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    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Rea Press</journal-id>
      <journal-id journal-id-type="publisher-id">null</journal-id>
      <journal-title>Rea Press</journal-title><issn pub-type="ppub">3009-4496</issn><issn pub-type="epub">09-4496</issn><publisher>
      	<publisher-name>Rea Press</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">https://doi.org/10.22105/masi.v2i3.75</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group><subject>Voice-assistant recommendation source, Tourists’ inspiration, Collective threats, Implicit beliefs</subject></subj-group>
      </article-categories>
      <title-group>
        <article-title>Voice Assistant Source and Tourist Inspiration: The Moderating Effects of Collective Threats and Implicit Beliefs</article-title><subtitle>Voice Assistant Source and Tourist Inspiration: The Moderating Effects of Collective Threats and Implicit Beliefs</subtitle></title-group>
      <contrib-group><contrib contrib-type="author">
	<name name-style="western">
	<surname> Jiang</surname>
		<given-names>Han Ling</given-names>
	</name>
	<aff>Department of Management, National Taipei University of Technology, Taipei, Taiwan.</aff>
	</contrib><contrib contrib-type="author">
	<name name-style="western">
	<surname>Lu</surname>
		<given-names>Lin Hua </given-names>
	</name>
	<aff>Department of Management, National Taipei University of Technology, Taipei, Taiwan.</aff>
	</contrib></contrib-group>		
      <pub-date pub-type="ppub">
        <month>07</month>
        <year>2025</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>18</day>
        <month>07</month>
        <year>2025</year>
      </pub-date>
      <volume>2</volume>
      <issue>3</issue>
      <permissions>
        <copyright-statement>© 2025 Rea Press</copyright-statement>
        <copyright-year>2025</copyright-year>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license>
      </permissions>
      <related-article related-article-type="companion" vol="2" page="e235" id="RA1" ext-link-type="pmc">
			<article-title>Voice Assistant Source and Tourist Inspiration: The Moderating Effects of Collective Threats and Implicit Beliefs</article-title>
      </related-article>
	  <abstract abstract-type="toc">
		<p>
			As Voice-Assistant (VA) devices become increasingly integrated into consumers’ daily lives, their application in tourism marketing has drawn scholarly attention. This research investigates how the source of VA-provided positive recommendations—either from in-group or out-group members—affects tourists’ attraction-visiting intentions and information-seeking behaviours. Drawing on social identity theory and the concept of consumer inspiration, this study examines the mediating role of tourist inspiration and the moderating effects of perceived collective threats and tourists’ implicit beliefs. Two scenario-based experiments were conducted to test the proposed framework. Study 1 involved 180 participants and study 2 involved 360 participants. Results show that in-group travel information provided by the VA evokes greater tourist inspiration than out-group information, leading to higher information-seeking and visit intention. The effect is moderated by perceived collective threat and belief type: under high threat, tourists are more inspired by in-group messages; under low threat, out-group messages are more effective. Tourists with entity beliefs respond more to in-group messages, while those with incremental beliefs are more inspired by out-group information. These findings highlight the importance of tailoring VA messages to tourist profiles and travel contexts to enhance marketing impact. Theoretically, the study contributes to understanding the interplay between social identity, inspiration, and AI-based recommendation systems. Practically, it offers actionable insights for tourism marketers and technology developers to optimise VA messaging strategies, tailoring them according to tourist profiles and contextual factors to enhance destination marketing effectiveness.
		</p>
		</abstract>
    </article-meta>
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