

	1	2	3	4	5	6
1. Cognitive fluency
2. Continuance intention towards foodstagramming	0.685
3. Desire for Food	0.428	0.713
4. Menu informativeness	0.627	0.812	0.748
5. Menu visual appeal	0.487	0.654	0.688	0.604
6. Product design/visual design story congruency	0.845	0.852	0.649	0.804	0.587

, with the maximum HTMT value being 0.852. The DV of the measurement model used in the current study is confirmed based on the results of the Fornell–Larcker matrix and HTMT values.

4.2. Hypotheses Testing (Inner Model)

Figure 2 and

Table 4. Hypotheses testing.

Hypothesis	β	t	p	F2		Remark
Direct effect
H1a: MVA → CIF	0.149	2.076	0.038	0.031		✔
H1b: MVA → DoF	0.606	15.596	0.001	0.580		✔
H2a: MI → CIF	0.232	3.972	0.001	0.054		✔
H2b: MI → CF	0.543	12.491	0.001	0.418		✔
H3: DoF → CIF	0.228	3.772	0.001	0.047		✔
H4: CF → CIF	0.218	4.086	0.001	0.048		✔
Indirect mediating effect				Confidence intervals
H5: MVA → DoF → CIF	0.138	3.735	0.001	0.064	0.181	✔
H6: MI → CF → CIF	0.118	4.037	0.001	0.069	0.213	✔
Moderating effects
H7a: DoF × PVC → CIF	0.161	3.462	0.001			✔
H7b: CF × PVC → CIF	0.136	3.175	0.002			✔
Cognitive fluency		R2	0.293	Q2		0.216
CIF		R2	0.593	Q2		0.364
DoF		R2	0.366	Q2		0.234

Note: Cognitive fluency = (CF), Continuance intention towards foodstagramming (CIF), Desire for Food (DoF), Menu informativeness (MI), Menu visual appeal (MVA), Product design/visual design story congruency (PVC), ✔ = Supported.

present the findings of the study’s hypothesis evaluation.

Specifically, menu visual appeal (MVA) exhibited a significant direct effect on both continuance intention towards foodstagramming (CIF) (β = 0.149, p < 0.05) and desire for food (DoF) (β = 0.606, p < 0.001), with a strong effect size in the latter relationship (f² = 0.580), supporting H1a and H1b. Likewise, menu informativeness (MI) significantly influenced CIF (β = 0.232, p < 0.001) and cognitive fluency (CF) (β = 0.543, p < 0.001), confirming H2a and H2b.

Both DoF and CF, in turn, significantly predicted CIF (β = 0.228 and β = 0.218, respectively; p < 0.001), demonstrating their mediating roles; thus, H3 and H4 were accepted. Using the bootstrapping procedure by SmartPLS, with 5000 resamples and bias-corrected confidence intervals at the 97% level, the analysis confirmed significant indirect effects. The path MVA → DoF → CIF was significant (β = 0.138, t = 3.735, p < 0.001, CI [0.064–0.181]), and the path MI → CF → CIF was also significant (β = 0.118, t = 4.037, p < 0.001, CI [0.069–0.213]). Both confidence intervals excluded zero, supporting hypotheses H5 and H6.

Additionally, two interaction effects were found to be significant: the congruency between product visual design and narrative (PVC) positively moderated the effects of both DoF (β = 0.161, p = 0.001) and CF (β = 0.136, p = 0.002) on CIF, indicating that H7a and H7b were supported (see Figure 3 and Figure 4). In constructing the interaction terms, all latent variables were mean-centered prior to multiplication, following established recommendations to minimize multicollinearity.

Overall, the model demonstrated satisfactory explanatory power, with R² values of 0.600 for CIF, 0.367 for DoF, and 0.295 for CF. Predictive relevance was also confirmed, as Q² values exceeded the recommended threshold, further supporting the robustness of the structural model.

The study also assessed the endogenous factors “R², Cohen’s f², and Q² values”. As per Cohen (2013), the f² may be “small” (f² ≥ 0.02), “medium” (f² ≥ 0.15), or “large” (f² ≥ 0.35). As Table 4 indicates, the f² of all the endogenous factors in this paper ranged between “small” and “ large”. Additionally, the R² value for CF is 0.293 and for CIF is 0.593, while the R² for DoF is 0.366. The acceptable level of R² depends largely on the research context, and scholars have expressed different perspectives on its interpretation. For instance, Hair et al. (2011) suggested that values of 0.75, 0.50, and 0.25 can be described as substantial, moderate, and weak, respectively. In contrast, Tavakol and Dennick (2011) argued that, in human behavior research, an R² value of 0.20 may already be considered high. Consequently, the study model showed an adequate level of explanatory and predictive capability. Furthermore, as shown in Table 4, the Q² values for the endogenous constructs were all above zero, confirming the predictive relevance of the model (Hair et al., 2019). Specifically, the Q² values were 0.216 for Cognitive Fluency, 0.364 for CIF, and 0.234 for DoF. These results indicate medium to large predictive power across the constructs. Additionally, the standardised root mean square residual (SRMR) and the normed fit index (NFI) were tested, as recommended for PLS-SEM, to confirm the model fit. The SRMR value was 0.069, which is below the threshold of 0.08, indicating an acceptable model fit (Henseler et al., 2015). Additionally, the Bentler-Bonett Normed Fit Index (NFI) was used to assess the model’s fit (Henseler et al., 2016). The acceptable NFI values typically range between 0.60 and 0.90 (Schuberth et al., 2023; Singh, 2009). In this study, the obtained NFI was 0.741, which falls within the acceptable threshold.

Multi-Group Analysis (MGA)

To examine potential differences across groups based on gender, age, and frequency of use, we employed the Multi-Group Analysis (MGA) technique in SmartPLS. Following MGA procedures, a hypothesis is considered supported if the p-value is less than 0.05 or if the t-value exceeds 1.96 (Cheah et al., 2020). As presented in

Table 5. A multi-group analysis (Gender).

	Path Coefficients-Diff (Male–Female)	p-Value (Male vs. Female)
H1a: MVA → CIF	0.033	0.596
H1b: MVA → DoF	0.021	0.387
H2a: MI → CIF	0.089	0.215
H2b: MI → CF	0.040	0.682
H3: DoF → CIF	0.049	0.337
H4: CF → CIF	0.066	0.253
H5: MVA → DoF → CIF	0.035	0.322
H6: MI → CF → CIF	0.028	0.315
H7a: DoF × PVC → CIF	0.041	0.669
H7b: CF × PVC → CIF	0.012	0.551

,

Table 6. A multi-group analysis (Age).

	P.C.-Diff (18–35 vs. 36–50)	P.C.-Diff (18–35 vs. 51–60)	P.C.-Diff (36–50 vs. 51–60)	p (18–35 vs. 36–50)	p (18–35 vs. 51–60)	p (36–50 vs. 51–60)
H1a: MVA → CIF	0.141	0.181	0.322	0.778	0.098	0.041
H1b: MVA → DoF	0.032	0.023	0.009	0.639	0.611	0.480
H2a: MI → CIF	0.181	0.103	0.078	0.084	0.208	0.689
H2b: MI → CF	0.028	0.070	0.097	0.626	0.263	0.197
H3: DoF → CIF	0.032	0.071	0.039	0.595	0.711	0.587
H4: CF → CIF	0.108	0.110	0.218	0.191	0.817	0.941
H5: MVA → DoF → CIF	0.026	0.047	0.021	0.599	0.709	0.575
H6: MI → CF → CIF	0.055	0.036	0.091	0.224	0.682	0.864
H7a: DoF × PVC → CIF	0.142	0.049	0.191	0.931	0.338	0.053
H7b: CF × PVC → CIF	0.059	0.258	0.200	0.273	0.006	0.043

Note: P.C. = Path Coefficients, p = p-Value.

and

Table 7. A multi-group analysis (frequency-of-use).

	P.C.-Diff (3 and More vs. Once)	P.C.-Diff (3 and More vs. Twice)	P.C.-Diff (Once vs. Twice)	p (3 and More vs. Once)	p (3 and More vs. Twice)	p (Once vs. Twice)
H1a: MVA → CIF	0.231	0.327	0.558	0.118	0.960	0.973
H1b: MVA → DoF	0.132	0.060	0.073	0.930	0.725	0.274
H2a: MI → CIF	0.120	0.223	0.103	0.278	0.111	0.358
H2b: MI → CF	0.091	0.035	0.126	0.848	0.370	0.143
H3: DoF → CIF	0.162	0.101	0.263	0.789	0.241	0.125
H4: CF → CIF	0.136	0.032	0.168	0.806	0.406	0.189
H5: MVA → DoF → CIF	0.141	0.052	0.193	0.837	0.279	0.116
H6: MI → CF → CIF	0.103	0.023	0.126	0.839	0.363	0.152
H7a: DoF × PVC → CIF	0.193	0.091	0.285	0.151	0.800	0.912
H7b: CF × PVC → CIF	0.106	0.152	0.046	0.191	0.138	0.394

, no statistically significant differences were observed among the gender, age, or frequency-of-use groups for any of the proposed structural relationships. This indicates that these variables did not exert a meaningful influence on the structural paths under investigation and further confirms that sampling bias was not present in our study.

5. Discussion and Implications

According to Yim and Yoo (2020), traditional menu formats have recently been replaced in the restaurant business by electronic digital menus with a range of interactive capabilities. It promotes consumer participation, resulting in more vivid mental images of food options, greater enjoyment, improved convenience, and heightened excitement for theseptions (Gupta et al., 2020; Şahin, 2020). Thus, the phrase “first camera, then forks” has gained popularity (Y. V. Chen et al., 2024b). Consequently, the development of digital technology exposes us to increasingly inspiring food photography, and we actively contribute to the dissemination of these images through social media (Cavazza et al., 2020). Therefore, food presentation is very important, especially in the social media age we live in today (Lee et al., 2025). According to Holmberg et al. (2016), many young people use Instagram to share images of food, and the food is often presented in a variety of attractive ways. According to Lin et al. (2024), foodstagrammers are among the most active social media users, posting food selfies and promoting a healthy digital sharing economy.

The study’s findings revealed that menu visual appeal positively influences continuance intention towards foodstagramming and consumers’ desire for food (H1a and H1b). This finding is consistent with previous studies, such as those by Martins et al. (2016), which indicate that colour is one of the most eye-catching and pleasing food attributes, having a direct impact on customer preferences, choices, and appetite. Brewer and Sebby (2021) also confirmed that customer demand for food is positively related to the visual appeal of digital restaurant menus, which in turn affects their propensity to purchase. Further, Rosa et al. (2023) argued that multisensory digital menus (such as music and video) increase emotional impact. These results, however, contrast with a study by Yim and Yoo (2020), which shows that consumers’ evaluations of their dining experiences may be negatively impacted by digital menus compared to traditional paper menus.

Concerning (H2a and H2b), the study findings proved that menu informativeness positively influences continuance intention towards foodstagramming and consumers’ cognitive fluency. This result aligns with Lu and Chi (2018), who found that detailed menu information influences customers’ decision-making, nutritional perception, overall meal evaluation, and purchase intention, thereby facilitating cognitive processing and decision-making. This finding is also consistent with Yim and Yoo (2020), who indicated that interactive menu information displays enhance emotional states and accelerate cognitive processing, suggesting that informativeness enhances cognitive engagement. In contrast, Yim and Yoo (2020) found that the information presented on different menus had little effect on either food selection or design preferences.

Regarding (H3), desire for food positively influences continuance intention towards foodstagramming. Köster and Mojet (2018) claimed that food choice is one behavior that has been extensively investigated to understand how and why people make food choices. Similarly, Brewer and Sebby (2021) indicated that frequent eating experiences encourage choice and preference, which affect consumers’ desire to purchase. According to Y. V. Chen et al. (2024b), foodstagramming is a consumer activity that highlights people’s love of food selfies and is influencing their eating habits. Moreover, Ibrahim et al. (2025) found that visual content, encompassing pictures, stories, and short videos, posted on social networking sites significantly influences users’ attitudes toward the content and their willingness to share it.

Furthermore, the study’s results displayed that cognitive fluency positively influences continuance intention towards foodstagramming (H4). Sharing content on social media is a typical self-presentational behavior that enables users to represent themselves and gain emotional benefits from self-affirmation and self-evaluation (Lin et al., 2022). Additionally, since food selfies are valuable experiences that may create positive emotional appeals, the socio-personal advantages of sharing and uploading on social media may eventually increase a person’s propensity to return (Huang et al., 2021). According to L. Wang et al. (2025), cognitive fluency refers to the efficiency of the fundamental mechanisms that support speech, which is influenced to some extent by the number of cognitive resources available. Accordingly, language assets and processing speed are frequently used to study cognitive fluency. This is important since cognitive fluency is a typical cognitive processing experience that can influence how various content is assessed either explicitly or implicitly (Zhang et al., 2018).

Beyond that, the study’s results indicated that desire for food mediates the relationship between menu visual appeal and continuance intention towards foodstagramming (H5). According to Qi et al. (2024), menus with visuals are more likely to result in a purchase than those with only text. In addition, Califano and Spence (2024) used the phrase “visual hunger” to explain how looking at images of food may make one feel hungrier and desire more. Moreover, Walsh and Baker (2020) indicated that Instagram offers more opportunities for showcasing the visually appealing elements of food images and stimulates curiosity about the symbolic aspects of food. Consequently, Foodstagramming, or sharing images of meals on social media, has become increasingly popular as a strategy to attract potential customers (Casales-Garcia et al., 2025). Finally, thanks to a variety of digital tools, consumers can now enhance digital food photos in a number of ways that could enhance the images’ visual appeal (Lee et al., 2025).

As well, regarding (H6), the results demonstrated that cognitive fluency mediates the relationship between menu informativeness and continuance intention towards foodstagramming. According to Y. V. Chen et al. (2024a), photographing food enhances customer experiences and increases their enjoyment. In this context, Wong et al. (2019) referred to this type of consumer behaviour as “foodstagramming” and linked it to the growing popularity of food selfies. Likewise, Plamondon et al. (2022) assert that the availability of information and the use of communication tools have the potential to increase consumer understanding, alter attitudes, and redirect eating habits and preferences. Chang (2022) also found that foodstagramming is an appealing alternative to digitized word-of-mouth. As a sequence, purchase intent and information adoption can be influenced by e-WOM (Rasty & Filieri, 2024).

Notably, the research results demonstrated that product design/visual design story congruency moderates the relationship between desire for food and continuance intention towards foodstagramming (H7a). According to Casales-Garcia et al. (2025), food is more visually appealing when presented in a way that facilitates visualisation of the act of picking it up or consuming it. Contacting a variety of eating-related content on social media may have an impact on eating-related behaviour (Y. Wu et al., 2024). According to Y. V. Chen et al. (2024a), a food selfie is more than just a photo; it is a way to project oneself into a virtual community. Finally, our study results found that product design/visual design story congruency moderates the relationship between cognitive fluency and continuance intention towards foodstagramming (H7b).

To the best of the researcher’s knowledge, this study is among the first to examine how food desires interact to influence the relationship between Instagram post intention and menu attractiveness. Additionally, the study examined the relationship between menu informativeness, Instagram post intention, and cognitive fluency. The association between food cravings and Instagram post intention was also investigated with respect to product design and visual narrative, with a higher correlation observed when congruity was high.

Additionally, the study provides several important practical implications for organizations operating in the food and beverage sector. The results emphasize the need to design visually appealing digital menus that stimulate customer appetite and digital engagement. The findings highlight the importance of creating visually appealing digital menus that stimulate consumer interest and appetite. To foster trust, managers should ensure that the menu visuals and the actual food are consistent and use high-quality photos. This means using adjectives like “crispy” or “fresh” that are visually portrayed and using images that reflect the dish’s vibrant hues. In addition to promoting brand identification, using consistent colours, themes, and designs throughout menus and social media should enhance the visual narrative. Prioritising visual congruency can increase consumer trust, enhance a company’s reputation, foster online interaction, and promote repeat business. These feasible steps offer helpful advice for designing digital menus that work.

6. Conclusions, Limitations, and Future Research

This study significantly advances our knowledge of digital consumer behavior in the context of foodstagramming by extending and applying the Stimulus–Organism–Response (SOR) framework to the domain of digital menus. By combining visual appeal, informativeness, food desire, cognitive fluency, and visual storytelling, it presents a comprehensive model that explains how digital menu design affects customer engagement and continuance intention on social media platforms. The findings show that menu visuals and informativeness enhance cognitive processing and boost customer desire, promoting deeper interaction with digital material. The moderating influence of product design/visual design story congruency further highlights the importance of aligning visual narratives with consumer expectations to boost engagement. These improvements expand the theoretical understanding of digital marketing and offer restaurant managers practical strategies for enhancing digital menus to enhance patron satisfaction and loyalty. Ultimately, this study reveals that menu design is more than just a practical tool; in the dynamic digital environment, it serves as a strategic driver of brand identity, customer connection, and ongoing engagement.

Despite the importance of the findings, the study was limited to a sample of active Instagram users, indicating the need for future research to include other platforms such as TikTok and Facebook. While the study conducted multi-group analyses based on age groups, gender, and visit frequency, it is recommended that future research examine differences across cultures (nationalities), educational levels, religious affiliations, or types of restaurants in response to visual food content. Furthermore, the data were collected at a single point in time, which restricts causal interpretation; future studies could employ longitudinal or experimental designs. Additionally, the study relied on a convenience sample due to cost and accessibility considerations, which limits the representativeness of the population and the generalizability of the findings. Therefore, future research should consider alternative data collection methods to overcome these limitations. Moreover, other mediating or moderating variables, such as technology acceptance, marketing messages, and environmental awareness, could be incorporated in future investigations.