Abstract
Purpose
Posting and sharing about food on social media has surged in popularity amongst younger generations such as Millennials and Generation Z. This study aims to analyse and compare food-tourism sharing behaviour on social media across generations. First, this study specifically investigates the factors influencing the intention to share food experiences on social media; second, it examines the impact of sharing intention on actual behaviour and loyalty; and third, it determines whether Millennials and Generation Z differ in these relationships.
Design/methodology/approach
A survey was carried out of Millennial and Generation Z travellers who shared food experiences on social media. Structural equation modelling (SEM) and multi-group analysis were performed to examine the cause-and-effect relationship in both generations.
Findings
The findings reveal differences in motivation, satisfaction, sharing intention, sharing behaviour and loyalty between generations (Millennials and Generation Z).
Research limitations/implications
This study contributes to the literature on the antecedents of food-sharing behaviour in online communities by indicating factors that influence the sharing of culinary experiences and brand or destination loyalty across generations. Suggestions for future research include exploring online food-sharing behaviour through cross-cultural comparisons in various regions.
Practical implications
As Millennials and Generation Z will expand their market share in the coming years, the findings of this study can help improve marketing strategies for culinary tourism and generate more intense food experiences for both generations.
Originality/value
The outcome of the research provides new insights to develop a conceptual model of food-sharing behaviour and tourism on social media by drawing comparisons across generations.
Keywords
Citation
Poyoi, P., Gassiot-Melian, A. and Coromina, L. (2024), "Generation Z and Millennials’ food-sharing behaviour: a cross-generational analysis of motivations, satisfaction and behavioural intention", British Food Journal, Vol. 126 No. 13, pp. 207-225. https://doi.org/10.1108/BFJ-10-2023-0899
Publisher
:Emerald Publishing Limited
Copyright © 2024, Pimsuporn Poyoi, Ariadna Gassiot-Melian and Lluís Coromina
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
In today’s world of digital transformation, social media has played a pivotal role in reshaping travellers’ behaviour. With ubiquitous Internet access and the rise of online social networking sites, the practice of posting photographs of food has become a pervasive trend, especially amongst younger generations, such as Generation Z and Millennials (Abril et al., 2022; Javed et al., 2021; Pham et al., 2019). Indeed, both generations have exhibited a propensity for taking photos whilst dining and sharing them and their experiences with others on social media. This is particularly useful in tourism, where people often rely on online reviews posted by previous customers when deciding where to dine (Peng, 2019). In this sense, social media has tremendously influenced tourists' dining habits, changing not just what they decide to eat but also how they consume and the reasons for their consumption (Atwal et al., 2019; Javed et al., 2021).
The impact of user-generated content (UGC) on social media, including text, images, videos, reviews and comments, extends beyond making travel or purchase decisions (Leung et al., 2013; Liu et al., 2018). It also shapes perceptions (Irimiás and Volo, 2023; Orea-Giner and Fusté-Forné, 2023) and overall experiences (Chen et al., 2021; Zhu et al., 2019). However, UGC, also known as electronic word-of-mouth (eWOM), can be a breeding ground for misinformation and fake news (Blandi et al., 2022; Chen et al., 2023) and lead to negative attitudes toward brands (Mehra, 2023). On the other hand, negative customer reviews provide organisations with opportunities to improve their service quality (Poyoi et al., 2023). In other words, sharing food-related information and experiences with others through social networking is deemed favourable in the business sector, as it enhances the efficiency of tourist attraction management (Zhu et al., 2019) and contributes to creating a positive image for tourist sites (Fatemi et al., 2023). Accordingly, destination marketers should actively encourage travellers to spread eWOM about their travel experiences (Li et al., 2021), as it is integral to promoting tourism destinations and selecting tourist sites (Liu et al., 2018). Motivating individuals to voluntarily share their positive experiences online is one of the main challenges for social media practitioners in tourism.
After considering the literature on tourists’ sharing behaviour of food-related content via social media cited above, some research gaps have been identified.
First, some precedents of food-sharing intentions have been described. For example, previous literature has identified tourist motivations as determinants for sharing their food experiences on social media (Lin et al., 2022; Peng, 2019; Wong et al., 2019; Zhu et al., 2019). In addition, Prebensen et al. (2010) and Uslu (2020) have suggested that satisfaction may directly affect intentions to spread eWOM. Although researchers have studied the effects of both motivation and satisfaction on food-sharing intentions, there is not enough empirical evidence to confirm these relationships within a single framework.
Second, whilst many studies have attempted to explain the impact of UGC or eWOM on social media platforms, the possible consequences of behavioural intention to share food experiences there have not been explored. Specifically, there is a lack of studies about the influence of these food-sharing intentions on specific behavioural components, such as loyalty and actual sharing behaviour.
Third, age has been one of the most relevant sociodemographic variables in exploring differences in consumer behaviour (Makrides et al., 2022). Prior research indicates significant intergenerational differences in the use of the Internet to seek digital information (Blandi et al., 2022), share personal information (Hartijasti and Cho, 2018; Sun and Xing, 2022; Wahyuningsih et al., 2022) or shop (Luo et al., 2023). Likewise, different generations may have distinct behaviours when sharing food experiences on social media platforms. Despite all these assumptions, cross-generational studies of food-tourism sharing behaviour are missing.
The main aim of this study is to bridge these research gaps by analysing and comparing food-tourism sharing behaviour on social media across generations. It focuses on Millennials and Generation Z, since both generations are deemed tech-savvy, are more active on social media platforms, and constitute the major target segment (Blandi et al., 2022; Gumasing and Niro, 2023; Manley et al., 2023).
The specific research questions (RQ) to develop this cross-generational study of Generation Z and Millennials are the following: (RQ1) How does motivation influence the intention to share food experiences on social media?; (RQ2) How does satisfaction influence the intention to share food experiences on social media?; (RQ3) How does the intention to share food experiences determine the actual sharing behaviour?; (RQ4) How does the intention to share food experiences determine loyalty?
The structure of the article is as follows. After this introduction, the theoretical framework and the proposed hypotheses are explained. The methodology is then discussed, with details about the study site, measurement and data analysis. Subsequently, the conceptual model is evaluated, the findings are presented, and the discussion and conclusion are combined in one section. Finally, the article provides valuable insights into theoretical and practical implications, along with limitations and suggestions for future research.
2. Literature review
This section starts with a review of the differences in sharing behaviour across generations, specifically Millennials and Generation Z. After this, it delves into the relevant concepts of this behaviour to formulate the research hypotheses, where the moderating effect of Millennials and Generation Z is integrated. First, the motivations behind the intention to share food experiences are explored. Second, the crucial role of satisfaction in influencing behavioural intention to share is reviewed. Third, the impact of sharing intentions on travellers’ actual sharing behaviour is discussed. Fourth, the impact of these sharing intentions on loyalty is considered.
2.1 Millennials and Generation Z
Previous research has stated that there are significant differences between cross-generational cohorts in the use of the Internet to seek digital information (Blandi et al., 2022) and to share it (Hartijasti and Cho, 2018; Sun and Xing, 2022; Wahyuningsih et al., 2022). According to generational theory, each generational cohort possesses characteristics that are unique to that group (Brosdahl and Carpenter, 2011).
The term Millennials, or Generation Y, typically refers to people born between 1981 and 1996 (Dimock, 2019), so-called “digital natives” as the first age cohort to grow up in a world where technology was already pervasive (Prasad et al., 2019). This generation is now mainly young adults (Hanafiah et al., 2021; Styvén and Foster, 2018). They easily adapt to the Internet, social networks and a wide variety of digital platforms. Millennials use social media mainly to research and compare products and services before making purchase decisions (Hartijasti and Cho, 2018) and are more likely to create and share content, such as photos, videos or blogs, on social media. For example, they are more likely to use Facebook to maintain contact with acquaintances and relatives and use Twitter to keep up with information (Pew Research Center, 2021).
On the other hand, Generation Z refers to people born between 1997 and 2010, when smartphones and social media were already widespread in society (Dimock, 2019). Generation Z is the most digitally proficient generation (Hanafiah et al., 2021). They use social media as a primary means of communication and self-expression (Haddouche and Salomone, 2018). Indeed, Generation Z habitually prefers social media platforms that allow for short-form and visual content, such as Snapchat, TikTok and Instagram, to express their thoughts, emotions, experiences and interests with others (Pew Research Center, 2021). Also, members of Generation Z are more likely to spend more time online (Djafarova and Bowes, 2021) and are more social than previous generations (Slivar et al., 2019).
Given these key differences between Millennials and Generation Z in using social media, this study suggests that generations may moderate the relationships amongst different food-sharing behavioural components (i.e. motivations, satisfaction, intentions to share food experiences, actual sharing behaviour and loyalty). Thus, these moderating effects on different behavioural components will be integrated into the research hypotheses that are formulated in the literature review sections that follow.
2.2 Motivations behind intentions to share food experiences
The literature provides a cognitive explanation of why tourists share food experiences on social media (Abril et al., 2022; Javed et al., 2021; Lin et al., 2022; Wang et al., 2017). According to Wang et al. (2017), five key motivations influence this behaviour: (1) social and relational, (2) self-image projection, (3) emotion articulation, (4) self-archiving and (5) information sharing. These motivations are conceptualised in a two-dimensional plane with two continua: the functional-psychological continuum and the intended continuum (towards others or towards oneself).
In the centre of these continua, many scholars have conceptualised a self-expression theory to explain how and why individuals share online content (Styvén and Foster, 2018; Zhu et al., 2019) and found self-expression to have a significant effect on posting food-related content on social networking sites (Abril et al., 2022; Lin et al., 2022; Peng, 2019).
From a functional point of view, some people post food photographs to create memories during the trip (Iványi and Bíró-Szigeti, 2021; Javed et al., 2021). However, by spreading eWOM about their experiences, they achieve personal fulfilment (Oliveira et al., 2020; Yang and Lai, 2011) and are therefore acting towards themselves and deriving psychological benefits.
In general, literature has also drawn upon the uses and gratifications theory of the Internet and social media to demonstrate that users fulfil personal satisfaction or psychological needs, ultimately influencing sharing behaviour (Falgoust et al., 2022; Kakhk et al., 2019; Yang and Xu, 2021). For example, the perceived enjoyment of using social media (Daxböck et al., 2021; Kang and Schuett, 2013) has been widely identified as significantly influencing users’ willingness to contribute online content and share it with others.
In addition, psychological factors towards others can be considered. For example, the literature refers to altruistic motivation, which is the desire to serve others without expecting anything in return (Cheung and Lee, 2012; Leung et al., 2013; Munar and Jacobsen, 2014; Oliveira et al., 2020).
The effect of motivations on sharing intention has been explained by the literature. In consideration of this previous research and the cross-generational differences mentioned above, the first hypotheses, related to RQ1, are proposed:
Tourist motivation positively influences the intention to share food experiences on social media.
The effect of tourist motivation on the intention to share food experiences on social media is different for Millennials and Generation Z.
2.3 Satisfaction and food-experience sharing intention
Satisfaction is a central concept that has been extensively examined in marketing and tourism literature, arising from a comparison of anticipated and actual experiences encountered during trips (Molinari et al., 2008; Wahyuningsih et al., 2022). Chi et al. (2013) grouped satisfaction towards food consumption into three categories: restaurant atmosphere and service, convenience and local cultural experience, and food quality and variety. A study by Poyoi et al. (2023) acknowledges that the food experiences shared by tourists are primarily related to the food and service, the atmosphere, but also the place and price. Thus, satisfaction with food experiences is a multi-dimensional component of tourists’ behaviour.
In terms of the effects of satisfaction on other behavioural components, the literature highlights the significant relationship between satisfaction with dining experiences and sharing intention (Prebensen et al., 2010; Tan, 2017; Uslu, 2020; Yang, 2017). Specifically, it supports the impact of satisfaction on behavioural intention on social media, which indicates that satisfaction with the service quality and atmosphere of restaurants affects eWOM engagement (Molinari et al., 2008). Specifically, users are more inclined to share positive reviews than negative ones, so customer satisfaction leads to better sharing intentions, including positive information, opinions and recommendations (Poyoi et al., 2023).
Conversely, some studies have cast doubt on the impact of satisfaction on sharing intention, suggesting that satisfaction factors might not be a significant predictor of intention to share food experiences on social media. Tsao and Hsieh (2012) show that customers’ satisfaction with products does not determine their intention to spread positive eWOM. Research on food-sharing behaviour by Javed et al. (2021) and Yang (2017) found that satisfactory dining experiences did not have a direct influence on the behavioural intention to post food experiences on social media (Javed et al., 2021; Yang, 2017).
In view of this controversy and in order to better explore the influence of satisfaction on sharing intentions across generations, the following hypotheses, related to RQ2, are formulated:
Satisfaction positively influences tourists' intentions to share food experiences on social media.
The effect of tourist satisfaction on the intention to share food experiences on social media is different for Millennials and Generation Z.
2.4 The impact of intention to share experiences on actual sharing behaviour
Prior research on knowledge-sharing intention claimed it mediates the relationship between motivation and actual sharing behaviour (Wang et al., 2016). Kakhk et al. (2019) demonstrated that the intention to share one’s information serves as a driver for the sharing behaviour. Drawing on the theory of reasoned action (TRA), Lin and Huang (2013) also mention that behavioural intention directly influences actual behaviour. In particular, sharing travel experiences on social media affects current and consequent behaviour (Wang et al., 2016), so the greater the intention to share, the stronger the sharing behaviour.
Thus, the impact of food sharing intentions on actual sharing behaviour is explored and the following hypotheses, linked to RQ3, are formulated:
Tourists' intentions to share food experiences on social media positively influences actual sharing behaviour.
The effect of tourists’ intentions to share food experiences on social media differently influences the actual sharing behaviour of Millennials and Generation Z.
2.5 The impact of intention to share experiences on loyalty
Wong et al. (2020) found that the impact of sharing intention was also empirically linked to brand loyalty and future behaviour. In line with this finding, Li et al. (2022) found that people are more likely to share information with their peers and will have stronger loyalty when they receive some feedback, such as comments and likes. Additionally, the relationship between sharing memorable tourism experiences on social media and tourists' intentions to visit other destinations was examined by Kumar et al. (2021), who found that the intention to share travel experiences in virtual communities influences revisit intention.
Thus, the following hypotheses linked to RQ4 are formulated:
Tourists' intentions to share food experiences on social media positively influences loyalty.
The effect of tourists’ intentions to share food experiences on social media differently influences the loyalty of Millennials and Generation Z.
In sum, the research model showing all hypotheses is depicted in Figure 1.
3. Methodology
3.1 Study site
Ayutthaya, a city in Thailand, was selected as the site for data collection in this study owing to its designation as a UNESCO World Heritage Site and its position as a significant food tourism destination. Ayutthaya was the capital of Thailand before Bangkok and has several attractive restaurants, local markets, floating markets, food events and street food vendors. That, together with a unique traditional cuisine and cultural heritage, has made the city a foodie destination for both local and foreign tourists (Poyoi et al., 2023). Moreover, 26 restaurants and eateries from Ayutthaya have been listed in the Bib Gourmand of the Michelin Guide 2022, highlighting the city’s remarkable food tourism opportunities. Given the outstanding and varied food experiences in Ayutthaya, the selection of this destination as the case study was well justified within the scope of this research.
3.2 Data collection and sampling
A representative sample of 18- to 42-year-old tourists who used social media to share their food experiences whilst visiting Ayutthaya was selected to participate in a structured questionnaire-based survey (Nardi, 2018). The sampling was designed to ensure a balanced proportional representation of the Millennial and Generation Z cohorts.
The inclusion criteria for the study sample were that respondents be non-residents of the destination, fall within the age range of 18–42 years, and had shared food-related experiences (restaurants, food markets, street food, events, etc.) at the destination on social media platforms.
A survey was conducted at the entrances of the Wat Mahathat and the Wat Phra Mongkol Bophit temples in Ayutthaya Historical Park from 7 June to 5 July 2022. These locations were chosen due to being famous tourist attractions surrounded by culinary landmarks such as the Chao Phrom local market, night markets, street food vendors, restaurants, etc. The participants in the survey were required to answer a screening question about their practice of sharing food experiences on social media during their travels. Those who confirmed using social media to share food experiences, such as posting photos/videos or writing online reviews, were selected to participate as respondents to the study survey. Thus, a non-probability convenience sampling method was used.
Initial data collected were subjected to screening through data cleaning and validation processes to ensure data integrity for research purposes. As a result, the final sample consisted of 392 respondents: 118 international tourists and 274 Thai nationals.
3.3 Questionnaire design
The items used to address the components considered in this study (motivation, satisfaction, intention to share food experience, actual sharing behaviour and loyalty) were designed and developed from the previous literature and a pre-test carried out by the authors.
Regarding the pre-test, the survey was initially evaluated to ensure content validity based on the suggestions of six academic experts. Next, a pilot study was conducted with 50 participants to develop the questionnaire and then revise it based on the results and feedback. Confusing and ambiguous elements were modified during this phase.
Table 1 presents the final constructs and items for measuring causal relationships of the research model, together with previous studies for each construct. Items were measured using a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree).
3.4 Data analysis
The proposed RQ and hypotheses were tested through quantitative analysis. In the first step, confirmatory factor analysis (CFA) was conducted to estimate the fit of the items in the measurement model. Reliability of the measurement scale and the construct validity (convergent and discriminant validity) were also evaluated in this stage.
The next step was a structural equation modelling (SEM) approach using Mplus 7.4. SEM is a multivariate analysis method widely used to simultaneously examine and model causal relationships between constructs (Byrne, 2013; Bollen, 1989; Cheung and Rensvold, 2002). This procedure is used to test the hypothesised relationships of the full model (H1a to H4a).
Finally, multigroup SEM (MGSEM) (Adhikari and Panda, 2020; Cheah et al., 2023) was conducted to examine the cross-generational moderating effects, i.e. to test whether there are significant differences in the model between Millennial and Generation Z travellers (H1b to H4b). To correctly compare the MGSEM model results for Millennials and Generation Z, measurement invariance Z had to be assessed (Brown, 2015; Leitgöb et al., 2023).
4. Results
4.1 Respondent demographics
A sample of 392 valid responses was collected from both generations. Of these, 48.5% were from Millennials, aged 26 to 42, and 51.5% came from Generation Z travellers, aged 18 to 26. The total sample presented 63.8% women and 36.2% men. Participants were well educated, with 67.1% completing an undergraduate degree and 24% holding a postgraduate degree. In terms of occupation, the sample included company employees (39%), students (26.8%), public servants (13.5%), business owners and self-employed (13%), unemployed (5.9%) and homemakers (1.8%). The Millennial sample was primarily comprised of women (63.4%). More than half of the respondents were undergraduates (57.4%) and more than a third had a master’s degree or higher (35.6%). In addition, most Millennial respondents were employees (52.5%). As for Generation Z respondents, the sample was mainly female (63.4%), which is similar to the Millennial sample. One notable difference between the two samples was in terms of education and occupation. Generation Z respondents were mostly students (53.2%) with an undergraduate degree (77.4%) (see Table 2).
4.2 Measurement validation
Confirmatory factor analysis (CFA) with robust maximum likelihood estimation was performed to assess the model fit through five goodness-of-fit indices: the ratio of chi-square to the degree of freedom (χ2/df), the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA) and the standardised root mean square residual (SRMR). The goodness-of-fit index measures for the total sample are acceptable following the suggested threshold of Hooper et al. (2008). The overall goodness-of-fit measurements for the model are χ2/df = 1.778 (χ2 = 284.417 with 160 df), CFI = 0.962, TLI = 0.954, RMSEA = 0.045 and SRMR = 0.046. Thus, the model fit for the pooled sample is confirmed.
The validity of the measurement model was then examined (see Table 3). The results showed that Cronbach’s alphas for all latent constructs were higher than the recommended cut-off value of 0.70, indicating a good internal consistency (Fornell and Larcker, 1981). Besides, all items had standardised factor loadings above the recommended value of 0.5 (Hair et al., 2010). The convergent validity of the constructs was examined using average variance extracted (AVE), where values were all above 0.5, achieving the requirement for convergent validity. In terms of composite reliability (CR), the result verified that all factors were higher than the thresholds (CR > 0.7). Finally, discriminant validity was performed to investigate how effectively a construct differs from other constructs by comparing the square root of the AVE for each construct with the correlations between pairs of the construct (Fornell and Larcker, 1981).
As shown in Table 4, the square roots of AVE values along the diagonal are greater than all correlation coefficients, confirming discriminant validity in this study.
4.3 Structural model
A SEM approach was conducted to test the RQs and the proposed hypotheses. The overall goodness-of-fit indices showed χ2(df = 163) = 293.755, p < 0.001, TLI = 0.953, CFI = 0.960, RMSEA = 0.045 and SRMR = 0.049, indicating a good model fit for the estimated structural model. Figure 2 depicts the standardised path coefficients between constructs. Findings associated with RQ1 found that motivation (β = 0.790, p < 0.001) and satisfaction (RQ2) with dining experiences (β = 0.148, p < 0.05) have significant positive effects on travellers’ intentions to share food experiences on social media. Thus, the results supported H1a and H2a. Concerning RQ3 and RQ4, results suggest that the intention to share food experiences on social media positively influenced travellers’ sharing behaviour (β = 0.723, p < 0.001) and destination loyalty (β = 0.401, p < 0.001), supporting H3a and H4a, respectively.
4.4 Comparison between Millennials and Generation Z
Before conducting the MGSEM analysis between Millennials and Generation Z, measurement invariance was evaluated to ensure comparability across these generations. To assess measurement invariance, an evaluation procedure, recommended by Brown (2015), was performed to test and compare a series of increasingly constrained confirmatory factor analysis (CFA). This evaluation included testing for configural invariance (same factorial structure with no constraints between the groups), metric invariance (same factor loadings) and scalar invariance (same intercepts). To assess each degree of invariance, the difference between the fit of the increasingly constrained CFA model and the following less constrained model was compared using the CFI and RMSEA difference tests recommended by Cheung and Rensvold (2002) (ΔCFI ≤ 0.010 and ΔRMSEA ≤ 0.015). As shown in Table 5, the ΔCFI and ΔRMSEA results were under the thresholds, providing strong evidence for measurement (scalar) and structure (metric) invariance across groups.
Thus, measurement invariance across groups allowed us to use MGSEM analysis to investigate the moderating effect of Millennials and Generation Z on the model. Fit indices for MGSEM showed good fits to the data (χ2 = 541.035, df = 356, p < 0.001, RMSEA = 0.051, SRMR = 0.066 CFI = 0.947 and TLI = 0.943). Thus, the conceptual model can be accepted for assessing the hypothesised relationships, as shown in Figure 3.
Considering the moderating role of the generational effect (H1b, H2b, H3b and H4b), the overall differences in the path coefficients of the two groups are presented in Table 6. Initially, motivation differently influenced the intention to share food experiences on social media for Millennials (βMillennials = 0.867, p < 0.001) and Generation Z (βGeneration Z = 0.690, p-value < 0.001), thus supporting H1b. These results show that the effect of motivation on the intention to share food experiences is stronger amongst Millennials than amongst Generation Z. H2b is supported because the effect of satisfaction on the intention to share food experiences on social media is different for Generation Z (βGeneration Z = 0.238, p-value < 0.01) and Millennials (βMillennials = 0.088, p-value > 0.10).
Regarding the effect of intention to share food experience on actual sharing behaviour, results found a higher effect for Generation Z (βMillennials = 0.665, p-value < 0.001; βGeneration Z = 0.789, p-value < 0.001). Therefore, H3b is supported. Finally, the effect of intention to share food experiences on social media on loyalty is higher for Millennials (βMillennials = 0.403, p-value < 0.001) compared with Generation Z (βGeneration Z = 0.395, p-value < 0.001), supporting H4b.
5. Discussion and conclusions
Sharing dining experiences on social media is a new phenomenon that has emerged with the popularity of social media platforms, particularly amongst Millennial and Generation Z users. Considering earlier research on motivation, satisfaction, sharing intention, sharing behaviour and loyalty, this study investigated the relationship between these dimensions in the context of sharing food-related content on social media (Kakhk et al., 2019; Li et al., 2022; Prebensen et al., 2010; Wong et al., 2020). The study enhances understanding of the determinants of travellers’ intentions to share food experiences on social media and how their sharing intention affects their actual behaviours and loyalty by analysing multi-group SEM between a sample of Millennials and Generation Z.
First, tourist motivation is found to positively influence the intention to share food experiences on social media (supporting H1a). This finding aligns with the research of Yang and Lai (2011) and Daxböck et al. (2021), who emphasised the significant role of motivation in the intention to share content on social networking sites. Some similarities in terms of the positive impact of motivation on behavioural intention to share food experiences on social media existed in the study by Yang (2017), supporting that those tourists more motivated by altruistic needs exhibit a markedly increased propensity for eWOM intentions. Moreover, their motivations for sharing food photos were also driven by a desire for social attention (Javed et al., 2021) and self-expression (Styvén and Foster, 2018; Zhu et al., 2019). Additionally, having fun using social media to share or post reviews on food items and detailing dining experiences is the key motive for willingness to share food experiences there (Kang and Schuett, 2013; Daxböck et al., 2021). As for the path differences, motivations affect the intention to share food experiences on social media differently for Millennials and Generation Z (supporting H1b). Findings reveal that the impact of motivation on the intention to share these food experiences is stronger for Millennials than for Generation Z. This can be due to the fact that Millennials have more experience using social media platforms, and they are also more likely to employ these platforms to search and exchange their opinions with others through online communities than Generation Z (Haddouche and Salomone, 2018; Hanafiah et al., 2021; Styvén and Foster, 2018). Furthermore, Millennials may want to express themselves by sharing food photos or content online to let others know what they eat and where they hang out (Javed et al., 2021).
Second, satisfaction positively influences tourists' intentions to share food experiences on social media (supporting H2a). In this sense, satisfaction levels were cited as an integral component in determining word-of-mouth behavioural intention (Molinari et al., 2008) and results confirm that satisfied people tend to be associated with greater sharing intention, which is consistent with studies conducted by Uslu (2020), indicating customer satisfaction is a key predictor of individuals' willingness to spread eWOM. In addition, based on the empirical evidence from the multi-group analysis, tourist satisfaction with dining experiences influences tourist intention to share food experiences on social media differently for Millennials and Generation Z (supporting H2b). This study supports existing findings on the relationship between restaurant satisfaction and eWOM intention by Yang (2017) or Tsao and Hsieh (2012), who observed that some people may not intend to share their food experience even if their restaurant experience is satisfactory. In this study, satisfaction directly and positively affects the sharing intention of Generation Z travellers but not Millennials. Therefore, satisfaction with the dining experience (i.e. food variety, ambience, staff service and culture) can trigger their inclination to share food tourism reviews online (Björk and Kauppinen-Räisänen, 2016; Poyoi et al., 2023). Possibly due to their age and greater life experience, Millennials place a higher value on the tourism experience than Generation Z (Styvén and Foster, 2018). In this sense, more research is needed to explore their behaviour in the case of dissatisfaction as, for example, Alqadi et al. (2020) found that people tend to share and write negative opinions about restaurant experiences on social media when dissatisfied.
Third, tourists' intentions to share food experiences on social media positively influences actual sharing behaviour (supporting H3a). The results support the study of Lin and Huang (2013), who found that actual behaviour is directly influenced by behavioural intention (Lin and Huang, 2013) and with previous knowledge-sharing studies, confirming that individual intention to share knowledge influences knowledge-sharing behaviour (Yang and Lai, 2011; Yang and Xu, 2021).
By using multi-group SEM analysis, results show that sharing intention impacts actual sharing behaviour differently for Millennials and Generation Z (supporting H3b). Specifically, the intention to generate online content about food experiences tends to have a greater effect on the actual sharing behaviour amongst Generation Z than amongst Millennials. In this regard, although Millennials already seem to prefer online sites to communicate and interact with people (Confente and Vigolo, 2018), Generation Z exceeds that behaviour with stronger intentions to share content online. This could be because they derive enjoyment from it and feel it important to actively provide feedback and comments about their consumption, which may make them more likely to engage in social behaviours, such as sharing food experiences or posting food photos.
Finally, tourists' intentions to share food experiences on social media positively influence travellers’ loyalty (supporting H4a). This result is consistent with prior studies on sharing travel-related experiences on social media, indicating sharing intention on social media has a positive impact on revisiting the same destination (Kumar et al., 2021). From a similar perspective but with a different behavioural intention, Wong et al. (2020) support the idea that sharing memorable tourism experiences on mobile social media leads people to be willing to revisit destinations, and Li et al. (2021) indicate that writing or sharing positive travel experiences on social media can encourage travellers’ post-trip evaluations. In other words, the intention to share food experiences is a precedent of these behavioural intentions in the context of this study. If generations are compared, findings prove that using social media to share food experiences on social media differently affects destination loyalty for Millennials and Generation Z. In this case, Millennials have stronger behavioural intention when they intend to share their food experiences, meaning that they may become more loyal to the destination.
6. Theoretical and practical implications
6.1 Theoretical implications
From a theoretical viewpoint, the study extended the existing body of knowledge on food-related content sharing via social media in three important ways (Abril et al., 2022; Atwal et al., 2019; Javed et al., 2021; Peng, 2019; Wang et al., 2017; Yang, 2017). First, the study proposes a theoretical model that explains the influence of the factors of motivation and dining satisfaction on travellers’ intentions to share their food experiences on social media. Many prior studies on sharing food-related content online have predominantly focused on the effect of motivation on personal willingness to share food experiences with other people within online communities (Abril et al., 2022; Atwal et al., 2019; Lin et al., 2022). However, experiential aspects such as dining satisfaction have been insufficiently explored. This study not only proposes a theoretical model to validate a sharing behavioural intention mechanism, but findings also contribute to the literature by elucidating the substantial role of food experiential value in affecting consumer sharing behaviour.
Second, this research adds to the current literature by shedding light on the impact of the intention to share food experiences via social media on consumer intentions, actual behaviour and destination loyalty. Previous research has extensively explored the consequences of eWOM intentions (Li et al., 2022; Wang et al., 2017). However, the role of behavioural intention to share food experiences on social media on future behaviour has not been investigated.
Third, the study contributes to the extant literature on cross-generational studies and consumer behaviour (Blandi et al., 2022; Gumasing and Niro, 2023; Hartijasti and Cho, 2018; Manley et al., 2023; Wahyuningsih et al., 2022). However, this is the first study to incorporate the generational differences of this behavioural model in the food tourism context and it specifically highlights that age plays a moderating role in determining food sharing intentions and future behaviour.
6.2 Practical implications
The study also provides some managerial implications. The findings can be used as a guideline to boost the awareness of food tourism, food activities and food-related business through understanding the concept of food experience sharing with a public community because behavioural intention or future behaviour is the leading determinant of the success of a travel marketing plan. This study also recommends that food-related businesses or organisations promote food tourism in the destination by offering forums or official websites for customers to establish their own communities and provide comments on the products and services they receive. This is because the eWOM from customers are powerful motivations to purchase or consume their product and services, even more than direct advertising.
According to this comparative study, there is evidence that Millennials and Generation Z are distinct in terms of motivation, dining satisfaction, intention to share food experiences, sharing behaviour and loyalty. This finding can give practitioners a framework for building effective strategies by designing plans relevant to the targeted market based on a generational segmentation to encourage people to share and review food experiences on social media since it reflects loyalty to the brand or the destination.
In addition, it is crucial to pay attention to the factors that trigger a willingness to share dining experiences across generations, as this can help to develop loyalty to the destination. Therefore, it is strongly advised for marketers to respond enthusiastically to those who share posts about food experiences in their destinations. For example, they should encourage travellers to include location or destination hashtags in their posts. This would make it easier to identify visitor reactions and engage them with creative responses. This practice can increase travellers’ assessments of the experience along with their engagement and loyalty to the destination.
7. Limitations and future research
This study has some limitations. First, the respondents were mainly domestic tourists. This limits the interpretation of the results based on the data analysis. Future research may pursue multi-group analysis between local and international travellers and people from different cultural backgrounds to uncover the effect on behaviour of sharing food experiences on social media whilst travelling.
Second, the data were gathered exclusively in Thailand. Expanding this comparative study to include different countries or different samples of tourists is recommended in future studies to explore the generality of its findings. Accordingly, future research should consider cross-cultural and cross-national comparisons. In this sense, it may provide more evidence of these hypothesised relationships across generations.
Lastly, this study considers generations (Millennials and Generation Z) as an important sociodemographic feature that moderates consumers' behavioural intention to share food experiences on social media. However, based on previous research (Makrides et al., 2022), other variables may also act as moderators. For example, gender may influence behavioural intention in the context of eWOM (Alnoor et al., 2022). In this sense, it is recommended to explore this moderating effect for other characteristics and to better acknowledge differences in tourists’ behavioural patterns.
Figures
Figure 1
Proposed research model
Figure 2
Structural model results
Figure 3
Multigroup structural model results
Constructs and items
| Construct | Measurement item | Reference |
|---|---|---|
| Motivation | MOT 1: To get replies from friends/others about my food tourism experience posted on social media | Javed et al. (2021), Munar and Jacobsen (2014) Kang and Schuett (2013), Oliveira et al. (2020), Zhu et al. (2019) |
| MOT 2: To show others how enjoyable the meal experience | ||
| MOT 3: To be socially recognised experience | ||
| MOT 4: To have fun sharing my food experiences on social media | ||
| MOT 5: To improve my self-fulfilment | ||
| MOT 6: To help others make better decisions | ||
| Satisfaction with dining experiences | SAT 1: Food variety and taste | Poyoi et al. (2023), Tan (2017), Yang (2017) |
| SAT 2: Restaurant atmosphere and surrounding environment | ||
| SAT 3: Service | ||
| SAT 4: Local culture involved in gastronomy | ||
| SAT 5: Overall food experiences | ||
| Intention to share food experiences | INT 1: Intention to share my food experience with others on social media | Kumar et al. (2021) |
| INT 2: Intention to share photos of my food experiences on social media | ||
| INT 3: Intention to reply to others who ask or respond to my post shared via social media | ||
| Sharing behaviour | SB 1: When I travel, I share photos of food experiences on social media | Munar and Jacobsen (2014), Wong et al. (2020) |
| SB 2: When I travel, I post videos of food experiences on social media | ||
| SB 3: When I travel, I write reviews or comments about my food experiences on social platforms | ||
| Loyalty | LOY 1: Intention to revisit the destination | Kumar et al. (2021) |
| LOY 2: Intention to return to eat food at the destination | ||
| LOY 3: Intention to recommend others to come to the destination |
Source(s): Authors’ work
The demographic characteristics
| Variable | Frequency (%) | ||
|---|---|---|---|
| Total (n = 392) | Millennials (n = 202) | Gen Z (n = 190) | |
| Gender | |||
| Female | 250 (63.8%) | 128 (63.4%) | 122 (64.2%) |
| Male | 143 (36.2%) | 74 (36.6%) | 68 (35.8%) |
| Education level | |||
| Secondary school or less | 35 (9%) | 14 (6.9%) | 21 (11.1%) |
| Undergraduate | 263 (67.1%) | 116 (57.4%) | 147 (77.4%) |
| Master’s and above | 94 (24%) | 72 (35.6%) | 22 (11.6%) |
| Occupation | |||
| Student | 105 (26.8%) | 4 (2%) | 101 (53.2%) |
| Employee | 153 (39%) | 106 (52.5%) | 47 (24.7%) |
| Business owner/self-employed | 51 (13%) | 34 (16.8%) | 17 (8.9%) |
| Homemaker | 7 (1.8%) | 6 (3%) | 1 (0.5%) |
| Unemployed | 23 (5.9%) | 12 (5.9%) | 11 (5.8%) |
| Public servant | 53 (13.5%) | 40 (19.8%) | 13 (6.8%) |
Source(s): Authors’ work
Construct validity and reliability assessment
| Construct/items | Standardised factor loadings | α | AVE | CR |
|---|---|---|---|---|
| Motivation | 0.87 | 0.54 | 0.87 | |
| MOT 1 | 0.769 | |||
| MOT 2 | 0.696 | |||
| MOT 3 | 0.641 | |||
| MOT 4 | 0.806 | |||
| MOT 5 | 0.716 | |||
| MOT 6 | 0.762 | |||
| Satisfaction with dining experiences | 0.92 | 0.69 | 0.92 | |
| SAT 1 | 0.833 | |||
| SAT 2 | 0.86 | |||
| SAT 3 | 0.847 | |||
| SAT 4 | 0.784 | |||
| SAT 5 | 0.828 | |||
| Intention to share food experiences | 0.84 | 0.65 | 0.84 | |
| INT 1 | 0.874 | |||
| INT 2 | 0.799 | |||
| INT 3 | 0.730 | |||
| Sharing behaviour | 0.79 | 0.57 | 0.80 | |
| SB 1 | 0.768 | |||
| SB 2 | 0.786 | |||
| SB 3 | 0.713 | |||
| Loyalty | 0.91 | 0.78 | 0.92 | |
| LOY 1 | 0.899 | |||
| LOY 2 | 0.938 | |||
| LOY 3 | 0.812 |
Note(s): α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted
Source(s): Authors’ work
Discriminant validity
| MOT | SAT | INT | SB | LOY | |
|---|---|---|---|---|---|
| Motivation (MOT) | 0.734 | ||||
| Satisfaction (SAT) | 0.406 | 0.831 | |||
| Intention to share food experiences (INT) | 0.717 | 0.461 | 0.803 | ||
| Sharing behaviour (SB) | 0.538 | 0.535 | 0.596 | 0.756 | |
| Loyalty (LOY) | 0.571 | 0.638 | 0.558 | 0.400 | 0.885 |
Note(s): Correlation is significant at the 0.01 level; the square root AVE is on the diagonal
Source(s): Authors’ work
Measurement invariance testing
| Model | χ2(df) | RMSEA | CFI | ΔCFI | ΔRMSEA | Δχ2 |
|---|---|---|---|---|---|---|
| Configural invariance | 496.82(320) | 0.053 | 0.949 | |||
| Metric invariance | 510.02(335) | 0.052 | 0.950 | 0.001 | −0.001 | 13.901, p = 0.53 |
| Scalar invariance | 530.28(350) | 0.051 | 0.948 | −0.002 | −0.001 | 19.396, p = 0.20 |
Source(s): Authors’ work
Multigroup analysis results between generations
| Hypotheses | Paths | Standardised estimates | Results | |
|---|---|---|---|---|
| Millennials | Generation Z | |||
| H1b | MOT → INT | 0.867*** | 0.690*** | Supported |
| H2b | SAT → INT | 0.088ns | 0.238* | Supported |
| H3b | INT → SB | 0.665*** | 0.789*** | Supported |
| H4b | INT → LOY | 0.403*** | 0.395*** | Supported |
Note(s): ***p < 0.001; **p < 0.01; *p < 0.05; ns: non-significant
Source(s): Authors’ work
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Acknowledgements
This study was funded by University of Girona (IFUdG2020). This study has also been funded by the Open Access funding provided owing to the CSUC-UdG agreement with Emerald.