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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">Oalib</journal-id>
      <journal-title-group>
        <journal-title>Open Access Library Journal</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2333-9721</issn>
      <issn pub-type="ppub">2333-9705</issn>
      <publisher>
        <publisher-name>Scientific Research Publishing</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.4236/oalib.1115855</article-id>
      <article-id pub-id-type="publisher-id">Oalib-154960</article-id>
      <article-categories>
        <subj-group>
          <subject>Article</subject>
        </subj-group>
        <subj-group>
          <subject>Biomedical</subject>
          <subject>Life Sciences</subject>
          <subject>Business</subject>
          <subject>Economics</subject>
          <subject>Chemistry</subject>
          <subject>Materials Science</subject>
          <subject>Computer Science</subject>
          <subject>Communications</subject>
          <subject>Earth</subject>
          <subject>Environmental Sciences</subject>
          <subject>Engineering</subject>
          <subject>Medicine</subject>
          <subject>Healthcare</subject>
          <subject>Physics</subject>
          <subject>Mathematics</subject>
          <subject>Social Sciences</subject>
          <subject>Humanities</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Building Brand Trust through Food Bloggers in Egypt: The Mediating Roles of Parasocial Relationships and Source Credibility</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0006-7022-4933</contrib-id>
          <name name-style="western">
            <surname>Ramadan</surname>
            <given-names>Manar Mohammed</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1"><label>1</label> School of Business, Jiangnan University, Wuxi, China </aff>
      <author-notes>
        <fn fn-type="conflict" id="fn-conflict">
          <p>The author declares no conflicts of interest.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="epub">
        <day>08</day>
        <month>10</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="collection">
        <month>10</month>
        <year>2026</year>
      </pub-date>
      <volume>13</volume>
      <issue>10</issue>
      <fpage>1</fpage>
      <lpage>25</lpage>
      <history>
        <date date-type="received">
          <day>06</day>
          <month>08</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>26</day>
          <month>10</month>
          <year>2026</year>
        </date>
        <date date-type="published">
          <day>29</day>
          <month>10</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>© 2026 by the authors and Scientific Research Publishing Inc.</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access">
          <license-p> This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link> ). </license-p>
        </license>
      </permissions>
      <self-uri content-type="doi" xlink:href="https://doi.org/10.4236/oalib.1115855">https://doi.org/10.4236/oalib.1115855</self-uri>
      <abstract>
        <p>This study examines how food bloggers’ <italic>interaction</italic> strategies—interactivity (INT) and content quality (CQ)—influence consumer brand trust in the Egyptian fast-food context, via parasocial relationship (PSR) and source credibility (CR). Survey data from 333 consumers were analyzed via SmartPLS 4. Results show that INT enhances both PSR and CR, while CQ only affects CR, not PSR. Both PSR and CR predict brand trust and serve as mediators: PSR mediates the link between INT and brand trust, and CR mediates the link between CQ and brand trust. Serial mediation is partially supported: the cognitive-to-emotional path is significant, while the reverse path is not. This research enriches influencer marketing literature by separating affective and cognitive pathways, and offers insights for Egyptian food bloggers and fast-food brands.</p>
      </abstract>
      <kwd-group kwd-group-type="author-generated" xml:lang="en">
        <kwd>Influencer Marketing</kwd>
        <kwd>Interaction Strategies</kwd>
        <kwd>Parasocial Relationship</kwd>
        <kwd>Source Credibility</kwd>
        <kwd>Brand Trust</kwd>
        <kwd>Food Bloggers</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>1. Introduction</title>
      <p>Egypt has undergone profound digital transformation over the past decade. By January 2021, national internet penetration hit 57.3%, with social media users reaching 49 million, a year-on-year increase of 7 million [<xref ref-type="bibr" rid="B1">1</xref>]. Young citizens, accounting for over one-third of Egypt’s 100 million population, constitute the core audience for social media content [<xref ref-type="bibr" rid="B2">2</xref>]. Within this digital ecosystem, social media influencers have emerged as authoritative opinion leaders. Unlike traditional celebrities, they are ordinary internet creators who accumulate followers by sharing daily experiences and professional viewpoints [<xref ref-type="bibr" rid="B2">2</xref>][<xref ref-type="bibr" rid="B3">3</xref>].</p>
      <p>In Egypt’s foodservice industry, food bloggers dominate consumer dining decision-making, and their recommendations gain higher consumer trust than traditional offline advertising [<xref ref-type="bibr" rid="B4">4</xref>]-[<xref ref-type="bibr" rid="B6">6</xref>]. Two core theoretical frameworks explain such persuasive effects: parasocial relationship (PSR; [<xref ref-type="bibr" rid="B7">7</xref>]), referring to one-sided emotional bonds between audiences and media figures; and source credibility (CR; [<xref ref-type="bibr" rid="B8">8</xref>]), which interprets persuasion based on perceived expertise, trustworthiness and attractiveness. Although global influencer research has widely adopted both theories [<xref ref-type="bibr" rid="B9">9</xref>]-[<xref ref-type="bibr" rid="B11">11</xref>], few Egyptian restaurant studies integrate the two mechanisms into one unified analytical framework.</p>
      <p>Three prominent research gaps remain in local and international literature. First, most Egyptian influencer studies solely rely on source credibility theory and neglect the affective parasocial relationship mechanism, despite its wide global application in influencer contexts [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B4">4</xref>][<xref ref-type="bibr" rid="B12">12</xref>]. Second, existing scholarship mostly explores fixed personal traits of influencers (attractiveness, inherent expertise), while overlooking adjustable interaction strategies—interactivity (INT) and content quality (CQ)—which are more operable for bloggers and brands [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B12">12</xref>][<xref ref-type="bibr" rid="B13">13</xref>]. Third, prior research prioritizes short-term outcomes such as purchase intention and brand love, yet brand trust (TR) acts as the foundational long-term relational asset, especially amid Egypt’s high food inflation and restrained household spending [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B6">6</xref>][<xref ref-type="bibr" rid="B14">14</xref>][<xref ref-type="bibr" rid="B15">15</xref>].</p>
      <p>Against this research backdrop, the current study constructs a PLS-SEM framework to untangle the distinct affective and cognitive pathways through which food bloggers’ interactivity and content quality predict brand trust, with parasocial relationship and source credibility serving as parallel and sequential mediators. Using survey data from 333 Egyptian fast-food consumers who follow food bloggers, this work fills the contextual gap in local foodservice research by uniting two classic persuasion theories within a single analytical model. Its outcomes offer nuanced theoretical extensions and practical operational guidance for Egyptian food content creators and fast-food brands navigating inflationary market conditions.</p>
    </sec>
    <sec id="sec2">
      <title>2. Literature Review and Theoretical Foundation</title>
      <sec id="sec2dot1">
        <title>2.1. Social Media Influencers and Food Bloggers</title>
        <p>Social media influencers (SMIs) are individuals who have built substantial followings by consistently generating and sharing content [<xref ref-type="bibr" rid="B3">3</xref>][<xref ref-type="bibr" rid="B16">16</xref>]. Unlike traditional celebrities, they are perceived as more relatable, trustworthy, and accessible, which enhances their persuasive power [<xref ref-type="bibr" rid="B17">17</xref>]. Within the broader influencer landscape, food bloggers have emerged as a particularly influential category [<xref ref-type="bibr" rid="B4">4</xref>][<xref ref-type="bibr" rid="B6">6</xref>]. In Egypt, food bloggers review restaurants, share cooking tutorials, and document food experiences across platforms, making them essential marketing tools for the foodservice industry. Research shows that nearly half of consumers make purchases influenced by social media posts, with food and drink content having the highest engagement [<xref ref-type="bibr" rid="B18">18</xref>].</p>
      </sec>
      <sec id="sec2dot2">
        <title>2.2. Interaction Strategies</title>
        <p>Interaction strategies refer to the intentional, manageable behaviours that influencers employ to engage audiences and build relationships [<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B20">20</xref>]. Unlike static personal attributes (e.g., attractiveness), these strategies are dynamic and controllable. Drawing on social penetration theory [<xref ref-type="bibr" rid="B21">21</xref>] and social presence theory [<xref ref-type="bibr" rid="B22">22</xref>], this study conceptualises interaction strategies across two dimensions:</p>
        <p>Interactivity refers to bidirectional communication between influencers and followers, including responding to comments, participating in conversations, and demonstrating attentiveness [<xref ref-type="bibr" rid="B23">23</xref>][<xref ref-type="bibr" rid="B24">24</xref>]. Research confirms that interactivity enhances parasocial relationships and source credibility [<xref ref-type="bibr" rid="B9">9</xref>][<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B24">24</xref>].</p>
        <p>Content quality refers to the perceived value, relevance, and usefulness of content [<xref ref-type="bibr" rid="B25">25</xref>][<xref ref-type="bibr" rid="B26">26</xref>], encompassing informativeness, entertainment, originality, and presentation. Research shows that content quality enhances audience engagement, trust, and behavioural intentions [<xref ref-type="bibr" rid="B4">4</xref>][<xref ref-type="bibr" rid="B5">5</xref>][<xref ref-type="bibr" rid="B27">27</xref>].</p>
      </sec>
      <sec id="sec2dot3">
        <title>2.3. Parasocial Relationship (PSR)</title>
        <p>Parasocial relationships (PSR), first proposed by Horton &amp; Wohl [<xref ref-type="bibr" rid="B7">7</xref>], describe the one-sided intimate, trusting emotional attachments audiences build with media personalities, further refined by Rubin [<xref ref-type="bibr" rid="B28">28</xref>]. Social media strengthens such affective bonds via realtime comment exchanges and live streaming [<xref ref-type="bibr" rid="B9">9</xref>][<xref ref-type="bibr" rid="B10">10</xref>]. Followers treat food bloggers as intimate acquaintances and transfer their trust toward influencers to endorsed restaurant brands [<xref ref-type="bibr" rid="B11">11</xref>][<xref ref-type="bibr" rid="B19">19</xref>]. Egyptian survey data also confirms local followers frequently describe food bloggers as friends or family, indicating strong PSR formation [<xref ref-type="bibr" rid="B29">29</xref>]. A body of prior work verifies parasocial relationship acts as a critical mediator linking influencer behaviors to consumer behavioral outcomes [<xref ref-type="bibr" rid="B14">14</xref>][<xref ref-type="bibr" rid="B30">30</xref>][<xref ref-type="bibr" rid="B31">31</xref>].</p>
      </sec>
      <sec id="sec2dot4">
        <title>2.4. Source Credibility</title>
        <p>Source credibility theory, developed by Hovland [<xref ref-type="bibr" rid="B32">32</xref>], posits that the persuasiveness of a communicator depends on perceived credibility. Ohanian [<xref ref-type="bibr" rid="B8">8</xref>] identified three key dimensions: expertise (perceived knowledge and competence), trustworthiness (perceived honesty and sincerity), and attractiveness (perceived physical or social appeal). In influencer marketing, credibility mediates the influence of behaviours on consumer trust [<xref ref-type="bibr" rid="B10">10</xref>][<xref ref-type="bibr" rid="B24">24</xref>][<xref ref-type="bibr" rid="B33">33</xref>]. Egyptian research confirms that credibility dimensions significantly influence brand related outcomes, including brand love, brand respect, and purchase intention [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B12">12</xref>][<xref ref-type="bibr" rid="B13">13</xref>].</p>
      </sec>
      <sec id="sec2dot5">
        <title>2.5. Brand Trust</title>
        <p>Brand trust reflects consumers’ willingness to rely on a brand to fulfil its promised functions [<xref ref-type="bibr" rid="B34">34</xref>][<xref ref-type="bibr" rid="B35">35</xref>]. It is a key determinant of long-term customer relationships, loyalty, and purchase intentions. In influencer marketing, trust transfers from the influencer to the endorsed brand when followers perceive the influencer as credible and trustworthy [<xref ref-type="bibr" rid="B10">10</xref>][<xref ref-type="bibr" rid="B36">36</xref>]. Studies confirm that source credibility positively influences brand trust, with PSR and credibility serving as chain mediators [<xref ref-type="bibr" rid="B19">19</xref>]. In Egypt’s competitive fast food sector, brand trust is particularly important given high food inflation and cautious consumer spending [<xref ref-type="bibr" rid="B6">6</xref>][<xref ref-type="bibr" rid="B15">15</xref>].</p>
      </sec>
    </sec>
    <sec id="sec3">
      <title>3. Research Model and Hypotheses Development</title>
      <sec id="sec3dot1">
        <title>3.1. Research Model</title>
        <p>Based on a comprehensive literature review grounded in parasocial relationship theory [<xref ref-type="bibr" rid="B7">7</xref>] and source credibility theory [<xref ref-type="bibr" rid="B8">8</xref>], this conceptual model illustrates how food bloggers’ interaction strategies shape Egyptian consumers’ brand trust toward endorsed fast-food restaurant brands. The model categorizes bloggers’ core interaction tactics into two exogenous constructs: Interactivity and Content Quality. These two strategies predict Brand Trust via two parallel mediating mechanisms, namely Parasocial Relationship and Source Credibility. In addition to these parallel paths, the model examines a sequential serial mediation pathway where source credibility precedes and enhances parasocial relationship, which then leads to brand trust (CQ → CR → PSR → TR), representing a cognitive-to-emotional processing sequence. As visually presented in <xref ref-type="fig" rid="fig1">Figure 1</xref><xref ref-type="fig" rid="fig1">Figure 1</xref>, this framework integrates both affective emotional bonding and cognitive credibility evaluation routes to explain the brand trust formation process of Egyptian fast-food consumers who follow food bloggers.</p>
        <fig id="fig1">
          <label>Figure 1</label>
          <graphic xlink:href="https://html.scirp.org/file/1115855-rId15.jpeg?20261029025259" />
        </fig>
        <p><bold>Figure 1.</bold>Conceptual framework. Note: INT = Interactivity; CQ = Content Quality; PSR = Parasocial Relationship; CR = Source Credibility; TR = Brand Trust.</p>
        <p>As illustrated in <xref ref-type="fig" rid="fig1">Figure 1</xref><xref ref-type="fig" rid="fig1">Figure 1</xref>, this conceptual model establishes two distinct mediating mechanisms: parallel affective-cognitive pathways and bidirectional sequential serial mediation between parasocial relationship (PSR) and source credibility (CR). The dual-mediation framework is rooted in dual-process theory, which distinguishes emotional attachment and rational source evaluation as separate consumer judgment routes. Detailed theoretical rationales and the two opposing serial mediation chains are elaborated in the subsequent hypothesis development section.</p>
      </sec>
      <sec id="sec3dot2">
        <title>3.2. Hypotheses Development</title>
        <p>This study proposes that food bloggers’ interaction strategies—comprising interactivity and content quality—influence consumer brand trust through the parallel and sequential mediating mechanisms of parasocial relationship and source credibility. Drawing on parasocial relationship theory [<xref ref-type="bibr" rid="B7">7</xref>], source credibility theory [<xref ref-type="bibr" rid="B8">8</xref>], and social penetration theory [<xref ref-type="bibr" rid="B21">21</xref>], the following hypotheses are developed.</p>
        <p>3.2.1. Interactivity and Parasocial Relationship</p>
        <p>Interactivity refers to the bidirectional communication between influencers and followers, including responding to comments and participating in conversations [<xref ref-type="bibr" rid="B23">23</xref>][<xref ref-type="bibr" rid="B24">24</xref>]. Previous research has established that interactivity enhances the emotional bonds audiences form with media figures [<xref ref-type="bibr" rid="B9">9</xref>][<xref ref-type="bibr" rid="B24">24</xref>]. When food bloggers actively engage with their followers, followers perceive them as more accessible and relatable, thereby strengthening their emotional connection [<xref ref-type="bibr" rid="B29">29</xref>][<xref ref-type="bibr" rid="B37">37</xref>]. Therefore, we hypothesize:</p>
        <p>H1: There is a relationship between interactivity and consumers’ parasocial relationship with food bloggers.</p>
        <p>3.2.2. Interactivity and Source Credibility</p>
        <p>Interactivity also plays a crucial role in shaping consumers’ perceptions of source credibility. When influencers respond to followers’ comments and engage in conversations, they signal transparency and expertise, which enhance their perceived trustworthiness [<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B24">24</xref>]. Studies in the Egyptian market have shown that food bloggers who maintain active communication are perceived as more credible sources of information [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B12">12</xref>]. Thus, we propose:</p>
        <p>H2: There is a relationship between interactivity and consumers’ perception of source credibility.</p>
        <p>3.2.3. Content Quality and Parasocial Relationship</p>
        <p>Content quality refers to the perceived value, relevance, and usefulness of the content created by influencers [<xref ref-type="bibr" rid="B25">25</xref>][<xref ref-type="bibr" rid="B26">26</xref>]. Research has shown that high-quality content enhances audience engagement and emotional connection [<xref ref-type="bibr" rid="B4">4</xref>][<xref ref-type="bibr" rid="B27">27</xref>]. When food bloggers provide valuable and engaging content, followers perceive them as more relatable, thereby fostering emotional bonds [<xref ref-type="bibr" rid="B5">5</xref>][<xref ref-type="bibr" rid="B29">29</xref>]. We hypothesize:</p>
        <p>H3: There is a relationship between content quality and consumers’ parasocial relationship with food bloggers.</p>
        <p>3.2.4. Content Quality and Source Credibility </p>
        <p>Content quality is a key determinant of source credibility. When followers perceive an influencer’s content as informative, relevant, and well-presented, they are more likely to view the influencer as credible and trustworthy [<xref ref-type="bibr" rid="B25">25</xref>][<xref ref-type="bibr" rid="B26">26</xref>]. Studies in the Egyptian context confirm that high-quality content enhances perceptions of expertise and trustworthiness [<xref ref-type="bibr" rid="B4">4</xref>][<xref ref-type="bibr" rid="B5">5</xref>]. Thus, we hypothesize:</p>
        <p>H4: There is a relationship between content quality and consumers’ perception of source credibility.</p>
        <p>3.2.5. Parasocial Relationship and Brand Trust</p>
        <p>Parasocial relationships are the one-sided emotional bonds audiences develop with media figures, characterized by feelings of intimacy and trust [<xref ref-type="bibr" rid="B7">7</xref>][<xref ref-type="bibr" rid="B28">28</xref>]. When followers form strong parasocial relationships with influencers, they are more likely to trust the brands endorsed by those influencers [<xref ref-type="bibr" rid="B11">11</xref>][<xref ref-type="bibr" rid="B30">30</xref>]. In the Egyptian context, followers who perceive food bloggers as “friends” are more likely to trust their restaurant recommendations [<xref ref-type="bibr" rid="B29">29</xref>]. Therefore, we propose:</p>
        <p>H5: There is a relationship between consumers’ parasocial relationship with food bloggers and their brand trust towards the recommended restaurants.</p>
        <p>3.2.6. Source Credibility and Brand Trust</p>
        <p>Source credibility theory posits that the persuasiveness of a communicator depends on perceived credibility, encompassing expertise, trustworthiness, and attractiveness [<xref ref-type="bibr" rid="B8">8</xref>]. Research has consistently demonstrated that source credibility positively influences brand trust and purchase intentions [<xref ref-type="bibr" rid="B10">10</xref>][<xref ref-type="bibr" rid="B24">24</xref>]. Egyptian studies confirm that credibility dimensions significantly influence brand-related outcomes [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B12">12</xref>]. Hence, we hypothesize:</p>
        <p>H6: There is a relationship between food bloggers’ source credibility and consumers’ brand trust towards the recommended restaurants.</p>
        <p>3.2.7. Mediating Role of Parasocial Relationship</p>
        <p>Parasocial relationships have been established as a key mechanism through which influencer behaviors influence consumer outcomes [<xref ref-type="bibr" rid="B14">14</xref>][<xref ref-type="bibr" rid="B30">30</xref>][<xref ref-type="bibr" rid="B31">31</xref>]. Specifically, interactivity enhances parasocial relationships, which in turn foster brand trust. When influencers actively engage with their followers, followers develop stronger emotional bonds, leading to increased trust in the endorsed brands [<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B37">37</xref>]. We propose:</p>
        <p>H7: Parasocial relationship mediates the relationship between interactivity and brand trust.</p>
        <p>3.2.8. Mediating Role of Source Credibility</p>
        <p>Source credibility serves as a cognitive mechanism through which content quality influences brand trust. When followers perceive an influencer’s content as high-quality, they view the influencer as more credible, which subsequently enhances their trust in the endorsed brands [<xref ref-type="bibr" rid="B26">26</xref>][<xref ref-type="bibr" rid="B33">33</xref>]. Research confirms that source credibility mediates the relationship between influencer behaviors and consumer trust [<xref ref-type="bibr" rid="B10">10</xref>][<xref ref-type="bibr" rid="B24">24</xref>]. Thus, we hypothesize:</p>
        <p>H8: Source credibility mediates the relationship between content quality and brand trust.</p>
        <p>3.2.9. Serial Mediation (Chain Mediation)</p>
        <p>Beyond parallel mediation, this study examines two possible sequential mediation pathways. The first, H9a, proposes that interactivity fosters parasocial relationships, which in turn enhance source credibility, ultimately leading to brand trust (INT → PSR → CR → TR). The second, H9b, proposes that content quality enhances source credibility, which in turn strengthens parasocial relationships, ultimately leading to brand trust (CQ → CR → PSR → TR), representing a cognitive-to-emotional sequence where rational evaluation precedes emotional bonding [<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B38">38</xref>]. We propose:</p>
        <p>H9a: Parasocial relationship and source credibility serve as sequential mediators (chain mediation) in the relationship between interactivity and brand trust, specifically through the pathway INT → PSR → CR → TR.</p>
        <p>H9b: Parasocial relationship and source credibility serve as sequential mediators (chain mediation) in the relationship between content quality and brand trust, specifically through the pathway CQ → CR → PSR → TR.</p>
      </sec>
    </sec>
    <sec id="sec4">
      <title>4. Research Methodology</title>
      <p>A quantitative cross-sectional design was adopted for this study. All questionnaire items were adapted from validated mature scales: interactivity and content quality from [<xref ref-type="bibr" rid="B23">23</xref>] and [<xref ref-type="bibr" rid="B24">24</xref>]; parasocial relationship from [<xref ref-type="bibr" rid="B9">9</xref>] and [<xref ref-type="bibr" rid="B28">28</xref>]; source credibility from [<xref ref-type="bibr" rid="B8">8</xref>]; brand trust from [<xref ref-type="bibr" rid="B34">34</xref>] and [<xref ref-type="bibr" rid="B35">35</xref>]. All items adopt a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The original English questionnaire underwent translation and back-translation into Arabic to guarantee cross-cultural equivalence. The sources of the questionnaire items are presented in <bold>Table A1</bold> in Appendix A. Survey data were collected via Google Forms targeting Egyptian fast-food consumers who follow food bloggers. A screening question (“Do you regularly follow any food blogger?”) filtered invalid respondents. Convenience sampling was selected, as no public database of Egyptian fast-food consumers exists [<xref ref-type="bibr" rid="B39">39</xref>][<xref ref-type="bibr" rid="B40">40</xref>]. Questionnaires were distributed on Facebook, Instagram, TikTok and WhatsApp over six months (December 2025-June 2026). Data were collected through an online survey. Eligibility was limited to Egyptian consumers who actively follow food bloggers, as confirmed by a screening question at the beginning of the survey. To ensure data quality, we implemented controls for duplicate responses by monitoring IP addresses and timestamps, and incomplete questionnaires were excluded from the final sample. All participants provided informed consent electronically before starting the survey, and they were assured of the anonymity and confidentiality of their responses. This study adhered to ethical research standards; as it involved no sensitive personal data, medical interventions, or vulnerable populations, formal ethical approval was not required, but institutional guidelines were followed. A total of 345 responses were collected; after removing 12 incomplete invalid samples, 333 valid questionnaires remained for analysis. <bold>Table 1</bold> summarizes respondent demographic profiles. Data analysis was executed in SmartPLS 4, including PLS-SEM measurement and structural model evaluation, bootstrapping (5000 resamples) for path and mediation significance testing, and multi-group analysis to explore gender heterogeneity.</p>
      <p><bold>Table 1.</bold> Respondent profile characteristics.</p>
      <table-wrap id="tbl1">
        <label>Table 1</label>
        <table>
          <tbody>
            <tr>
              <td>
                <bold>Variable</bold>
              </td>
              <td>
                <bold>Number</bold>
              </td>
              <td>
                <bold>Percentage (%)</bold>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Gender</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Female</td>
              <td>177</td>
              <td>53.2%</td>
            </tr>
            <tr>
              <td>Male</td>
              <td>156</td>
              <td>46.8%</td>
            </tr>
            <tr>
              <td>
                <bold>Age</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Under 18</td>
              <td>8</td>
              <td>2.4%</td>
            </tr>
            <tr>
              <td>18 - 24</td>
              <td>171</td>
              <td>51.4%</td>
            </tr>
            <tr>
              <td>25 - 34</td>
              <td>128</td>
              <td>38.4%</td>
            </tr>
            <tr>
              <td>35 - 44</td>
              <td>22</td>
              <td>6.6%</td>
            </tr>
            <tr>
              <td>45 or Older</td>
              <td>4</td>
              <td>1.2%</td>
            </tr>
            <tr>
              <td>
                <bold>Education Level</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>High school or below</td>
              <td>46</td>
              <td>13.8%</td>
            </tr>
            <tr>
              <td>Bachelor</td>
              <td>198</td>
              <td>59.5%</td>
            </tr>
            <tr>
              <td>Postgraduate</td>
              <td>89</td>
              <td>26.7%</td>
            </tr>
            <tr>
              <td>
                <bold>Monthly Income (EGP)</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>0 - 1000</td>
              <td>29</td>
              <td>8.7%</td>
            </tr>
            <tr>
              <td>1000 - 3000</td>
              <td>103</td>
              <td>30.9%</td>
            </tr>
            <tr>
              <td>3000 - 5000</td>
              <td>49</td>
              <td>14.7%</td>
            </tr>
            <tr>
              <td>5000 - 8000</td>
              <td>47</td>
              <td>14.1%</td>
            </tr>
            <tr>
              <td>8000 - 10,000</td>
              <td>18</td>
              <td>5.4%</td>
            </tr>
            <tr>
              <td>Over 10,000</td>
              <td>87</td>
              <td>26.1%</td>
            </tr>
            <tr>
              <td>
                <bold>Visited Restaurant Based on Blogger Recommendation</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Yes</td>
              <td>166</td>
              <td>49.8%</td>
            </tr>
            <tr>
              <td>No</td>
              <td>167</td>
              <td>50.2%</td>
            </tr>
            <tr>
              <td>Total</td>
              <td>333</td>
              <td>100%</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
    </sec>
    <sec id="sec5">
      <title>5. Results</title>
      <sec id="sec5dot1">
        <title>5.1. Measurement Model Assessment</title>
        <p>Before testing the structural model, we assessed the reliability and validity of the measurement model using SmartPLS 4. We evaluated internal consistency, convergent validity, and discriminant validity following established guidelines.</p>
        <p>5.1.1. Internal Consistency and Convergent Validity</p>
        <p>The reliability and validity of the measurement items were assessed using several indices, including Outer Loadings, Cronbach’s Alpha, rho_A, Composite Reliability (rho_c), and Average Variance Extracted (AVE), as detailed in <bold>Table 2</bold>.</p>
        <p><bold>Table 2.</bold> Construct reliability and validity.</p>
        <table-wrap id="tbl2">
          <label>Table 2</label>
          <table>
            <tbody>
              <tr>
                <td>Variables</td>
                <td>Items</td>
                <td>Outer loadings</td>
                <td>Cronbach’s Alpha</td>
                <td>rho_A</td>
                <td>Composite Reliability</td>
                <td>AVE</td>
              </tr>
              <tr>
                <td rowspan="4">Content Quality (CQ)</td>
                <td>CQ1</td>
                <td>0.841</td>
                <td rowspan="4">0.848</td>
                <td rowspan="4">0.851</td>
                <td rowspan="4">0.898</td>
                <td rowspan="4">0.688</td>
              </tr>
              <tr>
                <td>CQ2</td>
                <td>0.875</td>
              </tr>
              <tr>
                <td>CQ3</td>
                <td>0.849</td>
              </tr>
              <tr>
                <td>CQ4</td>
                <td>0.748</td>
              </tr>
              <tr>
                <td rowspan="5">Source Credibility (CR)</td>
                <td>CR1</td>
                <td>0.858</td>
                <td rowspan="5">0.908</td>
                <td rowspan="5">0.909</td>
                <td rowspan="5">0.932</td>
                <td rowspan="5">0.732</td>
              </tr>
              <tr>
                <td>CR2</td>
                <td>0.860</td>
              </tr>
              <tr>
                <td>CR3</td>
                <td>0.867</td>
              </tr>
              <tr>
                <td>CR4</td>
                <td>0.822</td>
              </tr>
              <tr>
                <td>CR5</td>
                <td>0.869</td>
              </tr>
              <tr>
                <td rowspan="5">Interactivity (INT)</td>
                <td>INT1</td>
                <td>0.714</td>
                <td rowspan="3">0.811</td>
                <td rowspan="3">0.813</td>
                <td rowspan="3">0.869</td>
                <td rowspan="3">0.570</td>
              </tr>
              <tr>
                <td>INT2</td>
                <td>0.784</td>
              </tr>
              <tr>
                <td>INT3</td>
                <td>0.792</td>
              </tr>
              <tr>
                <td>INT4</td>
                <td>0.735</td>
                <td rowspan="2">
                </td>
                <td rowspan="2">
                </td>
                <td rowspan="2">
                </td>
                <td rowspan="2">
                </td>
              </tr>
              <tr>
                <td>INT5</td>
                <td>0.746</td>
              </tr>
              <tr>
                <td rowspan="5">Parasocial Relationship (PSR)</td>
                <td>PSR1</td>
                <td>0.781</td>
                <td rowspan="5">0.849</td>
                <td rowspan="5">0.850</td>
                <td rowspan="5">0.892</td>
                <td rowspan="5">0.623</td>
              </tr>
              <tr>
                <td>PSR2</td>
                <td>0.796</td>
              </tr>
              <tr>
                <td>PSR3</td>
                <td>0.780</td>
              </tr>
              <tr>
                <td>PSR4</td>
                <td>0.797</td>
              </tr>
              <tr>
                <td>PSR5</td>
                <td>0.794</td>
              </tr>
              <tr>
                <td rowspan="3">Brand Trust (TR)</td>
                <td>TR1</td>
                <td>0.882</td>
                <td rowspan="3">0.818</td>
                <td rowspan="3">0.819</td>
                <td rowspan="3">0.892</td>
                <td rowspan="3">0.733</td>
              </tr>
              <tr>
                <td>TR2</td>
                <td>0.840</td>
              </tr>
              <tr>
                <td>TR3</td>
                <td>0.846</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Regarding internal consistency, Cronbach’s Alpha values were used to assess the internal consistency of each construct. As shown in <bold>Table 2</bold>, the Cronbach’s Alpha values ranged from 0.811 to 0.908, demonstrating a high level of internal consistency across all constructs, with values well above the recommended threshold of 0.70. Similarly, rho_A values ranged from 0.813 to 0.909, further supporting the internal consistency of the constructs. Additionally, Composite Reliability (rho_c) values were consistently above 0.70, with values ranging from 0.869 to 0.932, indicating strong reliability for all constructs. These results confirm that the measurement items for all constructs—Content Quality (CQ), Source Credibility (CR), Interactivity (INT), Parasocial Relationship (PSR), and Brand Trust (TR)—are reliable.</p>
        <p>Convergent validity was evaluated using the Average Variance Extracted (AVE) and outer loadings. As presented in <bold>Table 2</bold>, all AVE values exceeded the recommended threshold of 0.50, ranging from 0.570 to 0.733. This indicates that each construct explains a sufficient amount of variance in its respective indicators. Additionally, as shown in <bold>Table 2</bold>, all indicator outer loadings exceeded the recommended value of 0.70, ranging from 0.714 to 0.882. These findings confirm adequate convergent validity, demonstrating that the indicators effectively measure their associated constructs.</p>
        <p>5.1.2. Discriminant Validity</p>
        <p>Discriminant validity was assessed using two criteria: the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations. As presented in <bold>Table 3</bold>, the square root of the AVE for each construct (diagonal elements in bold) was greater than its correlations with all other constructs. For instance, the square root of AVE for CQ was 0.830, which exceeded its correlations with CR (0.841), INT (0.690), PSR (0.628), and TR (0.702). This pattern was consistent across all constructs, confirming that each construct shares more variance with its own indicators than with other constructs.</p>
        <p><bold>Table 3.</bold> Discriminant validity.</p>
        <table-wrap id="tbl3">
          <label>Table 3</label>
          <table>
            <tbody>
              <tr>
                <td>
                </td>
                <td>
                </td>
                <td>
                  <bold>1</bold>
                </td>
                <td>
                  <bold>2</bold>
                </td>
                <td>
                  <bold>3</bold>
                </td>
                <td>
                  <bold>4</bold>
                </td>
                <td>
                  <bold>5</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="5">Fornell-Larcker Criterion</td>
                <td>(CQ)</td>
                <td>0.83</td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(CR)</td>
                <td>0.84</td>
                <td>0.85</td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(INT)</td>
                <td>0.69</td>
                <td>0.73</td>
                <td>0.75</td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(PSR)</td>
                <td>0.62</td>
                <td>0.67</td>
                <td>0.78</td>
                <td>0.78</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(TR)</td>
                <td>0.70</td>
                <td>0.72</td>
                <td>0.71</td>
                <td>0.75</td>
                <td>0.85</td>
              </tr>
              <tr>
                <td rowspan="5">Heterotrait-Monotrait Ration (HTMT)</td>
                <td>(CQ)</td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(CR)</td>
                <td>0.95</td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(INT)</td>
                <td>0.83</td>
                <td>0.85</td>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(PSR)</td>
                <td>0.73</td>
                <td>0.76</td>
                <td>0.94</td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>(TR)</td>
                <td>0.84</td>
                <td>0.84</td>
                <td>0.87</td>
                <td>0.9</td>
                <td>
                </td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Additionally, the HTMT values were assessed to evaluate discriminant validity [<xref ref-type="bibr" rid="B41">41</xref>]. While most construct pairs showed HTMT values below the conservative threshold of 0.85, two pairs exceeded this benchmark: CQ and CR (0.956) and INT and PSR (0.944). To further investigate these elevated values, we examined the HTMT confidence intervals through bootstrapping (5000 resamples). The results indicated that the confidence intervals for these pairs did not include 1.0, suggesting that the constructs are still empirically distinct [<xref ref-type="bibr" rid="B41">41</xref>]. Nevertheless, given the theoretical overlap between content quality and source credibility (as high-quality content inherently signals credibility), and between interactivity and parasocial relationships (as interactive engagement fosters emotional bonds), these elevated HTMT values can be considered theoretically reasonable. All Fornell-Larcker criteria fully meet the standard threshold, and HTMT values of all other construct pairs are below the strict 0.85 cutoff, confirming that the constructs are empirically distinct and that the measurement model exhibits satisfactory discriminant validity.</p>
        <p>5.1.3. Effect Size (f2)</p>
        <p>The effect size (f2) was assessed to evaluate the contribution of each independent variable to the R2 of the dependent variables. The results indicate that CQ → CR (0.864) and INT → PSR (0.474) have large effects, demonstrating that these predictors substantially contribute to their respective dependent variables. INT → CR (0.170) and PSR → TR (0.176) have medium effects, indicating moderate contributions. Conversely, CQ → TR (0.034) and CR → PSR (0.020) have small effects, whereas CQ → PSR (0.002) and INT → TR (0.013) show negligible effects. These findings suggest that while most relationships are meaningful, some paths may have limited practical significance.</p>
        <p>5.1.4. Coefficient of Determination (R2) and Predictive Relevance (Q2)</p>
        <p>The model’s predictive power was evaluated using the coefficient of determination (R2) and the Stone-Geisser Q2 value through the PLSpredict procedure [<xref ref-type="bibr" rid="B42">42</xref>]. The model explains 74.9% of the variance in Source Credibility (CR), 63.6% in Parasocial Relationship (PSR), and 67.1% in Brand Trust (TR). These values are considered substantial in social sciences research, indicating that the model has strong explanatory power. The Q2 values for all endogenous constructs were positive (CR = 0.746, PSR = 0.618, TR = 0.585), confirming the model’s predictive relevance. This indicates that the model is not only capable of explaining variance in the sample but also has predictive validity for new observations.</p>
        <p>5.1.5. Inner Collinearity (VIF)</p>
        <p>Before interpreting the structural model, we assessed inner collinearity using the Variance Inflation Factor (VIF). All inner VIF values were below the recommended threshold of 5.0 [<xref ref-type="bibr" rid="B43">43</xref>], ranging from 1.440 to 2.638. This indicates that collinearity is not a concern in the structural model, and the path coefficients can be interpreted reliably.</p>
        <p>5.1.6. Common Method Bias</p>
        <p>To address potential common method bias (CMB) arising from self-reported data, we conducted Harman’s single-factor test [<xref ref-type="bibr" rid="B44">44</xref>]. The results indicated that no single factor accounted for the majority of the variance (<italic>i.e.</italic>, the first factor did not explain more than 50% of the total variance), suggesting that common method bias is not a significant concern in this study.</p>
      </sec>
      <sec id="sec5dot2">
        <title>5.2. Model Fit</title>
        <p>The model fit was assessed using the Standardized Root Mean Square Residual (SRMR) and the Normed Fit Index (NFI). As shown in <bold>Table 4</bold>, the SRMR value for both the saturated and estimated models was 0.054, which is below the recommended threshold of 0.08, indicating a good model fit. Additionally, the NFI value was 0.875, exceeding the recommended threshold of 0.80, further confirming the acceptability of the model. The d_ULS (0.746) and d_G (0.321) values also support the model’s adequate fit to the data. These fit indices collectively indicate that the proposed model fits the data well and is suitable for interpreting the hypothesized relationships.</p>
        <p><bold>Table 4.</bold> Model fit.</p>
        <table-wrap id="tbl4">
          <label>Table 4</label>
          <table>
            <tbody>
              <tr>
                <td>
                </td>
                <td>
                  <bold>Saturated model</bold>
                </td>
                <td>
                  <bold>Estimated model</bold>
                </td>
              </tr>
              <tr>
                <td>SRMR</td>
                <td>0.054</td>
                <td>0.054</td>
              </tr>
              <tr>
                <td>d_ULS</td>
                <td>0.746</td>
                <td>0.746</td>
              </tr>
              <tr>
                <td>d_G</td>
                <td>0.321</td>
                <td>0.321</td>
              </tr>
              <tr>
                <td>Chi-square</td>
                <td>590.749</td>
                <td>590.749</td>
              </tr>
              <tr>
                <td>NFI</td>
                <td>0.875</td>
                <td>0.875</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
      </sec>
      <sec id="sec5dot3">
        <title>5.3. Hypotheses Testing</title>
        <p>Following the validation of the measurement model and the assessment of model fit, we tested the proposed hypotheses using bootstrapping analysis with 5000 resamples. The results are presented in <bold>Table 5</bold> and <bold>Table 6</bold>.</p>
        <p><bold>Table 5.</bold> Direct effects (Hypothesis testing).</p>
        <table-wrap id="tbl5">
          <label>Table 5</label>
          <table>
            <tbody>
              <tr>
                <td>PATH</td>
                <td>Original sample</td>
                <td>Standard deviation</td>
                <td>t statistics</td>
                <td>p values</td>
                <td>Decision</td>
              </tr>
              <tr>
                <td>Content Quality (CQ) → Source Credibility (CR)</td>
                <td>0.644</td>
                <td>0.043</td>
                <td>15.078</td>
                <td>0.000</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Content Quality (CQ) → Parasocial Relationship (PSR)</td>
                <td>0.056</td>
                <td>0.083</td>
                <td>0.676</td>
                <td>0.499</td>
                <td>Not supported</td>
              </tr>
              <tr>
                <td>Content Quality (CQ) → Brand Trust (TR)</td>
                <td>0.200</td>
                <td>0.070</td>
                <td>2.858</td>
                <td>0.004</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Source Credibility (CR) → Parasocial Relationship (PSR)</td>
                <td>0.170</td>
                <td>0.079</td>
                <td>2.153</td>
                <td>0.031</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Source Credibility (CR) → Brand Trust (TR)</td>
                <td>0.202</td>
                <td>0.075</td>
                <td>2.703</td>
                <td>0.007</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Interactivity (INT) → Source Credibility (CR)</td>
                <td>0.285</td>
                <td>0.045</td>
                <td>6.374</td>
                <td>0.000</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Interactivity (INT) → Parasocial Relationship (PSR)</td>
                <td>0.621</td>
                <td>0.061</td>
                <td>10.186</td>
                <td>0.000</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Interactivity (INT) → Brand Trust (TR)</td>
                <td>0.119</td>
                <td>0.054</td>
                <td>2.198</td>
                <td>0.028</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Parasocial Relationship (PSR) → Brand Trust (TR)</td>
                <td>0.398</td>
                <td>0.076</td>
                <td>5.255</td>
                <td>0.000</td>
                <td>Supported</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p><bold>Table 6.</bold> Specific indirect effects (Mediation testing).</p>
        <table-wrap id="tbl6">
          <label>Table 6</label>
          <table>
            <tbody>
              <tr>
                <td>PATH</td>
                <td>Original sample (O)</td>
                <td>Standard deviation (STDEV)</td>
                <td>T statistics (|O/STDEV|)</td>
                <td>p values</td>
                <td>Decision</td>
              </tr>
              <tr>
                <td>Content Quality (CQ) → Parasocial Relationship (PSR) → Trust (TR)</td>
                <td>0.022</td>
                <td>0.033</td>
                <td>0.681</td>
                <td>0.496</td>
                <td>Not supported</td>
              </tr>
              <tr>
                <td>Content Quality (CQ) → Source Credibility (CR) → Parasocial Relationship (PSR)</td>
                <td>0.110</td>
                <td>0.050</td>
                <td>2.200</td>
                <td>0.028</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Source Credibility (CR) → Parasocial Relationship (PSR) → Brand Trust (TR)</td>
                <td>0.068</td>
                <td>0.032</td>
                <td>2.108</td>
                <td>0.035</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Content Quality (CQ) → Source Credibility (CR) → Brand Trust (TR)</td>
                <td>0.130</td>
                <td>0.047</td>
                <td>2.756</td>
                <td>0.006</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Interactivity (INT) → Parasocial Relationship (PSR) → Brand Trust (TR)</td>
                <td>0.247</td>
                <td>0.061</td>
                <td>4.067</td>
                <td>0.000</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Interactivity (INT) → Source Credibility (CR) → Parasocial Relationship (PSR)</td>
                <td>0.049</td>
                <td>0.026</td>
                <td>1.902</td>
                <td>0.057</td>
                <td>Not supported</td>
              </tr>
              <tr>
                <td>Interactivity (INT) → Source Credibility (CR) → Brand Trust (TR)</td>
                <td>0.057</td>
                <td>0.025</td>
                <td>2.312</td>
                <td>0.021</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Content Quality (CQ) → Source Credibility (CR) → Parasocial Relationship (PSR) → Brand Trust (TR)</td>
                <td>0.044</td>
                <td>0.021</td>
                <td>2.105</td>
                <td>0.035</td>
                <td>Supported</td>
              </tr>
              <tr>
                <td>Interactivity (INT) → Source Credibility (CR) → Parasocial Relationship (PSR) → Brand Trust (TR)</td>
                <td>0.019</td>
                <td>0.010</td>
                <td>1.942</td>
                <td>0.052</td>
                <td>Not supported</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>5.3.1. Direct Effects</p>
        <p>The results of the hypothesis testing for direct effects are presented in <bold>Table 5</bold>. The findings indicate the following:</p>
        <p>H1 (INT → PSR): The path was positive and significant (<italic>β</italic> = 0.621, t = 10.186, p &lt; 0.001), supporting H1. This suggests that interactivity is positively associated with consumers’ parasocial relationships with food bloggers. Specifically, when food bloggers actively engage with their followers through comments and responses, followers develop stronger emotional bonds with them.</p>
        <p>H2 (INT → CR): The path was positive and significant (<italic>β</italic> = 0.285, t = 6.374, p &lt; 0.001), supporting H2. This indicates that interactivity positively influences consumers’ perception of source credibility. Followers who experience interactive communication with food bloggers tend to perceive them as more credible sources of information.</p>
        <p>H3 (CQ → PSR): The path was positive but not significant (<italic>β</italic> = 0.056, t = 0.676, p = 0.499), leading to the rejection of H3. This suggests that content quality does not directly associate with parasocial relationships with food bloggers. While content quality may be important for other outcomes, it does not appear to directly contribute to the formation of emotional bonds with followers.</p>
        <p>H4 (CQ → CR): The path was positive and significant (<italic>β</italic> = 0.644, t = 15.078, p &lt; 0.001), supporting H4. This confirms that content quality positively influences consumers’ perception of source credibility. High-quality content—characterized by informativeness, relevance, and visual appeal—enhances followers’ perceptions of the blogger’s expertise and trustworthiness.</p>
        <p>H5 (PSR → TR): The path was positive and significant (<italic>β</italic> = 0.398, t = 5.255, p &lt; 0.001), supporting H5. This indicates that parasocial relationships positively influence brand trust. When followers develop emotional bonds with food bloggers, they are more likely to trust the restaurants and brands recommended by those bloggers.</p>
        <p>H6 (CR → TR): The path was positive and significant (<italic>β</italic> = 0.202, t = 2.703, p = 0.007), supporting H6. This confirms that source credibility positively influences brand trust. Followers who perceive food bloggers as credible—expert, trustworthy, and attractive—are more likely to trust the brands endorsed by them.</p>
        <p>5.3.2. Mediation Effects (Specific Indirect Effects)</p>
        <p>To provide a comprehensive assessment of the mediation effects, we examined the total, direct, and indirect effects with bootstrap confidence intervals (5000 resamples). The total effect of Interactivity on Brand Trust (<italic>β</italic> = 0.444, p &lt; 0.001) is decomposed into a direct effect (<italic>β</italic> = 0.119, p = 0.028) and an indirect effect through Parasocial Relationship (<italic>β</italic> = 0.247, p &lt; 0.001). Similarly, the total effect of Content Quality on Brand Trust (<italic>β</italic> = 0.395, p &lt; 0.001) comprises a direct effect (<italic>β</italic> = 0.200, p = 0.004) and an indirect effect through Source Credibility (<italic>β</italic> = 0.130, p = 0.006). All bootstrap confidence intervals for the indirect effects did not include zero, further confirming the mediating roles. To test the mediation hypotheses (H7, H8, H9), we assessed the specific indirect effects using bootstrapping analysis (5000 resamples). The results are presented in <bold>Table 6</bold>. H7 (INT → PSR → TR): The indirect effect was positive and significant (<italic>β</italic> = 0.247, t = 4.067, p &lt; 0.001), supporting H7. This confirms that parasocial relationship partially mediates the relationship between interactivity and brand trust, as the direct path (INT → TR) also remained significant (<italic>β</italic> = 0.119, p = 0.028). This indicates that the effect of interactivity on brand trust operates through the emotional bond followers develop with the blogger, in addition to its direct influence. H8 (CQ → CR → TR): The indirect effect was positive and significant (<italic>β</italic> = 0.130, t = 2.756, p = 0.006), supporting H8. This confirms that source credibility partially mediates the relationship between content quality and brand trust, as the direct path (CQ → TR) also remained significant (<italic>β</italic> = 0.200, p = 0.004). This demonstrates that content quality’s influence on brand trust is channeled through the cognitive evaluation of the blogger’s credibility, alongside its direct effect. H9 (Serial Mediation): The results for serial mediation show partial support. Specifically, H9a, which proposed the sequential pathway from interactivity to brand trust through source credibility and then parasocial relationship (INT → CR → PSR → TR), was not supported (<italic>β</italic> = 0.019, t = 1.942, p = 0.052). This suggests that the sequential pathway from interactivity to brand trust through both mediators does not hold. In contrast, H9b, which proposed the cognitive-to-emotional pathway from content quality to brand trust through source credibility and parasocial relationship (CQ → CR → PSR → TR), was supported (<italic>β</italic> = 0.044, t = 2.105, p = 0.035). This indicates that content quality is positively associated with source credibility, which in turn is positively associated with parasocial relationships, ultimately leading to increased brand trust. This pathway reflects a cognitive-to-emotional sequence where rational evaluation precedes emotional bonding.</p>
        <p>5.3.3. Multi-Group Analysis (Gender)</p>
        <p>To examine whether the relationships in the model differ between male and female consumers, a multi-group analysis (MGA) was conducted. Measurement invariance was first established using the measurement invariance of composite models (MICOM) procedure [<xref ref-type="bibr" rid="B45">45</xref>]. The bootstrap MGA results revealed that most path coefficients did not differ significantly across gender groups (p &gt; 0.05). However, two significant differences were observed. The effect of Content Quality on Source Credibility was stronger for females than for males (difference = −0.209, p = 0.012). Similarly, the effect of Interactivity on Source Credibility was stronger for males than for females (difference = 0.217, p = 0.011). These findings suggest that while most relationships are consistent across genders, gender moderates the effects of content quality and interactivity on source credibility.</p>
      </sec>
    </sec>
    <sec id="sec6">
      <title>6. Discussion</title>
      <sec id="sec6dot1">
        <title>6.1. General Findings</title>
        <p>This study set out to understand how food bloggers’ interaction strategies—specifically interactivity and content quality—influence consumer brand trust in the Egyptian fast-food context, through the mediating roles of parasocial relationship and source credibility. While previous research has largely focused on either the emotional or cognitive aspects of influencer marketing separately, and most Egyptian studies have relied primarily on source credibility theory [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B12">12</xref>], this study integrates both pathways within a single framework to address this gap.</p>
        <p>The results reveal that interactivity significantly enhances both parasocial relationships and source credibility. When food bloggers actively engage with their followers—by responding to comments, asking questions, or participating in conversations—followers develop stronger emotional connections and simultaneously perceive the blogger as more credible. This finding aligns with previous research by [<xref ref-type="bibr" rid="B9">9</xref>][<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B24">24</xref>], and confirms that interactivity is not merely a communication tool but a bridge that connects both the heart and the mind of the consumer. In the Egyptian setting, where young consumers heavily rely on social media for dining decisions [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B29">29</xref>], this interactive engagement appears to be particularly influential in building trust.</p>
        <p>Interestingly, content quality was found to significantly influence source credibility but not parasocial relationships. This means that while high-quality content—informative, visually appealing, and relevant—makes followers perceive the blogger as expert and trustworthy, it does not directly translate into emotional bonding. One possible explanation is that emotional bonds require a sense of personal connection and reciprocity, which content alone cannot provide. This contrasts with some earlier assumptions that all forms of valuable content automatically deepen emotional ties [<xref ref-type="bibr" rid="B27">27</xref>], yet it aligns with the view that parasocial relationships are fundamentally built through interpersonal interaction rather than content consumption alone [<xref ref-type="bibr" rid="B29">29</xref>][<xref ref-type="bibr" rid="B37">37</xref>]. This clear distinction between the cognitive pathway (content quality → credibility) and the affective pathway (interactivity → parasocial relationship) constitutes a core observation of this research.</p>
        <p>Both parasocial relationships and source credibility significantly predict brand trust, verifying their function as parallel mediators. Parasocial relationship mediates the link between interactivity and brand trust, whereas source credibility mediates the association between content quality and brand trust. The two pathways operate simultaneously yet through distinct emotional and rational mechanisms. Regarding serial mediation, the cognitive-to-emotional sequence (content quality → source credibility → parasocial relationship → brand trust) is statistically significant, indicating that rational source evaluation facilitates subsequent emotional bonding. This finding supports the theoretical direction that credibility perceptions (CR) precede and enhance parasocial relationships (PSR), rather than the reverse. Conversely, the reverse serial path (interactivity → source credibility → parasocial relationship → brand trust) lacks significance, demonstrating that affective attachment does not inherently reshape followers’ credibility judgments within this cross-sectional sample. This partial serial mediation outcome deepens existing understanding of consumers’ multistage information processing of food influencers, confirming that cognitive evaluation can serve as a foundation for emotional bonding in the context of food blogger-follower relationships.</p>
        <p>Within Egypt’s fiercely competitive fast-food market marked by persistent food inflation, these results carry distinct contextual value. Local consumers exhibit heightened spending caution and increasingly turn to trusted social media figures for dining recommendations [<xref ref-type="bibr" rid="B6">6</xref>][<xref ref-type="bibr" rid="B15">15</xref>]. Bloggers who combine consistent audience interaction with high-quality content are better positioned to foster enduring brand trust. The findings complement prior Egyptian influencer research centred on source credibility dimensions [<xref ref-type="bibr" rid="B1">1</xref>][<xref ref-type="bibr" rid="B12">12</xref>] and extend regional literature by incorporating the underexplored affective parasocial relationship mechanism. Collectively, this study verifies that contemporary consumer trust in food brands is co-constructed via dual emotional and cognitive routes, each activated by distinct blogger interaction tactics.</p>
        <p>6.1.1. Theoretical Contributions</p>
        <p>First, this research extends parasocial relationship theory [<xref ref-type="bibr" rid="B7">7</xref>] and source credibility theory [<xref ref-type="bibr" rid="B8">8</xref>] to the understudied Egyptian food blogging and fast-food market context, responding to the call for more regional empirical evidence of influencer marketing frameworks. Most local Egyptian scholarship has prioritized cognitive credibility mechanisms in isolation; this study unites affective parasocial bonding and cognitive credibility evaluation into one unified research model to fill this contextual literature gap.</p>
        <p>Second, the research differentiates two discrete influence pathways triggered by separate blogger strategies, challenging the universal assumption that high-quality content alone cultivates consumer emotional attachment to influencers. The null direct effect of content quality on parasocial relationships empirically proves that one-way content output fails to generate interpersonal emotional bonds, which instead depend on two-way interactive exchanges. This boundary condition enriches theoretical applications of parasocial relationship theory within food influencer domains.</p>
        <p>Third, partial serial mediation results advance nuanced comprehension of sequential consumer judgment formation. The unidirectional cognitive-to-emotional serial path validates a staged evaluation logic for influencer information, while the insignificant reverse sequence identifies a contextual boundary for cross-over effects between parasocial bonds and source credibility, offering novel insights for subsequent mediating mechanism research.</p>
        <p>These contributions extend the theoretical boundaries of influencer marketing by demonstrating that affective and cognitive trust-building mechanisms operate through distinct blogger strategies.</p>
        <p>6.1.2. Managerial Implications</p>
        <p>For food content creators, interactivity should be prioritized as a core operational strategy. Proactive comment replies, audience Q&amp;A sessions and continuous conversational participation are critical to build both emotional parasocial ties and perceived credibility, whereas polished high-quality content functions primarily as a cognitive trust booster rather than an emotional bonding tool.</p>
        <p>For Egyptian fast-food restaurant brands partnering with food bloggers, collaboration contracts should incentivize sustained audience interaction beyond standardized content production. Marketers must adopt a dual-focus strategy: balancing informative, visually attractive content creation with consistent interactive influencer-follower dialogue to activate both trust-building pathways. Amid inflation-driven conservative consumer spending in Egypt, long-term influencer partnerships that nurture both credibility and emotional connection are critical to establishing sustainable brand trust and mitigating market competition pressures.</p>
        <p>6.1.3. Limitation and Future Research Directions</p>
        <p>This study contains several notable limitations that create avenues for follow-up inquiry. First, its cross-sectional survey design cannot establish strict causal relationships between the tested variables; longitudinal tracking or experimental manipulations are recommended for future work to validate causal inference. Second, the sample exclusively comprises Egyptian fast-food consumers, restricting cross-cultural generalizability; cross-national comparative research can test whether the dual trust-building pathways identified here hold across different cultural influencer ecosystems. Third, the scope is limited to fast-food dining, and results may not transfer to fine dining, grocery or other food consumption categories. Fourth, all data relies on self-reported participant perceptions; future studies may integrate objective social media engagement metrics to triangulate subjective attitudinal measurements.</p>
        <p>Future researchers can also expand the model by incorporating additional blogger interaction strategies such as personal self-disclosure, and test moderating variables including consumer age, social media usage frequency, or food involvement to identify heterogeneous response patterns across consumer subgroups. Longitudinal research tracking shifts in brand loyalty over time would further unpack the lasting impacts of blogger interactivity and content quality on consumer trust.</p>
        <p>Taken together, this study systematically unpacks how food bloggers’ interactivity and content quality shape Egyptian fast-food brand trust via emotional and cognitive mediating pathways. By distinguishing parallel and partial serial mediation effects, it fills regional theoretical gaps and delivers implementable guidance for local bloggers and restaurant marketers. Subsequent cross-cultural and longitudinal research can further validate and expand the boundary conditions of the proposed model.</p>
      </sec>
    </sec>
    <sec id="sec7">
      <title>Acknowledgements</title>
      <p>The author would like to express sincere gratitude to her supervisor, Dr. Honghong Zhang, for her continuous guidance, valuable feedback, and support throughout this research. Special thanks also go to all respondents who participated in the survey.</p>
    </sec>
    <sec id="sec8">
      <title>Appendix: Questionnaire Items</title>
      <p><bold>Table A1</bold> presents the sources of the questionnaire items.</p>
      <p><bold>Table A1.</bold>Sources of questionnaire items.</p>
      <table-wrap id="tbl7">
        <label>Table 7</label>
        <table>
          <tbody>
            <tr>
              <td>
                <bold>Construct</bold>
              </td>
              <td>
                <bold>Item</bold>
                <bold>Code</bold>
              </td>
              <td>
                <bold>Source</bold>
              </td>
            </tr>
            <tr>
              <td>Interactivity</td>
              <td>INT1 - INT5</td>
              <td>Mao (2023) - Jun &amp; Yi (2020)</td>
            </tr>
            <tr>
              <td>Content Quality</td>
              <td>CQ1 - CQ4</td>
              <td>Mao (2023) - Jun &amp; Yi (2020)</td>
            </tr>
            <tr>
              <td>Source Credibility</td>
              <td>CR1 - CR6</td>
              <td>Mao (2023) - Jun &amp; Yi (2020)</td>
            </tr>
            <tr>
              <td>Parasocial Relationship</td>
              <td>PSR1 - PSR5</td>
              <td>Mao (2023) - Jun &amp; Yi (2020)</td>
            </tr>
            <tr>
              <td>Brand Trust</td>
              <td>BT1 - BT3</td>
              <td>Mao (2023) - Jun &amp; Yi (2020)</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
    </sec>
    <sec id="sec9">
      <title>Questionnaire</title>
      <p>Dear Participant,</p>
      <p>You are invited to participate in an academic research study about social media food content creators (bloggers) in Egypt. The purpose of this study is to understand how different interaction strategies of food bloggers influence their followers’ perceptions and preferences. All your responses will be kept strictly confidential and used for academic purposes only. The survey will take approximately 5 minutes to complete.</p>
      <p><bold>Section 1: General Information &amp; Viewing Habits</bold></p>
      <p>1. Do you follow any Egyptian food bloggers/content creators?</p>
      <p>() Yes</p>
      <p>() No</p>
      <p>2. On which platform do you mainly follow Egyptian food bloggers?</p>
      <p>() Instagram</p>
      <p>() TikTok</p>
      <p>() YouTube</p>
      <p>() Facebook</p>
      <p>() Other: __________</p>
      <p>3. What is the username of your favorite Egyptian food blogger? (optional)</p>
      <p>(__________)</p>
      <p>4. How often do you watch Egyptian food blogger content?</p>
      <p>() Almost never</p>
      <p>() 1 day per week</p>
      <p>() 2 - 3 days per week</p>
      <p>() 4 - 5 days per week</p>
      <p>() Every day</p>
      <p>5. How many Egyptian food bloggers do you follow approximately?</p>
      <p>() 1 - 3</p>
      <p>() 4 - 6</p>
      <p>() 7 - 10</p>
      <p>() More than 10</p>
      <p><bold>Section 2: Independent Variables</bold></p>
      <p>Please indicate your level of agreement with the following statements regarding the food blogger you follow:</p>
      <p>(1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)</p>
      <table-wrap id="tbl8">
        <label>Table 8</label>
        <table>
          <tbody>
            <tr>
              <td>
              </td>
              <td>1</td>
              <td>2</td>
              <td>3</td>
              <td>4</td>
              <td>5</td>
            </tr>
            <tr>
              <td>
                <bold>Interactivity (INT)</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>6. The food blogger I follow regularly replies to followers’ comments.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>7. The blogger interacts with the audience through live streams(e.g., Q &amp; A, eating shows).</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>8. The blogger uses polls or question features in Stories to ask for followers’ opinions.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>9. The blogger creates content based on followers’ requests or suggestions.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>10. The blogger provides followers with a way to communicate directly with him/her.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Content Quality (CQ)</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>The blogger’s content feels authentic and natural, not overly scripted or fake.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>12. The blogger’s content (photos, videos) is clear and well-produced.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>13. The blogger’s content (e.g., food presentation) is attractive and catches my interest.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>14. The blogger’s content clearly conveys information about the restaurant.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Source Credibility (CR)</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>15. I believe the blogger’s reviews are authentic.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>16. The food blogger is honest.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>17. The food blogger is reliable.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>18. The food blogger is trustworthy.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>19. The blogger is experienced.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>20. The food blogger is professional.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Parasocial Relationship (PSR)</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>21. Live streaming makes me feel closer to the blogger.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>22. I look forward to the food blogger’s new videos.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>23. I would like to meet the food blogger in real life.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>24. Watching the food blogger’s videos makes me feel comfortable, like being with a friend.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>25. If the blogger appears on other social media platforms or in other people’s content, I would want to watch it.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Brand Trust</bold>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>26. I can trust the restaurants recommended by this food blogger.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>27. The restaurants recommended by this food blogger seem reliable.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>28. The restaurants promoted by this food blogger are trustworthy.</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Section 3: Behavioral Impact</bold></p>
      <p>29. Have you ever visited a restaurant specifically because of a recommendation from an Egyptian food blogger?</p>
      <p>() Yes</p>
      <p>() No</p>
      <p>If “Yes”, what was the main reason that motivated you to visit?</p>
      <p>() The blogger’s recommendation itself</p>
      <p>() Attractive photos/videos of the restaurant</p>
      <p>() The blogger’s detailed description of the experience</p>
      <p>() Seeing many positive comments from other followers</p>
      <p>() Other: __________</p>
      <p><bold>Section 4: Demographic Information</bold></p>
      <p>30. Gender:</p>
      <p>() Male</p>
      <p>() Female</p>
      <p>31. Age:</p>
      <p>() Under 18</p>
      <p>() 18 - 24</p>
      <p>() 25 - 34</p>
      <p>() 35 - 44</p>
      <p>() 45 or older</p>
      <p>32. Education level:</p>
      <p>() High school or below</p>
      <p>() Bachelor’s degree</p>
      <p>() Postgraduate (Master’s/PhD)</p>
      <p>33. Monthly spending level (EGP):</p>
      <p>() 0 - 1000</p>
      <p>() 1000 - 3000</p>
      <p>() 3000 - 5000</p>
      <p>() 5000 - 8000</p>
      <p>() 8000 - 10,000</p>
      <p>() Over 10,000</p>
      <p>34. What is the main reason you follow a food blogger? (Select all that apply)</p>
      <p>() Content quality (production quality, diversity)</p>
      <p>() Blogger’s personality and style</p>
      <p>() Honesty and objectivity of reviews</p>
      <p>() Expertise in cooking/food</p>
      <p>() Restaurant recommendations</p>
      <p>() Interaction style with followers</p>
      <p>() Other: __________</p>
      <p>35. Do you have any other thoughts to share about why you follow food bloggers or how their recommendations influence your choices? (optional)</p>
      <p>(__________)</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <title>References</title>
      <ref id="B1">
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