Modeling Facebook users' behavior towards the use of pages related to healthy diet and sport activities

Misirlis, Nikolaos; Elshof, Marjon; Vlachopoulou, Maro Article Modeling Facebook users' behavior towards the use of pages related to healthy diet and sport activities Journal of Tourism, Heritage & Services Marketing Suggested Citation: Misirlis, Nikolaos; Elshof, Marjon; Vlachopoulou, Maro (2021) : Modeling Facebook users' behavior towards the use of pages related to healthy diet and sport activities, Journal of Tourism, Heritage & Services Marketing, ISSN 2529-1947, International Hellenic University, Thessaloniki, Vol. 7 2, pp. 49-57, https://doi.org/10.5281/zenodo.5549911 This Version is available at: https://hdl.handle.net/10419/243092 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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UDC: 658.8+338.48+339.1+640(05) http://doi.org/10.5281/zenodo.5549911 Published online: 18 October 2021 www.jthsm.grpyright © 200 Some rights reserved. Except otherwise noted, this work is licensed under https://creativecommons.org/licenses/by-nc-nd/4.0 Modeling Facebook users’ behavior towards the use of pages related to healthy diet and sport activities Nikolaos Misirlis HAN University of Applied Sciences, The Netherlands Marjon Elshof HAN University of Applied Sciences, The Netherlands Maro Vlachopoulou University of Macedonia, Greece Abstract: Purpose: In this article we aim to model social media users’ behavior in relation with the use of specified Facebook pages and groups, related to eHealth, specifically to healthy diet and sport activities. The study represents to the best of our knowledge the first region-focused on a specific geographical area research. Methods: The users’ personality is measured through the well-known Big Five model and the behavior is predicted with the Theory of Planned Behavior (TPB). Structural Equation Modeling is used in order to statistically control the associations among the diverse observed and latent variables. Results: The results suggest an extended theory of planned behavior in combination with personality traits, on eHealth field. Openness and Extraversion do not seem to have positive effect on Attitude. Users’ attitude can be affected positively from Agreeableness and Subjective Norms, guiding to finally positive affection of users’ actual behavior. Agreeableness cannot influence behavior, directly, nor through SN, since the hypothesis path A-SN is not verified, but it can through Attitude. Neuroticism was negatively correlated to PBC but this hypothesis was not, also, confirmed in the proposed model. Implications: While literature confirms all of our hypotheses, in our study only 8 in 12 are finally confirmed. The difference between the present model and literature findings can be located on the different cultural dimensions among the different studies. The present survey is focused on the Greek region with all the participants to be Greeks. This location-based limitation could be surpassed by conducting the same research on different geographical regions and then confront the outcomes. Keywords: Big Five Personality Model, Personality traits, Facebook, Theory of Planned Behavior JEL Classification: C31, O35, L86 Biographical note: Nikos Misirlis ([email protected]) is a computational social scientist. His research interests are related, but not limited to: Social media, data analysis, consumer behavior and digital marketing. He teaches Digital Marketing at the HAN University of Applied Sciences, Arnhem, The Netherlands. Marjon Elshof ([email protected]) is currently working as researcher, senior lecturer and project leader programme accreditation EFMD at HAN University of Applied sciences. Marjon is currently working on research topics in the field of internal communication, curriculum and competency development and in internationalization in higher education. Maro Vlachopoulou (mav[email protected]du.gr) is a Professor at the Department of Applied Informatics, University of Macedonia, Greece, specializing in Electronic Business & Marketing Information Systems. She holds a PhD in Marketing Information Systems from the Department of Applied Informatics of the University of Macedonia. 1 INTRODUCTION Internet has a major impact on social life with the selfpresentation need or the need to belong to be important (Nadkarni & Hofmann, 2012; Seidman, 2013). Nowadays, human interaction through social media platforms is rapidly increasing, providing researchers the opportunity to understand human behavior exploiting the rich information available in social media (Carpenter, Green, & LaFlam, 2011; Krakover & Corsale, 2021). Facebook counts more than 1.71 billion monthly active users worldwide, as of July 2018 (Samy, 2016; Marengo, Poletti, & Settanni, 2020; Önder, Gunter, & Gindl, 2020). According to Facebook, 757 million users log on Facebook daily with half of them having more than 200 friends (Sedghi, 2014). Facebook has become a useful tool not only for daily friendship interaction but also for seeking information, jobs and advertising medium. Extant research suggests that there is an association between Facebook activity data and personality, demonstrating a relationship between users’ online and offline activities. This indicates that users’ 50 Nikolaos Misirlis, Marjon Elshof & Maro Vlachopoulou personality can be extracted from their Facebook activity (Gosling, Augustine, Vazire, Holtzman, & Gaddis, 2011; Marengo et al., 2020). Facebook profiles reflect actual personality and not just a self-idealization and therefore it is suggested that people use Facebook to communicate their real personality and accurate personal profiles (Amichai- Hamburger & Vinitzky, 2010; Garcia & Sikström, 2014). As a result, it can be claimed that Facebook users do not try to misrepresent their profiles but on the contrary, Facebook profiles represent the true personality of a person. Given the multidisciplinary nature of the field, many researchers use theories from behavioral intention in order to better understand individuals who use specific features of social media. Diffusion of Innovation (DOI), Theory of Reasoned Actions (TRA), Technology of Acceptance Model (TAM) and Theory of Planned Behavior (TPB) are the main theories used by academics and researchers. In this research the TPB model is used for two main reasons: TPB has been widely used for health behavior predictions. Furthermore, the addition of the Big Five model as extra variables on TPB provides a more comprehensive theoretical perspective of the user’s acceptance and final behavior in the context of eHealth features of Facebook (Gretzel et al., 2012; Branley & Covey, 2018). TPB derives from TRA and it was first developed by Ajzen (1985). In TPB the performance of a person’s particular behavior can be predicted by three variables: the attitude of a person towards the behavior (ATT), the subjective norms (SN) and the perceived behavioral control (PBC). These three variables lead to the Intention towards a specific behavior (I), affecting finally the individual’s actual behavior. The present study associates known personality traits with consumers’ planned behavior. Both Big Five and TPB are previously used in literature individually or in combination. The combination, though of the two models in the field of eHealth has never tested or used before. The current literature present a gap, to the best of our knowledge. Most of the variables used have been examined previously, separately but not as a unique conceptual model. Furthermore, the article’s questionnaire is used for the first time. It is also conducted for the first time in Greece. This two aspects - the combination of the models applied in the eHealth field and the localization of the research – cover the existing gap, not only of the theoretical framework but also the practical implication. The aim of this study is to model social media users’ behavior with regard to the use of specified Facebook pages and groups, related to eHealth, specifically to healthy diet and sport activities. Furthermore, this study will be regionfocused on a specific geographical area that has never been used for the aforementioned scope. In order to model this behavior, theories and tools from a variety of disciplines will be used. The present article contributes to multiple aspects: First a theoretical and conceptual model is developed, examining the effects on individuals’ behavior towards healthy diet and sport activities in combination to Facebook pages and groups.. The results will be confronted with other research and outcomes in order to examine the concurrence and consistency with different geographical regions. Finally, the article combines theories and models from different fields, paving the ground to future multidisciplinary research. The paper is organized as follows: In the next section the literature review is presented. Furthermore, the constructs, included in the theoretical model, are explained. Big Five and TPB are thoroughly analyzed. Next paragraph presents the methodology used, following by and the research results. Finally, the last part includes a discussion on the outcomes as well as limitations of the research and future research implications. 2 LITERATURE The research was conducted using questionnaires, in order to examine the factors that affect users’ behavior towards the adoption of healthy eating/ sports tips suggested by various pages/ groups on Facebook. Many theories are used to explain the online behavior of users in combination with their personality (Gosling et al., 2011; Hall & Pennington, 2013; John, Naumann, & Soto, 2008; Daskalaki et al, 2020; Jspeert & Hernandez-Maskivker, 2020). One of them, the Five-Factor Model of personality (FFM), known as Big Five, represents the most commonly used model for researching and examining the relationships between personality traits and online users. This taxonomy is one of the most reliable methods for exporting and monitoring personalities (McCrae & John, 1992; Moore & McElroy, 2012; Ryan & Xenos, 2011; Tan, 2012; Zywica & Danowski, 2008; Misirlis et al, 2018). Big Five is based on the notion that users' personality can be ranked on a five axes model. Every axis represents a specific factor from: Openness to experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism. The aforementioned variables present the first set of our observed set of items. In literature, each one of the five factors has been examined, showing associations with the way users interact on social media. Furthermore, several of the measurable activities on social media are believed to be influenced by each of the five factor model, negatively or positively. Some of them indicate which personalities use Facebook under certain conditions (Sigala & Christou, 2006; Carpenter et al., 2011; Correa, Hinsley & de Zúñiga, 2010; Moore & McElroy, 2012; Ryan & Xenos, 2011; Błachnio, Przepiorka & Rudnicka, 2016; J.- E. R. Lee, Moore, Park & Park, 2012; Orosz, Tóth-Király & Bőthe, 2015; Skues, Williams & Wise, 2012; Wilson, Fornasier & White, 2010; Bachrach, Kosinski, Graepel, Kohli & Stillwell, 2012; Čukić & Bates, 2014; Chatzigeorgiou & Christou, 2019, 2020; Del Chiappa et al., 2021; Fotiadis, 2018; Fotiadis & Williams, 2018). Together with the Personality trait models, behavioral models are used in order to analyze users’ behavior. TBP is a theory that explains individuals’ behavior. Basic concept of TBP is the fact that every individual has the intention of particular behaviors. These behaviors are determined by attitude (ATT), subjective norms (SN) and perceived behavioral control (PBC) (Şahin, Karadağ, & Tuncer, 2019). Attitude is defined as a person’s demeanor towards a behavior that shapes the individual’s behavioral intention and actual behavior. Ajzen (1985) proposed that attitude is the main factor that develops the intention of using something. Thus, we propose the following hypotheses, regarding the association between ATT and diverse Big Five's personality traits as well as between ATT and TPB concepts: MODELING FACEBOOK USERS’ BEHAVIOR TOWARDS THE USE OF PAGES RELATED TO HEALTHY DIET 51 Hypothesis #1: There is a positive correlation between Openness and Attitude towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #2: There is a positive correlation between Extraversion and Attitude towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #3: There is a positive correlation between Agreeableness and Attitude towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #4: There is a positive correlation between Attitude and Intention towards the use of Facebook pages related to healthy diet and sport activities. Subjective Norms refer to individuals’ awareness of social influence from their narrow social circle to follow or not to follow a certain behavior (Ajzen, 1991). Ajzen’s assumption was that individuals tend to behave in such a way as to be accepted by their important referents. Godin and Kok (1996) and Hagger, Chatzisarantis, and Biddle (2002) studied SN in relation to ATT and PBC, finding that SN is a weaker predictor for the Intention, with respect to ATT and PBC. Lo, McKercher, Lo, Cheung, and Law (2011) found that SN, regarding Facebook users in specific, positively influence the intention to perform a certain behavior. Bozionelos and Bennett (1999); Devaraj, Easley, and Crant (2008); Hampson, Andrews, and Barckley (2007); Christou (2010); Marino et al. (2016) analyze and correlate the TPB or the use of social media with users’ personalities as predictors for exercise. In these studies, research indicates that extraversion and agreeableness are two of the traits that are positively associated with subjective norms, something that enforces our hypotheses 5 and 6. Thus, we hypothesize the following positive relationship between SN and Big Five's personality traits as well as between SN and TPB components: Hypothesis #5: There is a positive correlation between Extraversion and Subjective Norms towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #6: There is a positive correlation between Agreeableness and Subjective Norms towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #7: There is a positive correlation between Subjective Norms and Intention towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #8: There is a positive correlation between Subjective Norms and Attitude towards the use of Facebook pages related to healthy diet and sport activities. PBC refers to the amount of the control individuals possess on specific behaviors. In other words, PBC explains how easy or difficult it is for a person to perform a certain behavior. Individuals without control over behavioral intentions tend not to perform the behavior, eventually (Trafimow et al., 2002; Mugobi & Mlozi, 2021; Nechoud et al., 2021). Rosen and Kluemper (2008) associate positively, extraversion and conscientiousness with PBC and negatively neuroticism and PBC. In specific, neuroticism represents a rather doubledfeatured trait, since some authors associate neuroticism positively with Facebook usage (Correa et al., 2010; Mehdizadeh, 2010) but others, negatively (Amichai- Hamburger & Vinitzky, 2010). Furthermore, Al-Debei, Al- Lozi, and Papazafeiropoulou (2013) assert that experienced Facebook users tend to use the platform more frequently due to their abilities and that they will continue participating on Facebook, leading to a higher intention to continue carrying out the behaviors. Next we present the hypotheses formulated regarding PBC and Big Five and TPB: Hypothesis #9: There is a positive correlation between Conscientiousness and Perceived Behavioral Control towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #10: There is a positive correlation between Extraversion and Perceived Behavioral Control towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #11: There is a negative correlation between Neuroticism and Perceived Behavioral Control towards the use of Facebook pages related to healthy diet and sport activities. Hypothesis #12: There is a positive correlation between Perceived Behavioral Control and Intention towards the use of Facebook pages related to healthy diet and sport activities. Table 1: The conceptual definitions of the research Despite the plethora of articles for various alternations of usages and associations, there is a lack of research in literature regarding Big Five and Facebook usage related to healthy eating or sports tips that are suggested by various pages/ group. Furthermore, there is no research, to the best of our knowledge that combines Big Five, Facebook, eHealth and the TPB together in one unified model. The originality of this article consists of correlating TPB and personality traits on the field of eHealth, in specific healthy eating or sports tips that are suggested by various pages/ group on Facebook. The unified proposed model is tested statistically with Structural Equation Modeling (SEM), using AMOS SPSS 18. Table (1) summarizes the conceptual description for each research variable used in the research with the relevant literature. Depending on each hypothesis formulated, these variables are divided in dependent or independent. Figure 1 shows the conceptual model, based on the literature review and the association among the diverse components of the model, indexed with the relevant hypothesis. 52 Nikolaos Misirlis, Marjon Elshof & Maro Vlachopoulou Figure 1: Initial Conceptual Model 3 METHODOLOGY AND RESULTS 3.1. Operationalization of variables Table 2 (Appendix) indicates the operational definitions of the study instruments. Each variable is measured through diverse items based on a 5-point Likert scale, ranking from 1 (completely disagree) to 5 (completely agree). An extended 24-item Big Five Personality Trait Inventory was used for Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism. After reviewing the relevant literature, a 14-item inventory for TPB was created, customized for providing information about the adoption of healthy eating or sports tips suggested by various pages/ groups on Facebook. 3.2. Data collection and sample characteristics A questionnaire was distributed online on 750 Facebook users, via e-mail, Facebook and LinkedIn posts and personal messages. Answers were collected for a 6-month period, specifically from May 2018 to November 2018. We conducted a pilot collection of answers (n=40) in order to identify any misunderstandings and lacks of clarity and accuracy. 578 users responded the questionnaire completely, obtaining a response rate equal to 77%. Table 3 presents the demographic profile of the participants. Sex is equally distributed. The vast majority belongs to the 18-24 age range (76.8%), making the research even more targeted of specific age groups. Considering that the range 18-27 occupies the 87.7% of the total sample, we can extract hyper focused outcomes regarding the aforementioned age decade. An ample majority of the respondents are undergraduate students (82%), living in big cities and urban areas (82.7%). Table 3: Demographic characteristics of the participants It is also interesting to present the most used social media platforms (Table 4). The top three social media in the list, with a significant difference from the fourth, are: Facebook, where the overwhelming 99.3% has an active account, Instagram with 72% active users from our sample and Twitter with 41.2% of the participants responding to possess an active account. An important finding is that even in times of great economic crisis in the country where the research was conducted; only 3.3% of respondents have an account on the largest professional social medium, to wit, LinkedIn, something logical, taken into consideration the respondents’ age. Table 4 presents the percentage of users with active accounts, among our sample. Table 4: Social media platforms with active accounts 3.3. Data analysis and results The proposed model with the hypotheses was tested with Structural Equation Modeling (SEM), using maximum likelihood estimation. The techniques SEM uses, examine the covariance structure and the relationships between and among latent variables, including the effects of direct, indirect, reciprocal and misleading causal relationships. A main difference between SEM and other similar models is that SEM assumes that variables cannot be measured with precision, so it includes an error on its measurements. Two models are created in SEM, the measurement and the structural. The first one represents the construction on the measured variables, while the structural model presents the various associations between constructs. The measurement model proves that the latent variables measured the intended constructs. This technique is known as the Confirmatory Factor Analysis (CFA). CFA verifies the factor structure of the set of our observed variables. With CFA researchers can assume that a relationship between observed variables and their underlying latent constructs exists. After the CFA technique application, we confirmed that all constructs meet the measurement standards. This confirmation leads to the second test of the structural model in order to investigate the relationships among the theoretical constructs, thus confirming or not, the hypotheses model. Final purpose in order to confirm the theoretical model is an insignificant difference between measurement and structural model. SPPS 21 and SPSS AMOS 21 were used in this study to determine measurement and structural models. 3.4. Measurement model The constructs are represented by the measured variables of our model. In order to test the measurement model, CFA was used. The model used 38 items that describe the 10 latent constructs, analytically: Openness (O), Conscientiousness MODELING FACEBOOK USERS’ BEHAVIOR TOWARDS THE USE OF PAGES RELATED TO HEALTHY DIET 53 (C), Extraversion (E), Agreeableness (A), Neuroticism (N), Attitude (ATT), Subjective Norms (SN), Perceived Behavioral Control (PBC), Intention to use (IN) and Behavior (B). Su and Chan (2017) recommend a list of fit indices from different classes, such as absolute fit, incremental fit and comparative fit. The following combination of fit measures was used: x2/d.f., non-norm fit index (NNFI), the root mean square error of approximation (RMSEA), the adjusted goodness of fit index (AGFI), the goodness of fit index (GFI), the comparative fit index (CFI) and the root mean square residual (RMR). For every index a threshold is given, together with the model's data, to set the goodness of fit. As it is shown on Table 5, the proposed model fits well with the data collected. As a result, reliability and validity (convergent and discriminant) could be calculated and evaluated. Table 5: The model's fit indices The internal consistency was estimated through the Construct Reliability (CR) on Table 6, together with the Average Variance Extracted (AVE). CR is greater than 0.7, or close to 0.7 in only two cases, rendering all variables acceptable, and indicating also high internal consistency. Table 6: Construct Reliability (CR) and Average Variance Extracted (AVE) Table 7: Discriminant Validity AVE's lowest value is 0.49 for Agreeableness and PBC, all values though are greater than the squared correlation estimates on Table 7 (see the values below the diagonal). As a result, the test does not show problems with the discriminant validity and no new path was included in order to improve the fit of the model, therefore, next paragraph analyzes the results of our structural model. 3.5. Structural model The next step consists of estimating the structural model in order to empirically measure the relationships among variables and constructs. The structural model presented similar estimates to the measurement model, suggesting an overall good model fit. Table 8 presents the coefficients for each hypothesis and the t-value calculation based on Bonsón and Flores (2011), posing the t-critical cutoff on 1.69. By these measurements, 4 hypotheses are not significant (H1, H2, H6, H11). In conclusion, given that 8 out of 12 estimates are consistent with the initial hypotheses, the results support the theoretical model, therefore, our SEM model (Figure 2), explains the data equally well with the CFA model. Analytically, the first hypothesis that is not confirmed is H1 (H1; β = -0.04; t = -0.96; p > 0.05) , meaning that it is not confirmed the fact that there is a positive correlation between Openness and Attitude towards the use of Facebook pages related to healthy diet and sport activities, on our model and on our sample. Non confirmation we obtain also from the H2, H6 and H11 hypotheses, related to Extraversions and Attitude (H2; β = -0.02; t = -0.48; p > 0.05), Agreeableness and Subjective Norm (H6; β = 0.02; t = 0.48; p > 0.05), and Neuroticism and Perceived Behavioral Control (H11; β = - 0.02; t = -0.48; p > 0.05), respectively. On the other side 8 hypotheses of our initial model are confirmed. Immediate positive correlations from the Big Five to TPB factors were expected such as the Conscientiousness to PBC (H9; β = 0.25; t = 6.23; p < 0.05), the Extraversion to Subjective Norm and PBC (H5; β = 0.19; t = 4.67; p < 0.05, H10; β = 0.10; t = 2.42; p < 0.05) and the Agreeableness to Attitude (H3; β = 0.33; t = 8.43; p < 0.01). Furthermore, Attitude and Subjective norm were found to have a strong positive impact on intention to behavior (H4; β = 0.44; t = 11.8; p < 0.01, H7; β = 0.11; t = 2.67; p < 0.05), supporting our hypotheses. PBC was found to have positive impact on intention to behavior (H12; β = 0.19; t = 4.67; p < 0.05). Last, but not least, a very strong positive impact is noted on Subjective norm to Attitude (H8; β = 0.57; t = 16.73; p < 0.01). This last correlation is the only one between internal factors of the same model, in this case TPB. Table 8: Hypotheses' paths, coefficients and t-values 54 Nikolaos Misirlis, Marjon Elshof & Maro Vlachopoulou Figure 2: SEM results for the proposed model 4 THEORETICAL AND MANAGERIAL IMPLICATIONS The results of the proposed model suggest an extended theory of planned behavior in combination with personality traits, on eHealth field. Two thirds (8 in 12) of the hypotheses are verified, in the proposed model. Openness and Extraversion do not seem to have positive effect on Attitude. On the other side, users’ attitude can be affected positively from Agreeableness and Subjective Norms, guiding to finally positive affection of users’ actual behavior. Agreeableness cannot influence behavior, directly, nor through SN, since the hypothesis path A->SN is not verified, but it can through Attitude. Neuroticism was negatively correlated to PBC but this hypothesis was not confirmed in the proposed model, either. In general, the personality traits of Big Five are not considered individually but in combination with one another. So it's not very important that one trait does not affect a construct of the TPB, because there will be another one that does it. For example, individuals with elevate Extraversion and Agreeableness will affect their Attitude, if not directly from the extraversion, then agreeableness. From the proposed model it derives that Intention to behavior, and therefore behavior, are affected directly by ATT and PBC. Even if the study found that SN influence less the intention to use Facebook groups and pages related to healthy diet and sport activities, this can be done through ATT, considering the path SN->ATT->IN. Furthermore, conscientiousness via PBC and agreeableness via ATT can affect more the intention to use, and therefore the final behavior of usage. TPB literature can be contributed strongly to the verification of the hypotheses of our mode, as well as social science. Regarding the negative coefficients of our hypotheses (O->ATT, E->ATT, A->SN and N->PBC) even if they are not verified, useful outcomes can be extracted. Neurotic users for example tend to use a lot the social media platforms. Even though, theory implies that neurotics are quick and nervous. The other three hypotheses that were not verified, present a unique characteristic. All three hold a value of p, slightly greater than 0.5. We cannot accept them, but the theory implies that if such hypotheses run again in another environment or time, outcomes might be different. The conclusions of the present study can contribute to practitioners by helping them assess their marketing decisions based on the knowledge of which combination of personalities and planned behavior influence more the use of social media pages and groups related to healthy diet and sport activities. Companies involved in such activities can modify their strategies in order to communicate their products to a wider audience, maximizing awareness and engagement. Furthermore, international brands can take into consideration that the Greek market presents particularities that maybe other regions do not, altering, thus, their planning and approaches. 4.1. Limitations and future implications While literature confirms all of our hypotheses, in our study only 8 in 12 are finally confirmed. The difference between the present model and literature findings can be located on the different cultural dimensions among the different studies. The present survey is focused on the Greek region with all the participants being young Greek students. A repetitive research in other countries and/ or different age groups, taking into consideration the cultural differences and dimensions can lead to interesting outcomes. This way, the location-based limitation could be surpassed. The same implies for applying this research in other than eHealth areas. Furthermore, considering a wider sample from the same region could add to the research with a more depictive view of the social media services penetration. 5 NOTE Parts of the present article, especially the statistical analysis and the theoretical framework, make part of the first author’s doctoral dissertation and therefore can be found on first author’s affiliation’s repository (University of Macedonia, Thessaloniki, Greece) for doctoral dissertations. REFERENCES Ahmad, M. H., Shahar, S., Teng, N. I. M. F., Manaf, Z. A., Sakian, N. I. M., & Omar, B. (2014). Applying theory of planned behavior to predict exercise maintenance in sarcopenic elderly. Clinical interventions in aging, 9, 1551. Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. In J. Kuhl & J. Beckmann (Eds.), Action Control: From Cognition to Behavior (pp. 11-39). Berlin, Heidelberg: Springer Berlin Heidelberg. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. Ajzen, I. (2002). Perceived behavioral control, Self‐Efficacy, locus of control, and the theory of planned Behavior1. Journal of applied social psychology, 32(4), 665-683. Al-Debei, M. M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54. doi: http://dx.doi.org/10.1016/j.dss.2012.12.032 Amichai-Hamburger, Y., & Vinitzky, G. (2010). Social network use and personality. Computers in Human Behavior, 26(6), 1289- 1295. doi: http://dx.doi.org/10.1016/j.chb.2010.03.018 Amjad, N., & Wood, A. M. (2009). Identifying and changing the normative beliefs about aggression which lead young Muslim MODELING FACEBOOK USERS’ BEHAVIOR TOWARDS THE USE OF PAGES RELATED TO HEALTHY DIET 55 adults to join extremist anti-Semitic groups in Pakistan. Aggressive behavior, 35(6), 514-519. Armitage, C. J. (2005). Can the theory of planned behavior predict the maintenance of physical activity? Health psychology, 24(3), 235. Augustine, A. A., & Hemenover, S. H. (2008). Extraversion and the consequences of social interaction on affect repair. Personality and Individual Differences, 44(5), 1151-1161. doi: http://dx.doi.org/10.1016/j.paid.2007.11.009 Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012). Personality and patterns of Facebook usage. Paper presented at the Proceedings of the 4th Annual ACM Web Science Conference, Evanston, Illinois. Błachnio, A., Przepiorka, A., & Rudnicka, P. (2016). Narcissism and self-esteem as predictors of dimensions of Facebook use. Personality and Individual Differences, 90, 296-301. Bonsón, E., & Flores, F. (2011). Social media and corporate dialogue: the response of global financial institutions. Online Information Review, 35(1), 34-49. doi: doi:10.1108/14684521111113579 Bozionelos, G., & Bennett, P. (1999). The theory of planned behaviour as predictor of exercise: The moderating influence of beliefs and personality variables. Journal of health psychology, 4(4), 517-529. Branley, D. B., & Covey, J. (2018). Risky behavior via social media: The role of reasoned and social reactive pathways. Computers in Human Behavior, 78(Supplement C), 183-191. doi: https://doi.org/10.1016/j.chb.2017.09.036 Carpenter, J. M., Green, M. C., & LaFlam, J. (2011). People or profiles: Individual differences in online social networking use. Personality and Individual Differences, 50(5), 538-541. doi: http://dx.doi.org/10.1016/j.paid.2010.11.006 Chang, C.-W., & Chen, G. M. (2014). College students’ disclosure of location-related information on Facebook. Computers in Human Behavior, 35(0), 33-38. doi: http://dx.doi.org/10.1016/j.chb.2014.02.028 Chatzigeorgiou, C., & Christou, E. (2019). Social media in tourism marketing: Travellers’ attitudes and encounters. Published in: TOURMAN 2019 Conference Proceedings, Thessaloniki: Greece (27 October 2019): pp. 164-173. Choi, D.-H., & Shin, D.-H. (2017). Exploring political compromise in the new media environment: The interaction effects of social media use and the Big Five personality traits. Personality and Individual Differences, 106(Supplement C), 163-171. doi: https://doi.org/10.1016/j.paid.2016.11.022 Choi, J., & Kim, Y. (2014). The moderating effects of gender and number of friends on the relationship between self-presentation and brand-related word-of-mouth on Facebook. Personality and Individual Differences, 68(0), 1-5. doi: http://dx.doi.org/10.1016/j.paid.2014.03.040 Christou, E.. & Nella, A. (2016). Web 2.0 and networks in wine tourism: The case studies of greatwinecapitals.com and wineandhospitalitynetwork.com. In Social Media in Travel, Tourism and Hospitality: Theory, Practice and Cases. M. Sigala, E. Christou and Gretzel, U. (Eds). Surrey, UK: Ashgate Publishing, pp.11-24. Chu, T.-H., & Chen, Y.-Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92, 37-52. Correa, T., Hinsley, A. W., & de Zúñiga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247-253. doi: http://dx.doi.org/10.1016/j.chb.2009.09.003 Čukić, I., & Bates, T. C. (2014). Openness to experience and aesthetic chills: Links to heart rate sympathetic activity. Personality and Individual Differences, 64(0), 152-156. Daskalaki, V. V., Voutsa, M. C., Boutsouki, C. and Hatzithomas, L. Service quality, visitor satisfaction and future behavior in the museum sector. Journal of Tourism, Heritage & Services Marketing (JTHSM), 6, 1 2020), 3-8. Del Chiappa, G., Bregoli, I., & Fotiadis, A. (2021). The impact of COVID-19 on the Italian accommodation sector and related response actions: A supply-perspective using a mixed method approach. Journal of Tourism, Heritage & Services Marketing, 7(1), 13-22 Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note— how does personality matter? Relating the five-factor model to technology acceptance and use. Information systems research, 19(1), 93-105. Fotiadis, A. (2018). Modelling wedding marketing strategies: An fsQCA Analysis. Journal of Tourism, Heritage & Services Marketing, 4(1), 23-26. Fotiadis, A., & Williams, R. (2018). “TiCoSa” a 3d matrix conceptual model to investigate visitors’ perceptions in an athletic event. Journal of Tourism, Heritage & Services Marketing, 4(2), 32-36. Garcia, D., & Sikström, S. (2014). The dark side of Facebook: Semantic representations of status updates predict the Dark Triad of personality. Personality and Individual Differences, 67(0), 92-96. doi: http://dx.doi.org/10.1016/j.paid.2013.10.001 Godin, G., & Kok, G. (1996). The theory of planned behavior: a review of its applications to health-related behaviors. American journal of health promotion, 11(2), 87-98. Gosling, D., Augustine, A., Vazire, S., Holtzman, N., & Gaddis, S. (2011). Manifestation of personality in online social networks: Self-reported facebook-related behaviors and observable profile information. Cyberpsychology, behavior and social networking., 14, 483-488. Gretzel, U., Sigala, M., & Christou, E. (2012). Social Media Change the Name of the Game in the Tourism and Hospitality Industries, The European Financial Review, 20 October, available at: http://www.europeanfinancialreview.com/?p=1340. Hagger, M. S., Chatzisarantis, N. L., & Biddle, S. J. (2002). The influence of autonomous and controlling motives on physical activity intentions within the Theory of Planned Behaviour. British journal of health psychology, 7(3), 283-297. Hajli, N., Shanmugam, M., Powell, P., & Love, P. E. (2015). A study on the continuance participation in on-line communities with social commerce perspective. Technological Forecasting and Social Change, 96, 232-241. Hall, J. A., & Pennington, N. (2013). Self-monitoring, honesty, and cue use on Facebook: The relationship with user extraversion and conscientiousness. Computers in Human Behavior, 29(4), 1556-1564. doi: http://dx.doi.org/10.1016/j.chb.2013.01.001 Hampson, S. E., Andrews, J. A., & Barckley, M. (2007). Predictors of the development of elementary-school children's intentions to smoke cigarettes: Hostility, prototypes, and subjective norms. Nicotine & Tobacco Research, 9(7), 751-760. John, Naumann, L., & Soto, C. (2008). Paradigm Shift to the Integrative Big Five Trait Taxonomy: History, Measurement, and Conceptual Issues. In O. John, R. Robbins & L. Pervin (Eds.), Handbook of Personality: Theory and Research (pp. 114-156): Guilford. Jspeert, R. and Hernandez-Maskivker, G. Active sport tourists: Millennials vs baby boomers. Journal of Tourism, Heritage & Services Marketing (JTHSM), 6, 2 2020), 12-20. Krakover, S., & Corsale, A. (2021). Sieving tourism destinations: Decision-making processes and destination choice implications. Journal of Tourism, Heritage & Services Marketing, 7(1), 33–43. Lee, J.-E. R., Moore, D. C., Park, E.-A., & Park, S. G. (2012). Who wants to be “friend-rich”? Social compensatory friending on Facebook and the moderating role of public self-consciousness. 56 Nikolaos Misirlis, Marjon Elshof & Maro Vlachopoulou Computers in Human Behavior, 28(3), 1036-1043. doi: http://dx.doi.org/10.1016/j.chb.2012.01.006 Lee, S.-Y., Hansen, S. S., & Lee, J. K. (2016). What makes us click “like” on Facebook? Examining psychological, technological, and motivational factors on virtual endorsement. Computer Communications, 73, Part B, 332-341. doi: https://doi.org/10.1016/j.comcom.2015.08.002 Liu, D., & Campbell, W. K. (2017). The Big Five personality traits, Big Two metatraits and social media: A meta-analysis. Journal of Research in Personality, 70(Supplement C), 229-240. doi: https://doi.org/10.1016/j.jrp.2017.08.004 Lo, I. S., McKercher, B., Lo, A., Cheung, C., & Law, R. (2011). Tourism and online photography. Tourism Management, 32(4), 725-731. doi: http://dx.doi.org/10.1016/j.tourman.2010.06.001 Marengo, D., Poletti, I., & Settanni, M. (2020). The interplay between neuroticism, extraversion, and social media addiction in young adult Facebook users: Testing the mediating role of online activity using objective data. Addictive Behaviors, 102, 106150. doi: https://doi.org/10.1016/j.addbeh.2019.106150 Marino, C., Vieno, A., Pastore, M., Albery, I. P., Frings, D., & Spada, M. M. (2016). Modeling the contribution of personality, social identity and social norms to problematic Facebook use in adolescents. Addictive behaviors, 63, 51-56. McCrae, R. R., & John, O. P. (1992). An Introduction to the Five- Factor Model and Its Applications. Journal of Personality, 60(2), 175-215. doi: 10.1111/j.1467-6494.1992.tb00970.x Mehdizadeh, S. (2010). Self-presentation 2.0: Narcissism and selfesteem on Facebook. Cyberpsychology, Behavior, and Social Networking, 13(4), 357-364. Misirlis, N., Lekakos, G. and Vlachopoulou, M. Associating Facebook measurable activities with personality traits: A fuzzy sets approach. Journal of Tourism, Heritage & Services Marketing, 4, 2 2018), 10-16. Moore, K., & McElroy, J. C. (2012). The influence of personality on Facebook usage, wall postings, and regret. Computers in Human Behavior, 28(1), 267-274. doi: http://dx.doi.org/10.1016/j.chb.2011.09.009 Mugobi, T., & Mlozi, S. (2021). The impact of external factors on ICT usage practices at UNESCO World Heritage Sites. Journal of Tourism, Heritage & Services Marketing, 7(1), 3–12. Nadkarni, A., & Hofmann, S. G. (2012). Why do people use Facebook? Personality and Individual Differences, 52(3), 243- 249. doi: http://dx.doi.org/10.1016/j.paid.2011.11.007 Nechoud, L., Ghidouche, F., & Seraphin, H. (2021). The influence of eWOM credibility on visit intention: An integrative moderated mediation model. Journal of Tourism, Heritage & Services Marketing, 7(1), 54–63. Önder, I., Gunter, U., & Gindl, S. (2020). Utilizing Facebook statistics in tourism demand modeling and destination marketing. Journal of Travel Research, 59(2), 195-208. Orosz, G., Tóth-Király, I., & Bőthe, B. (2015). Four facets of Facebook intensity—The development of the Multidimensional Facebook Intensity Scale. Personality and Individual Differences. Ozkan, S., & Kanat, I. E. (2011). e-Government adoption model based on theory of planned behavior: Empirical validation. Government Information Quarterly, 28(4), 503-513. doi: https://doi.org/10.1016/j.giq.2010.10.007. Pi, S.-M., Chou, C.-H., & Liao, H.-L. (2013). A study of Facebook Groups members’ knowledge sharing. Computers in Human Behavior, 29(5), 1971-1979. doi: http://dx.doi.org/10.1016/j.chb.2013.04.019. Rosen, P. A., & Kluemper, D. H. (2008). The impact of the big five personality traits on the acceptance of social networking website. AMCIS 2008 proceedings, 274. Ryan, T., & Xenos, S. (2011). Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage. Computers in Human Behavior, 27(5), 1658-1664. doi: http://dx.doi.org/10.1016/j.chb.2011.02.004 Şahin, F., Karadağ, H., & Tuncer, B. (2019). Big five personality traits, entrepreneurial self-efficacy and entrepreneurial intention. International Journal of Entrepreneurial Behavior & Research. Samy, H. (2016). Exploring Factors that Influence Domestic Tourists' Satisfaction with Budget Hotel Services in Egypt. Journal of Tourism, Heritage & Services Marketing, 2(2), 17– 22. http://doi.org/10.5281/zenodo.376344. Sedghi, A. (2014). Facebook: 10 years of social networking, in numbers. The Guardian. Retrieved from http://www.theguardian.com/news/datablog/2014/feb/04/face book-in-numbers-statistics. Seidman, G. (2013). Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Personality and Individual Differences, 54(3), 402-407. doi: http://dx.doi.org/10.1016/j.paid.2012.10.009 Seidman, G. (2014). Expressing the “True Self” on Facebook. Computers in Human Behavior, 31(0), 367-372. doi: http://dx.doi.org/10.1016/j.chb.2013.10.052 Sigala, M., & Christou, E. (2002). The transformative power of ecommerce on hotel brands. Proceedings of the Annual European Council for Hotel, Restaurant and Institutional Education (Euro-CHRIE) Conference, Cross-cultural Challenges in the Tourism Industry, Barcelona, Spain. Skues, J. L., Williams, B., & Wise, L. (2012). The effects of personality traits, self-esteem, loneliness, and narcissism on Facebook use among university students. Computers in Human Behavior, 28(6), 2414-2419. doi: http://dx.doi.org/10.1016/j.chb.2012.07.012 Smith, M. M., Saklofske, D. H., & Nordstokke, D. W. (2014). The link between neuroticism and perfectionistic concerns: The mediating effect of trait emotional intelligence. Personality and Individual Differences, 61–62(0), 97-100. doi: http://dx.doi.org/10.1016/j.paid.2013.12.013 Su, C. C., & Chan, N. K. (2017). Predicting social capital on Facebook: The implications of use intensity, perceived content desirability, and Facebook-enabled communication practices. Computers in Human Behavior, 72, 259-268. doi: http://dx.doi.org/10.1016/j.chb.2017.02.058 Tan, W., Yang, C. (2012). Personality Traits Predictors of Usage of Internet Services. Paper presented at the International Conference on Economics, Business Innovation, Singapore. Trafimow, D., Sheeran, P., Conner, M., & Finlay, K. A. (2002). Evidence that perceived behavioural control is a multidimensional construct: Perceived control and perceived difficulty. British Journal of Social Psychology, 41(1), 101- 121. Wilson, K., Fornasier, S., & White, K. M. (2010). Psychological predictors of young adults' use of social networking sites. Cyberpsychology, Behavior, and Social Networking, 13(2), 173-177. Zywica, J., & Danowski, J. (2008). The Faces of Facebookers: Investigating Social Enhancement and Social Compensation Hypotheses; Predicting Facebook™ and Offline Popularity from Sociability and Self-Esteem, and Mapping the Meanings of Popularity with Semantic Networks. Journal of Computer- Mediated Communication, 14(1), 1-34. doi: 10.1111/j.1083- 6101.2008.01429.