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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Psychol.</journal-id>
<journal-title>Frontiers in Psychology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Psychol.</abbrev-journal-title>
<issn pub-type="epub">1664-1078</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpsyg.2023.1109092</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Psychology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Exploring the mechanism of live streaming e-commerce anchors&#x2019; language appeals on users&#x2019; purchase intention</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Ma</surname>
<given-names>Erwei</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="c001" ref-type="corresp"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2224408/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Jiaojiao</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1978799/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Kai</given-names>
</name>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1462397/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>School of Journalism and Communication, Zhengzhou University</institution>, <addr-line>Zhengzhou</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>School of Journalism and Communication, Guangxi University</institution>, <addr-line>Nanning</addr-line>, <country>China</country></aff>
<author-notes>
<fn id="fn0001" fn-type="edited-by"><p>Edited by: Kui Yi, East China Jiaotong University, China</p></fn>
<fn id="fn0002" fn-type="edited-by"><p>Reviewed by: Anxin Xu, Fujian Agriculture and Forestry University, China; J&#x00F6;rg Henseler, University of Twente, Netherlands</p></fn>
<corresp id="c001">&#x002A;Correspondence: Erwei Ma, <email>maerwei@zzu.edu.cn</email></corresp>
<fn id="fn0003" fn-type="other"><p>This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>09</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1109092</elocation-id>
<history>
<date date-type="received">
<day>27</day>
<month>11</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>09</day>
<month>02</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2023 Ma, Liu and Li.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Ma, Liu and Li</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Live streaming e-commerce is an important way for consumers to shop nowadays. Anchors, as salesperson in live streaming e-commerce, greatly affect the sales of the broadcast room. This paper studies the influence mechanism of anchors&#x2019; language appeals, rational appeal, and emotional appeal on users&#x2019; purchase intention. This study establishes a research framework which based on stimulus-organism-response (SOR) theory, and constructs a model to reflect the relationship between anchors language appeals, self-referencing, self-brand congruity, and purchase intention.</p>
</sec>
<sec>
<title>Methods</title>
<p>Survey using a convenience sample (N&#x2009;=&#x2009;509) was conducted on Chinese mainland netizens through WJX platform (October 17-23, 2022) to obtain data. The partial least square structural equation modeling (PLS-SEM) method was used for data analysis.</p>
</sec>
<sec>
<title>Results</title>
<p>The study found that anchors&#x2019; language appeals was positively correlated with self-referencing and self-brand congruity, and there is a positive correlation between self-referencing, self-brand congruity, and purchase intention. Self-referencing and self-brand congruity play a mediating effect between anchors language appeals and purchase intention.</p>
</sec>
<sec>
<title>Discussion</title>
<p>This study advances the literature on live streaming e-commerce research and SOR and provides practical implications to influence the strategy of the e-commerce anchors.</p>
</sec>
</abstract>
<kwd-group>
<kwd>live streaming e-commerce</kwd>
<kwd>language appeals</kwd>
<kwd>self-referencing</kwd>
<kwd>self-brand congruity</kwd>
<kwd>purchase intention</kwd>
</kwd-group>
<contract-num rid="cn1">22AXW009</contract-num>
<contract-sponsor id="cn1">Research on Advertising Algorithm Trap and its Governance</contract-sponsor>
<contract-sponsor id="cn2">Henan University Philosophy and Social Science Application Research</contract-sponsor>
<contract-sponsor id="cn3">2023-YYZD-23</contract-sponsor>
<counts>
<fig-count count="2"/>
<table-count count="7"/>
<equation-count count="0"/>
<ref-count count="94"/>
<page-count count="13"/>
<word-count count="9921"/>
</counts>
</article-meta>
</front>
<body>
<sec id="sec1" sec-type="intro">
<label>1.</label>
<title>Introduction</title>
<p>Live streaming e-commerce emerged in China in 2016, and several e-commerce platforms tested the live streaming e-commerce model (<xref ref-type="bibr" rid="ref26">Fan et al., 2022</xref>). In 2016, Amazon was the first to launch a style code live show, where netizens watch a live stream and can place a purchase directly through a link below the video. Facebook also introduced a live e-commerce service in 2018. With this service, merchants start a live stream on the platform, users take a screenshot of their selected products, submit it to the merchants, and the merchant&#x2019;s replies with payment instructions <italic>via</italic> messenger. Data show that in 2019 the US live e-commerce scale was less than US$1 billion (<xref ref-type="bibr" rid="ref15">Coresight Research, 2020</xref>), in just 2&#x2009;years it soared to US$11 billion in 2021 and is expected to exceed US$25 billion in 2023 (<xref ref-type="bibr" rid="ref16">Coresight Research, 2022</xref>), which is a very rapid growth. Since 2018, the development of China&#x2019;s live-streaming e-commerce industry has become an industry trend, attracting a large number of funds and institutions to enter, with a surge in practitioners and market size. According to data released by the Department of E-Commerce of the Ministry of Commerce of the People&#x2019;s Republic of China, the overall market size of live e-commerce in China will reach 1.05&#x2009;trillion yuan in 2020 (<xref ref-type="bibr" rid="ref62">Ministry of Commerce of the People's Republic of China, 2020</xref>), far exceeding the $6 billion of the United States, the world&#x2019;s largest consumer market (<xref ref-type="bibr" rid="ref68">Paper News, 2022</xref>). With the growth of live e-commerce, anchors engaged in live-streaming with goods are rapidly emerging, with celebrities, influencers, and government officials of all stripes joining the live-streaming bandwagon. During Amazon&#x2019;s Members&#x2019; Day 11&#x2013;13 July 2022, comedian Kevin Hart, Australian model Miranda Kerr, and actress Kyle Richards, among others, joined Amazon&#x2019;s live-streaming bandwagon anchors (<xref ref-type="bibr" rid="ref65">NetEase News, 2022</xref>). An e-commerce anchor is a person who interacts with users and recommends products in a live stream (<xref ref-type="bibr" rid="ref82">Sun et al., 2019</xref>; <xref ref-type="bibr" rid="ref69">Park and Lin, 2020</xref>). With their unique personal charisma and professional knowledge, e-commerce anchors recommend products to users in various languages, talk about their experiences in trying or using products, and answer their pop-up questions in the live stream, which not only enhance the consumption atmosphere in the live stream, but also strengthen the close connection with users, alleviate their worries when shopping, and promote them to click to place a purchase. As an important link between consumers and products, e-commerce anchors have become a key influence on live streaming traffic and transaction volume, and their value is highly valued.</p>
<p>The strong marketing influence of e-commerce anchors on consumers has attracted scholars&#x2019; research attention. Some scholars have studied the impact of anchor features on users&#x2019; purchase intention. The more authoritative anchor identity is, the more it will stimulate consumers&#x2019; purchase intention (<xref ref-type="bibr" rid="ref50">Liu F. et al., 2020</xref>). The credibility and professionalism of anchors will affect consumers&#x2019; purchase intention (<xref ref-type="bibr" rid="ref33">Han and Xu, 2020</xref>). Some scholars have studied the influence of anchors&#x2019; communication strategies on consumption intention. For example, the more similarity of communication styles between anchors and users, the higher the users&#x2019; purchase intention (<xref ref-type="bibr" rid="ref86">Wu et al., 2020</xref>). The interactivity of anchor language will also affect consumers&#x2019; purchase intention (<xref ref-type="bibr" rid="ref81">Sun, 2022</xref>). According to the study of <xref ref-type="bibr" rid="ref37">Hu and Chaudhry (2020)</xref>, anchors provide consumers with personalized recommendations, guidance, and services by means of in-line dialogue and barrage questioning, which has a positive impact on consumers&#x2019; purchase intention. Anchors use voice, expression, or movement skills to answer consumers&#x2019; questions, which can promote consumers to make purchasing decisions (<xref ref-type="bibr" rid="ref9">Cai and Wohn, 2019</xref>).</p>
<p>Scholars have identified language appeals as an important factor influencing consumer behavior (<xref ref-type="bibr" rid="ref30">Gong et al., 2021</xref>). Research in this area has also attracted the attention of researchers, focusing on two main areas. (1) The influence of linguistic appeals in advertising on consumer behavior. Some scholars focus on research on the type of advertising and the use of appeal strategies, e.g., some scholars focus on different types of advertising (<xref ref-type="bibr" rid="ref43">Kim et al., 2019</xref>), while others have studied the use and effects of appeal strategies in the sale of different types of products (utility, hedonic, and technological; <xref ref-type="bibr" rid="ref44">Kronrod and Danziger, 2013</xref>; <xref ref-type="bibr" rid="ref28">Gahlot Sarkar et al., 2019</xref>; <xref ref-type="bibr" rid="ref63">Motoki et al., 2019</xref>; <xref ref-type="bibr" rid="ref80">Srivastava and Dorsch, 2020</xref>). Some scholars have focused on the mechanisms of appeal strategies on purchase behavior, such as the effect of celebrity endorsement on purchase behavior (<xref ref-type="bibr" rid="ref84">Wang et al., 2013</xref>), the effect of inaccessible strategies such as emotional appeal, ability appeal, functional appeal, and experience on purchase behavior (<xref ref-type="bibr" rid="ref92">Zarantonello et al., 2013</xref>; <xref ref-type="bibr" rid="ref42">Kazakova et al., 2016</xref>; <xref ref-type="bibr" rid="ref43">Kim et al., 2019</xref>). (2) The effect of language appeals of anchors on consumer behavior in TV shopping. Scholars study found that Language appeals is an important communication strategy for anchors. The language appeals of anchors is a vital factor in promoting consumers&#x2019; purchase intention (<xref ref-type="bibr" rid="ref53">Luna and Peracchio, 2005</xref>; <xref ref-type="bibr" rid="ref7">Bishop and Peterson, 2010</xref>). Some researchers concentrate on the impact of communicators on consumers&#x2019; purchasing decisions, including celebrities, specialists, and professional anchors (<xref ref-type="bibr" rid="ref55">Ma and Jongchang, 2019</xref>; <xref ref-type="bibr" rid="ref91">Zafar et al., 2020</xref>; <xref ref-type="bibr" rid="ref81">Sun, 2022</xref>). Some researchers concentrate on understanding the mechanisms through which various appeal methods, including logical appeal, emotional appeal, and personalized appeal, affect consumers&#x2019; perceptions of brands and their propensity to make purchases (<xref ref-type="bibr" rid="ref75">Shih, 2011</xref>; <xref ref-type="bibr" rid="ref64">Siddiqui and Nabeel, 2014</xref>; <xref ref-type="bibr" rid="ref40">Jayathunga and Kumara, 2016</xref>; <xref ref-type="bibr" rid="ref54">Luo et al., 2021</xref>). Webcasting has developed into a significant means of influence because of the growth of live online commerce. However, there are few researches on the influence mechanism of webcasting e-commerce about anchors&#x2019; language appeals and users&#x2019; purchase intention. Research on this relationship is valuable because it helps to explain how the language appeals of anchors contributes to the purchase intention of users. At the same time, the research results are helpful to promote the sales performance of live shopping. Therefore, the first research question of this paper is put forward: What is the influence mechanism of anchors&#x2019; language appeals on users&#x2019; purchase intention?</p>
<p>This study contributes in four ways: firstly, it extends current research on Internet consumer behavior by exploring how anchors&#x2019; language appeals affect users&#x2019; purchase intention. Secondly, it helps to deepen our understanding of the mechanisms by which anchors&#x2019; language appeals influence users&#x2019; purchase intention. Thirdly, the findings of the study can serve the development of promotional strategies for live marketing campaigns; fourthly, broadens the applicable scenarios of the stimulus-organism-response (SOR) theoretical framework, particularly in the context of China.</p>
</sec>
<sec id="sec2">
<label>2.</label>
<title>Theoretical framework and hypotheses</title>
<sec id="sec3">
<label>2.1.</label>
<title>Theoretical basis</title>
<p>The stimulus-organism-response model was proposed by <xref ref-type="bibr" rid="ref58">Mehrabian and Russell (1974)</xref> as a framework for explaining the effects of external environmental stimuli on individuals&#x2019; cognition, emotion, and behavior. The SOR model is widely used to predict and explain consumer behavior in online marketing environments. <xref ref-type="bibr" rid="ref49">Li et al. (2022)</xref> explored the effect of social presence in live streaming on customer impulse buying based on the stimulus&#x2013;organism&#x2013;response framework. <xref ref-type="bibr" rid="ref37">Hu and Chaudhry (2020)</xref>, used the SOR model to study how relationships (social and structural bonds) in live e-commerce enhance consumer engagement. The structural integrity of SOR model and its classification of the process of the influence of information stimulus on human behavior have been widely recognized by researchers. Therefore, this study uses the SOR model as the theoretical basis, anchors&#x2019; language appeals as the stimulus, self-referencing and self-brand congruity as the organism state, Anchors&#x2019; language appeals as the stimulus, self-referencing and self-brand congruity as the organism state, and purchase intention as the behavioral response.</p>
<sec id="sec4">
<label>2.1.1.</label>
<title>Stimulus factors-anchors&#x2019; language appeals</title>
<p>&#x201C;Stimulus&#x201D; is a &#x201C;trigger&#x201D; (<xref ref-type="bibr" rid="ref58">Mehrabian and Russell, 1974</xref>) that causes a change in the internal or external state of an individual, which can be a source of information, message content, etc. According to <xref ref-type="bibr" rid="ref83">Turley and Kelley (1997)</xref>, language appeals to product promotion can be divided into rational and emotional appeals; <xref ref-type="bibr" rid="ref67">Pang and Lee (2004)</xref> showed that rational and emotional appeals are two types of appeals that can significantly influence consumption intention. Based on this, this study classifies anchors&#x2019; language appeals into rational appeal and emotional appeal. Rational appeal mainly changes consumers&#x2019; psychological perceptions and purchase behavior by conveying factual information, such as product recommendations in terms of price, quality, and composition (<xref ref-type="bibr" rid="ref73">Resnik and Stern, 1977</xref>). Affective appeal influences consumers&#x2019; psychological perception and purchase behavior by stimulating consumers&#x2019; emotions toward the brand and even toward the salesperson, such as product recommendation in terms of humorous appeal and nostalgic appeal (<xref ref-type="bibr" rid="ref18">De Pelsmacker and Geuens, 1997</xref>).</p>
</sec>
<sec id="sec5">
<label>2.1.2.</label>
<title>Organism state: Self-referencing and self-brand congruity</title>
<p>&#x201C;Organism&#x201D; refers to the emotional and cognitive mediated states that arise when an individual interacts with external stimuli (<xref ref-type="bibr" rid="ref58">Mehrabian and Russell, 1974</xref>).</p>
<p>Self-referencing refers to an individual comparing external information with self-relevant information stored in memory (<xref ref-type="bibr" rid="ref21">Debevec and Romeo, 1992</xref>). Research has found that there is a self-referential effect on consumers&#x2019; live shopping behavior, i.e., consumers compare the anchor with their self-characteristics (image, beliefs, personality, abilities, etc.), and the stronger the identification, the stronger their willingness to purchase (<xref ref-type="bibr" rid="ref38">Hu et al., 2017</xref>). Study has found that when consumers receive information about products, they associate the information with their personal experiences, triggering the phenomenon of self-referencing&#xFF0C;the outcome of treatment can affect their purchase intention (<xref ref-type="bibr" rid="ref8">Burnkrant and Unnava, 1995</xref>; <xref ref-type="bibr" rid="ref57">Martin et al., 2004</xref>). Research indicates that self-referencing is an important factor influencing consumers&#x2019; purchase decisions (<xref ref-type="bibr" rid="ref11">Chang, 2011</xref>; <xref ref-type="bibr" rid="ref90">Yoon and Park, 2012</xref>).</p>
<p>Consumer buying behavior is not just about satisfying physical needs, but also about satisfying psychological desires. Consumers tend to buy brands that help express and shape their self-image (<xref ref-type="bibr" rid="ref48">Levy, 1959</xref>). <xref ref-type="bibr" rid="ref29">Gardner and Levy (1955)</xref> refer to this phenomenon as self-brand congruity. The similarity and match between self-image and brand image. Study found that consumers make congruent judgments after acquiring information about products, and when consumers produce stronger congruence, they show stronger willingness to purchase (<xref ref-type="bibr" rid="ref74">Roy and Rabbanee, 2015</xref>). When users watch a live broadcast, they combine the brand image perceived through the linguistic description of the anchor with their own image, and the stronger the congruence between the two, the stronger their purchase intention (<xref ref-type="bibr" rid="ref39">Islam et al., 2018</xref>). Many studies have pointed out that self-congruity (self-consistency) is an important factor that influences consumers to choose and purchase products (<xref ref-type="bibr" rid="ref70">Peters and Leshner, 2013</xref>).</p>
<p>Based on the above discussion, self-referencing and self-brand congruity are both emotional and cognitive responses that occur when individuals interact with external stimuli (<xref ref-type="bibr" rid="ref77">Sirgy, 1982</xref>; <xref ref-type="bibr" rid="ref6">Bellezza, 1984</xref>). In this study, self-referencing and self-brand congruity were used as mediating factors.</p>
</sec>
<sec id="sec6">
<label>2.1.3.</label>
<title>Consumer behavior response: Purchase intention</title>
<p>The user&#x2019;s purchasing intention is referred to as the behavioral reaction in this study. Purchase intention is the subjective probability or potential that a consumer will purchase a specific good (<xref ref-type="bibr" rid="ref23">Dodds et al., 1991</xref>). Purchase intention, according to <xref ref-type="bibr" rid="ref78">S&#x00F8;ndergaard et al. (2005)</xref>, can be viewed as the irrational propensity of consumers to select particular goods, which develops during the process of product or service cognition prompted by external impacts.</p>
</sec>
</sec>
<sec id="sec7">
<label>2.2.</label>
<title>Hypotheses</title>
<sec id="sec8">
<label>2.2.1.</label>
<title>Relationship between anchors&#x2019; language appeals and self-referencing</title>
<p>E-commerce anchors recommend products to consumers through language appeals (<xref ref-type="bibr" rid="ref82">Sun et al., 2019</xref>). In this study, anchor appeals are divided into rational appeals and emotional appeals. It has been noted that both rational and emotional appeal evoke consumers&#x2019; memories of their own experiences and trigger self-referential behaviors (<xref ref-type="bibr" rid="ref84">Wang et al., 2013</xref>; <xref ref-type="bibr" rid="ref17">de Graaf, 2022</xref>). On the one hand, rational appeal is used to motivate consumers by providing factual information (<xref ref-type="bibr" rid="ref46">Lee et al., 2020</xref>). Emotional appeal, on the other hand, is used to arouse consumers&#x2019; memories by stimulating their emotional responses (<xref ref-type="bibr" rid="ref46">Lee et al., 2020</xref>). Anchors&#x2019; language demands for products will increase users&#x2019; self-information cognition and stimulate users&#x2019; self-reference to a certain extent. As a result, this paper proposes hypothesis 1.</p>
<disp-quote>
<p><italic>H1a</italic>: Anchors&#x2019; rational appeal has a positive influence on self-referencing.</p>
</disp-quote>
<disp-quote>
<p><italic>H1b</italic>: Anchors&#x2019; emotional appeal has a positive influence on self-referencing.</p>
</disp-quote>
</sec>
<sec id="sec9">
<label>2.2.2.</label>
<title>Relationship between anchors&#x2019; language appeals and self-brand congruity</title>
<p>Past research has found that consumers matched product&#x2019;s utilitarian attributes with their own ideal attributes through rational appeal, and matched product&#x2019;s value express attributes with their own self-concept through emotional appeal, creating self-brand congruity (<xref ref-type="bibr" rid="ref41">Johar and Sirgy, 1991</xref>). When customers receive information, they will relate it to their self-image and have a tendency to choose brands that are consistent with their self-image (<xref ref-type="bibr" rid="ref34">Heath and Scott, 1998</xref>). When the values conveyed by the brand are related to the values of consumers, consumers will have a positive attitude toward the brand (<xref ref-type="bibr" rid="ref60">Michel et al., 2022</xref>). Anchors&#x2019; language appeal to the brand will arouse users to compare the brand image with their self-image. If the two are consistent, they will increase their purchase intention. Thus, this paper proposes hypothesis 2:</p>
<disp-quote>
<p><italic>H2a</italic>: The rational appeal of the anchor has a positive impact on self-brand congruity.</p>
</disp-quote>
<disp-quote>
<p><italic>H2b</italic>: The emotional appeal of the anchor has a positive impact on self-brand congruity.</p>
</disp-quote>
</sec>
<sec id="sec10">
<label>2.2.3.</label>
<title>The influence of self-referencing on purchase intention</title>
<p>Self-referencing impacts customers&#x2019; buying intentions (<xref ref-type="bibr" rid="ref20">Debevec and Iyer, 1988</xref>; <xref ref-type="bibr" rid="ref84">Wang et al., 2013</xref>). Consumers buy products in order to maintain or improve the self-image they pursue. The self-reference effect causes consumers to compare their self-image with the product image. A high level of self-referencing will provide more recognition and a larger desire to make a purchase (<xref ref-type="bibr" rid="ref84">Wang et al., 2013</xref>; <xref ref-type="bibr" rid="ref89">Yim et al., 2021</xref>). When users watch live broadcasts, they will empathize with and recognize the language demands of anchors, and trigger the self-reference effect, which will strengthen the psychological connection between users and anchors and further affect the purchase intention. Thus, this paper proposes hypothesis 3:</p>
<disp-quote>
<p><italic>H3</italic>: Self-referencing influences purchase intention in a favorable way.</p>
</disp-quote>
</sec>
<sec id="sec11">
<label>2.2.4.</label>
<title>The influence of self-brand congruity on purchase intention</title>
<p>Previous studies have pointed out that self-brand congruity has an important impact on consumers&#x2019; purchasing behavior (<xref ref-type="bibr" rid="ref25">Ekinci and Riley, 2003</xref>; <xref ref-type="bibr" rid="ref72">Rabbanee et al., 2020</xref>). It is found that the higher the self-brand consistency of consumers, the more positive their attitude toward the product, the more likely they are to have the purchase intention (<xref ref-type="bibr" rid="ref51">Liu C. et al., 2020</xref>; <xref ref-type="bibr" rid="ref85">Wijnands and Gill, 2020</xref>). Customers are more likely to make purchases when their self-image is extremely aligned with that of the brand (<xref ref-type="bibr" rid="ref71">Phua and Kim, 2018</xref>; <xref ref-type="bibr" rid="ref13">Chen et al., 2021</xref>). Through rational and emotional appeals of anchor language, users connect self-concept with brand-image to improve their purchase intention. Hence, this paper proposes hypothesis 4:</p>
<disp-quote>
<p><italic>H4</italic>: Self-brand congruity has a positive impact on purchase intention.</p>
</disp-quote>
</sec>
<sec id="sec12">
<label>2.2.5.</label>
<title>The influence of self-referencing on self-brand congruity</title>
<p>Based on previous studies, the more self-referential consumers perceive, the more consistent their selves are with the brand, and the more likely they are to demonstrate a stronger sense of engagement, loyalty, and identity (<xref ref-type="bibr" rid="ref59">Mehta, 1999</xref>). Through self-referencing, consumers compare information with themselves to promote the congruity between self-image and brand image. It has been found that consumers&#x2019; self-referential behavior helps to facilitate consumers&#x2019; association with brands and increase self-brand congruence (<xref ref-type="bibr" rid="ref84">Wang et al., 2013</xref>). This paper argues that self-referencing triggered by anchors&#x2019; verbal appeals affects users&#x2019; self-brand congruence. Thus, this paper proposes hypothesis 5:</p>
<disp-quote>
<p><italic>H5</italic>: Self-referencing has a positive effect on self-brand congruity.</p>
</disp-quote>
</sec>
<sec id="sec13">
<label>2.2.6.</label>
<title>The mediating role of self-referencing between language appeals and purchase intention</title>
<p>Individuals&#x2019; perceptions of verbal and visual stimuli can induce their self-referencing behaviors and then influence their attitudes and intentions (<xref ref-type="bibr" rid="ref21">Debevec and Romeo, 1992</xref>). The appeals by communicators can stimulate consumers&#x2019; self-referencing behaviors, thus influencing consumers&#x2019; attitudes toward products (<xref ref-type="bibr" rid="ref90">Yoon and Park, 2012</xref>). Further research has found that both verbal and non-verbal appeals can stimulate self-referential behavior, which in turn influences consumers&#x2019; purchase intentions (<xref ref-type="bibr" rid="ref84">Wang et al., 2013</xref>). The self-referencing effect is mediated by the fact that users are influenced by the verbal appeal of the anchor, which stimulates self-referencing and increases their perception of their self-image, and when their perception of their self-image matches the product recommended by the anchor, they increase their purchase intention. Thus, this paper proposes hypothesis 6.</p>
<disp-quote>
<p><italic>H6a</italic>: Self-referencing mediates between rational appeal and purchase intentions.</p>
</disp-quote>
<disp-quote>
<p><italic>H6b</italic>: Self-referencing mediates between emotional appeal and purchase intentions.</p>
</disp-quote>
</sec>
<sec id="sec14">
<label>2.2.7.</label>
<title>The mediating role of self-brand congruity between language appeals and purchase intention</title>
<p>Research has found that people will consider the degree of consistency between a product&#x2019;s brand image and their own image when screening the product, and the higher this consistency, the higher people&#x2019;s willingness to purchase the product (<xref ref-type="bibr" rid="ref87">Yang, 2018</xref>; <xref ref-type="bibr" rid="ref13">Chen et al., 2021</xref>). When consumers are exposed to brand information, they develop brand image perceptions and tend to purchase the brand if they perceive that their self-image and the image portrayed by the brand converge, or if they believe that the image portrayed by the brand can satisfy their self-image (<xref ref-type="bibr" rid="ref51">Liu C. et al., 2020</xref>; <xref ref-type="bibr" rid="ref85">Wijnands and Gill, 2020</xref>). In a study on brand filter conditions, self-brand congruence between brand filter conditions (self-endorsing vs. other-endorsing) and brand attitudes, purchase intention <xref ref-type="bibr" rid="ref87">Yang (2018)</xref>. Based on the existing studies, this paper further explores whether self-brand congruence plays a mediating effect between anchor language appeals and purchase intention. Thus, this paper proposes hypothesis 7.</p>
<disp-quote>
<p><italic>H7a</italic>: Self-brand congruity plays a mediating role between rational appeal and purchase intention.</p>
</disp-quote>
<disp-quote>
<p><italic>H7b</italic>: Self-brand congruity plays a mediating role between emotional appeal and purchase intention.</p>
</disp-quote>
</sec>
<sec id="sec15">
<label>2.2.8.</label>
<title>The chain mediating role of self-referencing and self-brand congruity</title>
<p>As mentioned in the previous section, the anchor&#x2019;s rational and emotional appeal act as stimuli to trigger consumers&#x2019; self-referencing and self-brand congruity, which in turn influence consumers&#x2019; purchase intentions. Meanwhile, self-referencing has an impact on the relationship between anchors&#x2019; language appeals and consumers&#x2019; self-brand congruity (<xref ref-type="bibr" rid="ref59">Mehta, 1999</xref>). Research suggests that self-referencing encourages consumers to connect with brands and increases self-brand congruence, which in turn influences consumers&#x2019; purchase intentions (<xref ref-type="bibr" rid="ref84">Wang et al., 2013</xref>). A study by <xref ref-type="bibr" rid="ref71">Phua and Kim (2018)</xref> also found that self-brand congruence interacts with self-referencing and perceived humor to influence consumers&#x2019; brand attitudes and purchase intentions. Users perceive brand messages through anchor language appeals, and if users&#x2019; self-reference increases, self-concept, and brand image are more likely to be congruent, which in turn increases purchase intentions. Thus, this paper proposes hypothesis 8:</p>
<disp-quote>
<p><italic>H8a</italic>: Self-referencing and self-brand congruity play a chain mediating role between rational appeal and purchase intentions.</p>
</disp-quote>
<disp-quote>
<p><italic>H8b</italic>: Self-referencing and self-brand congruity play a chain mediating role between emotional appeal and purchase intentions.</p>
</disp-quote>
</sec>
</sec>
<sec id="sec16">
<label>2.3.</label>
<title>Model specification</title>
<p>Based on the above assumptions, the research model of this paper is constructed as follows (<xref rid="fig1" ref-type="fig">Figure 1</xref>). In this paper, the stimulus factors are rational appeal (RA) and emotional appeal (EA), the organism state is self-referencing (SR), self-brand congruity (SC), and the behavioral response is purchase intention (PI).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Conceptual model.</p>
</caption>
<graphic xlink:href="fpsyg-14-1109092-g001.tif"/>
</fig>
</sec>
</sec>
<sec id="sec17">
<label>3.</label>
<title>Research design</title>
<sec id="sec18">
<label>3.1.</label>
<title>Measures</title>
<p>The questionnaire consists of two parts, the first part is the basic demographic information (including gender, age, and education level, etc.). The second part is the measurement of research variables (including rational appeal, emotional appeal, self-referencing, self-brand congruity, and purchase intention). In this study, questionnaire items using measurement variables were all from existing studies, and were modified according to the live streaming e-commerce scenario to ensure the accuracy and effectiveness of measurement items. Rational appeal was measured by using <xref ref-type="bibr" rid="ref73">Resnik and Stern (1977)</xref> scale. Emotional appeal was measured by using <xref ref-type="bibr" rid="ref18">De Pelsmacker and Geuens (1997)</xref> scale, and provocation and eroticism were excluded from the study due to legal restrictions on live streaming. <xref ref-type="bibr" rid="ref19">Debevec (1995)</xref> scale was used for self-referencing. Self-brand congruity was adopted from <xref ref-type="bibr" rid="ref71">Phua and Kim (2018)</xref> scale. Purchase intention was measured using <xref ref-type="bibr" rid="ref93">Zeithaml et al. (1996)</xref> scale. Each item of the study variables was measured using a seven-point Likert scale (ranging from 1&#x2009;=&#x2009;&#x201C;completely disagree&#x201D; to 7&#x2009;=&#x2009;&#x201C;completely agree&#x201D;).</p>
<p>To enhance the validity of the formal survey, a small preliminary survey was conducted between October 15 and 16, 2022, and 100 questionnaires were distributed. According to the results of the analysis of the pre-survey data, one measure of the independent variable emotional appeal had a factor loading (loadings) of 0.601, which was less than the standard 0.707 (<xref ref-type="bibr" rid="ref76">Shimp and Sharma, 1987</xref>), so this questionnaire item was considered for deletion and the remaining items constituted the formal questionnaire for this study. The pre-study data were not applied to the final data analysis.</p>
</sec>
<sec id="sec19">
<label>3.2.</label>
<title>Data collection</title>
<p>The formal survey used a convenience sample for data collection, and the questionnaire was created on the Chinese online survey platform, Questionnaire Star,<xref rid="fn0004" ref-type="fn"><sup>1</sup></xref> to a sample of Internet users in mainland China. Respondents were informed of the purpose of the study before filling out the questionnaire and were aware that the survey was anonymous. The questionnaires were distributed from October 17 to October 23, 2022.</p>
<p>In the formal survey, 509 questionnaires were collected, and 482 questionnaires were obtained excluding the invalid questionnaires, and the percentage of valid questionnaires was 94.70%. The gender of the respondents was 55.40% female (<italic>N</italic>&#x2009;=&#x2009;267) and 44.60% male (<italic>N</italic>&#x2009;=&#x2009;215). 84.00% of the total sample were aged 18&#x2013;35, and nearly 77.80% of the respondents had university or higher education. According to the Discovery Report &#x201C;2022 Live E-Commerce Industry Report&#x201D; (<xref ref-type="bibr" rid="ref22">Discovery Report, 2022</xref>), 18&#x2013;37&#x2009;years old are the main group of live e-commerce consumers. The &#x201C;Tmall 618 Taobao Live Consumer Portrait&#x201D; (<xref ref-type="bibr" rid="ref52">Taobao Live, 2019</xref>) released by Chinese live e-commerce giant Taobao Live also shows that the top three main consumer groups are 22&#x2013;32, 12&#x2013;21, and 33&#x2013;40&#x2009;years old. Taken together, the demographic information distribution of the survey respondents is broadly consistent with the demographic information characteristics of the current main consumer groups of live shopping. The demographic information of the sample is shown in <xref rid="tab1" ref-type="table">Table 1</xref>.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Demographic characteristics of sample (<italic>N</italic>&#x2009;=&#x2009;482).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="left" valign="top">Item</th>
<th align="center" valign="top">Frequency</th>
<th align="center" valign="top">Ratio (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="2">Gender</td>
<td align="left" valign="top">Male</td>
<td align="center" valign="top">215</td>
<td align="left" valign="top">44.60%</td>
</tr>
<tr>
<td align="left" valign="top">Female</td>
<td align="center" valign="top">267</td>
<td align="left" valign="top">55.40%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Age</td>
<td align="left" valign="top">Under 18</td>
<td align="center" valign="top">17</td>
<td align="left" valign="top">3.50%</td>
</tr>
<tr>
<td align="left" valign="top">18&#x2013;24</td>
<td align="center" valign="top">283</td>
<td align="left" valign="top">58.70%</td>
</tr>
<tr>
<td align="left" valign="top">25&#x2013;35</td>
<td align="center" valign="top">122</td>
<td align="left" valign="top">25.30%</td>
</tr>
<tr>
<td align="left" valign="top">Above 35</td>
<td align="center" valign="top">60</td>
<td align="left" valign="top">12.40%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="7">Education</td>
<td align="left" valign="top">Below junior high school</td>
<td align="center" valign="top">14</td>
<td align="left" valign="top">2.90%</td>
</tr>
<tr>
<td align="left" valign="top">Junior high school</td>
<td align="center" valign="top">23</td>
<td align="left" valign="top">4.80%</td>
</tr>
<tr>
<td align="left" valign="top">High school</td>
<td align="center" valign="top">32</td>
<td align="left" valign="top">6.60%</td>
</tr>
<tr>
<td align="left" valign="top">Specialist</td>
<td align="center" valign="top">38</td>
<td align="left" valign="top">7.90%</td>
</tr>
<tr>
<td align="left" valign="top">Bachelor</td>
<td align="center" valign="top">235</td>
<td align="left" valign="top">48.80%</td>
</tr>
<tr>
<td align="left" valign="top">Master</td>
<td align="center" valign="top">124</td>
<td align="left" valign="top">25.70%</td>
</tr>
<tr>
<td align="left" valign="top">Phd and above</td>
<td align="center" valign="top">16</td>
<td align="left" valign="top">3.30%</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="sec20" sec-type="results">
<label>4.</label>
<title>Results</title>
<p>In this study, partial least squares structural equation model (PLS-SEM) was selected for data analysis and model test. The main reasons for PLS-SEM were as follows: first, PLS-SEM method is more suitable for exploratory research, and second, it is suitable for small sample research (<xref ref-type="bibr" rid="ref32">Hair et al., 2019</xref>). Based on this, this paper selects SmartPLS 4.0 software for analysis.</p>
<sec id="sec21">
<label>4.1.</label>
<title>Mean and standard deviations</title>
<p><xref rid="tab2" ref-type="table">Table 2</xref> shows the arithmetic mean, standard deviation, and the relative importance of all study variables. The arithmetic mean and the relative importance of all the variables reach the average level. Thus, considering the study sample under examination and analysis.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Mean, SD, excess kurtosis, and skewness.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">SD</th>
<th align="center" valign="top">Excess kurtosis</th>
<th align="center" valign="top">Skewness</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">RA1</td>
<td align="center" valign="bottom">5.241</td>
<td align="center" valign="bottom">1.380</td>
<td align="left" valign="bottom">0.758</td>
<td align="left" valign="bottom">&#x2212;0.894</td>
</tr>
<tr>
<td align="left" valign="bottom">RA2</td>
<td align="center" valign="bottom">5.046</td>
<td align="center" valign="bottom">1.418</td>
<td align="left" valign="bottom">0.133</td>
<td align="left" valign="bottom">&#x2212;0.694</td>
</tr>
<tr>
<td align="left" valign="bottom">RA3</td>
<td align="center" valign="bottom">5.137</td>
<td align="center" valign="bottom">1.352</td>
<td align="left" valign="bottom">0.371</td>
<td align="left" valign="bottom">&#x2212;0.705</td>
</tr>
<tr>
<td align="left" valign="bottom">RA4</td>
<td align="center" valign="bottom">4.934</td>
<td align="center" valign="bottom">1.488</td>
<td align="left" valign="bottom">&#x2212;0.125</td>
<td align="left" valign="bottom">&#x2212;0.549</td>
</tr>
<tr>
<td align="left" valign="bottom">RA5</td>
<td align="center" valign="bottom">5.160</td>
<td align="center" valign="bottom">1.434</td>
<td align="left" valign="bottom">0.347</td>
<td align="left" valign="bottom">&#x2212;0.787</td>
</tr>
<tr>
<td align="left" valign="bottom">RA6</td>
<td align="center" valign="bottom">5.178</td>
<td align="center" valign="bottom">1.435</td>
<td align="left" valign="bottom">0.416</td>
<td align="left" valign="bottom">&#x2212;0.852</td>
</tr>
<tr>
<td align="left" valign="bottom">RA7</td>
<td align="center" valign="bottom">4.952</td>
<td align="center" valign="bottom">1.453</td>
<td align="left" valign="bottom">0.298</td>
<td align="left" valign="bottom">&#x2212;0.674</td>
</tr>
<tr>
<td align="left" valign="bottom">RA8</td>
<td align="center" valign="bottom">5.039</td>
<td align="center" valign="bottom">1.456</td>
<td align="left" valign="bottom">0.212</td>
<td align="left" valign="bottom">&#x2212;0.675</td>
</tr>
<tr>
<td align="left" valign="bottom">RA9</td>
<td align="center" valign="bottom">5.019</td>
<td align="center" valign="bottom">1.511</td>
<td align="left" valign="bottom">&#x2212;0.083</td>
<td align="left" valign="bottom">&#x2212;0.690</td>
</tr>
<tr>
<td align="left" valign="bottom">RA10</td>
<td align="center" valign="bottom">4.934</td>
<td align="center" valign="bottom">1.424</td>
<td align="left" valign="bottom">&#x2212;0.139</td>
<td align="left" valign="bottom">&#x2212;0.501</td>
</tr>
<tr>
<td align="left" valign="bottom">RA11</td>
<td align="center" valign="bottom">4.828</td>
<td align="center" valign="bottom">1.457</td>
<td align="left" valign="bottom">&#x2212;0.272</td>
<td align="left" valign="bottom">&#x2212;0.454</td>
</tr>
<tr>
<td align="left" valign="bottom">RA12</td>
<td align="center" valign="bottom">4.587</td>
<td align="center" valign="bottom">1.663</td>
<td align="left" valign="bottom">&#x2212;0.635</td>
<td align="left" valign="bottom">&#x2212;0.403</td>
</tr>
<tr>
<td align="left" valign="bottom">RA13</td>
<td align="center" valign="bottom">4.558</td>
<td align="center" valign="bottom">1.712</td>
<td align="left" valign="bottom">&#x2212;0.730</td>
<td align="left" valign="bottom">&#x2212;0.389</td>
</tr>
<tr>
<td align="left" valign="bottom">RA14</td>
<td align="center" valign="bottom">4.766</td>
<td align="center" valign="bottom">1.587</td>
<td align="left" valign="bottom">&#x2212;0.267</td>
<td align="left" valign="bottom">&#x2212;0.576</td>
</tr>
<tr>
<td align="left" valign="bottom">EA1</td>
<td align="center" valign="bottom">4.994</td>
<td align="center" valign="bottom">1.579</td>
<td align="left" valign="bottom">0.142</td>
<td align="left" valign="bottom">&#x2212;0.780</td>
</tr>
<tr>
<td align="left" valign="bottom">EA2</td>
<td align="center" valign="bottom">4.795</td>
<td align="center" valign="bottom">1.691</td>
<td align="left" valign="bottom">&#x2212;0.389</td>
<td align="left" valign="bottom">&#x2212;0.605</td>
</tr>
<tr>
<td align="left" valign="bottom">EA3</td>
<td align="center" valign="bottom">4.193</td>
<td align="center" valign="bottom">1.789</td>
<td align="left" valign="bottom">&#x2212;0.859</td>
<td align="left" valign="bottom">&#x2212;0.168</td>
</tr>
<tr>
<td align="left" valign="bottom">SR1</td>
<td align="center" valign="bottom">4.166</td>
<td align="center" valign="bottom">1.895</td>
<td align="left" valign="bottom">&#x2212;1.041</td>
<td align="left" valign="bottom">&#x2212;0.221</td>
</tr>
<tr>
<td align="left" valign="bottom">SR2</td>
<td align="center" valign="bottom">4.533</td>
<td align="center" valign="bottom">1.671</td>
<td align="left" valign="bottom">&#x2212;0.485</td>
<td align="left" valign="bottom">&#x2212;0.498</td>
</tr>
<tr>
<td align="left" valign="bottom">SR3</td>
<td align="center" valign="bottom">4.577</td>
<td align="center" valign="bottom">1.731</td>
<td align="left" valign="bottom">&#x2212;0.562</td>
<td align="left" valign="bottom">&#x2212;0.463</td>
</tr>
<tr>
<td align="left" valign="bottom">SB1</td>
<td align="center" valign="bottom">4.371</td>
<td align="center" valign="bottom">1.666</td>
<td align="left" valign="bottom">&#x2212;0.672</td>
<td align="left" valign="bottom">&#x2212;0.310</td>
</tr>
<tr>
<td align="left" valign="bottom">SB2</td>
<td align="center" valign="bottom">4.394</td>
<td align="center" valign="bottom">1.657</td>
<td align="left" valign="bottom">&#x2212;0.569</td>
<td align="left" valign="bottom">&#x2212;0.297</td>
</tr>
<tr>
<td align="left" valign="bottom">PI1</td>
<td align="center" valign="bottom">4.950</td>
<td align="center" valign="bottom">1.447</td>
<td align="left" valign="bottom">&#x2212;0.053</td>
<td align="left" valign="bottom">&#x2212;0.597</td>
</tr>
<tr>
<td align="left" valign="bottom">PI2</td>
<td align="center" valign="bottom">4.732</td>
<td align="center" valign="bottom">1.560</td>
<td align="left" valign="bottom">&#x2212;0.434</td>
<td align="left" valign="bottom">&#x2212;0.445</td>
</tr>
<tr>
<td align="left" valign="bottom">PI3</td>
<td align="center" valign="bottom">4.471</td>
<td align="center" valign="bottom">1.738</td>
<td align="left" valign="bottom">&#x2212;0.674</td>
<td align="left" valign="bottom">&#x2212;0.391</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec22">
<label>4.2.</label>
<title>Measurement model inspection</title>
<p>The test of measurement model is mainly to test various indicators of reliability, convergent validity, and discriminant validity (<xref ref-type="bibr" rid="ref32">Hair et al., 2019</xref>). Reliability is an index to evaluate the stability and congruity of the model, which was evaluated by Cronbach&#x2019;s Alpha coefficient and combined reliability (CR). Cronbach&#x2019;s Alpha coefficient below 0.6 is considered untrustworthy, and above 0.8 indicates good reliability (<xref ref-type="bibr" rid="ref5">Bagozzi and Yi, 1988</xref>). The minimum value of CR is 0.7, and the larger the value, the more the item can measure the latent variable (<xref ref-type="bibr" rid="ref66">Nunnally, 1978</xref>). In this analysis, the reliability analysis results are shown in <xref rid="tab3" ref-type="table">Table 3</xref>. Cronbach&#x2019;s Alpha coefficients and CR coefficients of all dimensions are greater than 0.7, indicating that each item has good reliability and good internal consistency.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Reliability and validity results of measurement model.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="left" valign="top">Items</th>
<th align="center" valign="top">Loadings</th>
<th align="center" valign="top">Cronbach&#x2019;s alpha</th>
<th align="center" valign="top">Rho_A</th>
<th align="center" valign="top">CR</th>
<th align="center" valign="top">AVE</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" rowspan="14">RA</td>
<td align="left" valign="bottom">RA1</td>
<td align="center" valign="bottom">0.744</td>
<td align="center" valign="middle" rowspan="14">0.953</td>
<td align="center" valign="middle" rowspan="14">0.957</td>
<td align="center" valign="middle" rowspan="14">0.958</td>
<td align="center" valign="middle" rowspan="14">0.622</td>
</tr>
<tr>
<td align="left" valign="bottom">RA2</td>
<td align="center" valign="bottom">0.796</td>
</tr>
<tr>
<td align="left" valign="bottom">RA3</td>
<td align="center" valign="bottom">0.777</td>
</tr>
<tr>
<td align="left" valign="bottom">RA4</td>
<td align="center" valign="bottom">0.803</td>
</tr>
<tr>
<td align="left" valign="bottom">RA5</td>
<td align="center" valign="bottom">0.777</td>
</tr>
<tr>
<td align="left" valign="bottom">RA6</td>
<td align="center" valign="bottom">0.723</td>
</tr>
<tr>
<td align="left" valign="bottom">RA7</td>
<td align="center" valign="bottom">0.788</td>
</tr>
<tr>
<td align="left" valign="bottom">RA8</td>
<td align="center" valign="bottom">0.759</td>
</tr>
<tr>
<td align="left" valign="bottom">RA9</td>
<td align="center" valign="bottom">0.818</td>
</tr>
<tr>
<td align="left" valign="bottom">RA10</td>
<td align="center" valign="bottom">0.824</td>
</tr>
<tr>
<td align="left" valign="bottom">RA11</td>
<td align="center" valign="bottom">0.828</td>
</tr>
<tr>
<td align="left" valign="bottom">RA12</td>
<td align="center" valign="bottom">0.812</td>
</tr>
<tr>
<td align="left" valign="bottom">RA13</td>
<td align="center" valign="bottom">0.782</td>
</tr>
<tr>
<td align="left" valign="bottom">RA14</td>
<td align="center" valign="bottom">0.799</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">EA</td>
<td align="left" valign="bottom">EA1</td>
<td align="center" valign="bottom">0.817</td>
<td align="center" valign="middle" rowspan="3">0.808</td>
<td align="center" valign="middle" rowspan="3">0.816</td>
<td align="center" valign="middle" rowspan="3">0.886</td>
<td align="center" valign="middle" rowspan="3">0.722</td>
</tr>
<tr>
<td align="left" valign="bottom">EA2</td>
<td align="center" valign="bottom">0.873</td>
</tr>
<tr>
<td align="left" valign="bottom">EA3</td>
<td align="center" valign="bottom">0.859</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">SR</td>
<td align="left" valign="bottom">SR1</td>
<td align="center" valign="bottom">0.889</td>
<td align="center" valign="middle" rowspan="3">0.880</td>
<td align="center" valign="middle" rowspan="3">0.883</td>
<td align="center" valign="middle" rowspan="3">0.926</td>
<td align="center" valign="middle" rowspan="3">0.807</td>
</tr>
<tr>
<td align="left" valign="bottom">SR2</td>
<td align="center" valign="bottom">0.904</td>
</tr>
<tr>
<td align="left" valign="bottom">SR3</td>
<td align="center" valign="bottom">0.901</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="2">SC</td>
<td align="left" valign="bottom">SC1</td>
<td align="center" valign="bottom">0.934</td>
<td align="center" valign="middle" rowspan="2">0.861</td>
<td align="center" valign="middle" rowspan="2">0.862</td>
<td align="center" valign="middle" rowspan="2">0.935</td>
<td align="center" valign="middle" rowspan="2">0.878</td>
</tr>
<tr>
<td align="left" valign="bottom">SC2</td>
<td align="center" valign="bottom">0.940</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">PI</td>
<td align="left" valign="bottom">PI1</td>
<td align="center" valign="bottom">0.894</td>
<td align="center" valign="middle" rowspan="3">0.863</td>
<td align="center" valign="middle" rowspan="3">0.864</td>
<td align="center" valign="middle" rowspan="3">0.916</td>
<td align="center" valign="middle" rowspan="3">0.785</td>
</tr>
<tr>
<td align="left" valign="bottom">PI2</td>
<td align="center" valign="bottom">0.886</td>
</tr>
<tr>
<td align="left" valign="bottom">PI3</td>
<td align="center" valign="bottom">0.878</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Validity is an index to comprehensively evaluate whether the measurement model can accurately reflect the purpose and requirements of evaluation. Generally, it is tested from two aspects: convergence validity and discriminative validity. Convergence validity measures mean variance extraction (AVE) and factor load values. According to <xref ref-type="bibr" rid="ref4">Bagozzi (1981)</xref>, the AVE value is suggested to be greater than 0.5, and the higher the AVE, the better the convergent validity. According to the suggestion of <xref ref-type="bibr" rid="ref10">Carmines and Zeller (1979)</xref>, a loadings value greater than 0.707 has a good convergence validity. As can be seen from <xref rid="tab3" ref-type="table">Table 3</xref>, the AVE and loadings values of each measurement item in this study are all greater than 0.5 and 0.707, indicating good convergent validity of the scale.</p>
<p>Discriminant validity refers to the distinction between items in different dimensions. In this paper, the Fornell&#x2013;Larcker standard was adopted to evaluate the scale&#x2019;s discriminant validity (<xref ref-type="bibr" rid="ref27">Fornell and Larcker, 1981</xref>). If the square root of the AVE of factors is greater than all inter-construct correlations, the discriminant validity is supported. As shown in <xref rid="tab4" ref-type="table">Table 4</xref>, the value on the diagonal (in bold) is the square root value of each variable AVE, which is larger than the correlation coefficient of all other variables, indicating that all dimensions of the scale used in this study have good discriminative validity.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Fornell&#x2013;Larcker criterion (comparison of square root of average variance extracted and inter-construct correlations).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Emotional appeal</th>
<th align="center" valign="top">Purchase intention</th>
<th align="center" valign="top">Rational appeal</th>
<th align="center" valign="top">Self-brand congruity</th>
<th align="center" valign="top">Self-referencing</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">Emotional appeal</td>
<td align="center" valign="bottom"><bold>0.850</bold></td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Purchase intention</td>
<td align="center" valign="bottom">0.337</td>
<td align="center" valign="bottom"><bold>0.886</bold></td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Rational appeal</td>
<td align="center" valign="bottom">0.418</td>
<td align="center" valign="bottom">0.438</td>
<td align="center" valign="bottom"><bold>0.788</bold></td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Self-brand congruity</td>
<td align="center" valign="bottom">0.461</td>
<td align="center" valign="bottom">0.482</td>
<td align="center" valign="bottom">0.406</td>
<td align="center" valign="bottom"><bold>0.937</bold></td>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Self-referencing</td>
<td align="center" valign="bottom">0.472</td>
<td align="center" valign="bottom">0.459</td>
<td align="center" valign="bottom">0.396</td>
<td align="center" valign="bottom">0.639</td>
<td align="center" valign="bottom"><bold>0.898</bold></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The square root of average variance extracted (AVE) is shown on the diagonal of the matrix.</p>
</table-wrap-foot>
</table-wrap>
<p>Discriminant validity refers to the distinction between items in different dimensions. In this paper, the Heterotrait-monotrait ratio standard was adopted to evaluate the scale&#x2019;s discriminant validity, and HTMT shall be less than 0.9 (<xref ref-type="bibr" rid="ref35">Henseler et al., 2015</xref>). As shown in <xref rid="tab5" ref-type="table">Table 5</xref>, HTMT are less than 0.8, indicating that all dimensions of the scale used in this study have good discriminative validity.</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Discriminant validity of measurement model (HTMT).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Emotional appeal</th>
<th align="center" valign="top">Purchase intention</th>
<th align="center" valign="top">Rational appeal</th>
<th align="center" valign="top">Self-brand congruity</th>
<th align="center" valign="top">Self-referencing</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">Emotional appeal</td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Purchase intention</td>
<td align="center" valign="bottom">0.407</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Rational appeal</td>
<td align="center" valign="bottom">0.472</td>
<td align="center" valign="bottom">0.484</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Self-brand congruity</td>
<td align="center" valign="bottom">0.548</td>
<td align="center" valign="bottom">0.559</td>
<td align="center" valign="bottom">0.439</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="bottom">Self-referencing</td>
<td align="center" valign="bottom">0.555</td>
<td align="center" valign="bottom">0.526</td>
<td align="center" valign="bottom">0.425</td>
<td align="center" valign="bottom">0.730</td>
<td/>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec23">
<label>4.3.</label>
<title>Structural model analysis</title>
<p>The structural model was examined by checking the path coefficient (<italic>&#x03B2;</italic>) and coefficient of determination (<italic>R</italic><sup>2</sup>). <xref rid="fig2" ref-type="fig">Figure 2</xref> shows the results of the structural model test.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Structural equation model results diagram. <sup>&#x002A;&#x002A;&#x002A;</sup><italic>p</italic>&#x2009;&#x003C;&#x2009;0.001&#xFF1B;<sup>&#x002A;&#x002A;</sup><italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</caption>
<graphic xlink:href="fpsyg-14-1109092-g002.tif"/>
</fig>
<p>The relationship between anchor language appeals, self-referencing, self-brand congruity, and purchase intention was verified by examining the path coefficient (<italic>&#x03B2;</italic>). Rational appeal was significantly and positively correlated with self-referencing (<italic>&#x03B2;</italic>&#x2009;=&#x2009;0.241; <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001) and self-brand congruity (<italic>&#x03B2;</italic>&#x2009;=&#x2009;0.136; <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), and hypotheses H1a and H2a held, indicating that anchors&#x2019; rational appeal had a positive influence on users&#x2019; self-referencing and self-brand congruity; emotional appeal was positively correlated with self-referencing (<italic>&#x03B2;</italic>&#x2009;=&#x2009;0.371; <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001) and self-brand congruity (<italic>&#x03B2;</italic>&#x2009;=&#x2009;0.165; <italic>p</italic>&#x2009;&#x003C;&#x2009;0.01) were significantly positively correlated, and hypotheses H1b and H2b held, indicating that anchor emotional appeal has a positive influence on users&#x2019; self-referencing and self-brand congruity. Self-referencing was significantly and positively correlated with purchase intention (<italic>&#x03B2;</italic>&#x2009;=&#x2009;0.255; <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), and self-brand congruity was significantly and positively correlated with purchase intention (<italic>&#x03B2;</italic>&#x2009;=&#x2009;0.319; <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), and hypotheses H3 and H4 hold, indicating that users&#x2019; self-referencing and self-brand congruity have a positive influence on purchase intention. Self-referencing was significantly and positively correlated with self-brand congruity (<italic>&#x03B2;</italic>&#x2009;=&#x2009;0.508; <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), and hypothesis H5 holds, indicating that user self-referencing has a positive effect on self-brand congruity.</p>
<p>As can be seen from <xref rid="fig2" ref-type="fig">Figure 2</xref>, the explanatory power <italic>R</italic><sup>2</sup> of self-referencing and self-brand congruity to purchase intention is 0.271, the explanatory power <italic>R</italic><sup>2</sup> of rational and emotional appeal to self-referencing is 0.279, and the explanatory power <italic>R</italic><sup>2</sup> of rational and emotional appeal to self-brand congruity is 0.456.</p>
</sec>
<sec id="sec24">
<label>4.4.</label>
<title>Intermediary effect verification</title>
<p>This study analyzes the mediating effect according to the method proposed by <xref ref-type="bibr" rid="ref94">Zhao et al. (2010)</xref>, and the results are shown in <xref rid="tab6" ref-type="table">Table 6</xref>. Self-referencing and self-brand congruity mediate the influence of rational appeal and emotional appeal on purchase intention. The VAF values were 30.10, 31.35, 23.00, and 20.31%, respectively, indicating that self-referencing and self-brand congruity played a partial mediating role in the relationship between the two groups of independent variables and dependent variables.</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Results of mediation effect analysis.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Independent variables</th>
<th align="left" valign="top">Mediation variables</th>
<th align="left" valign="top">Dependent variable</th>
<th align="center" valign="top">Direct effects (<italic>T</italic> statistics)</th>
<th align="center" valign="top">Indirect effects (<italic>T</italic> statistics)</th>
<th align="center" valign="top">Total effects</th>
<th align="center" valign="top">VAF</th>
<th align="left" valign="top">Results</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">RA</td>
<td align="left" valign="middle">SR</td>
<td align="left" valign="middle" rowspan="4">PI</td>
<td align="center" valign="middle">0.144 (4.662)</td>
<td align="center" valign="middle">0.062 (2.614)</td>
<td align="center" valign="middle">0.206</td>
<td align="center" valign="middle">30.10%</td>
<td align="left" valign="middle">H6a supported</td>
</tr>
<tr>
<td align="left" valign="middle">EA</td>
<td align="left" valign="middle">SR</td>
<td align="center" valign="middle">0.208 (7.433)</td>
<td align="center" valign="middle">0.095 (3.380)</td>
<td align="center" valign="middle">0.303</td>
<td align="center" valign="middle">31.35%</td>
<td align="left" valign="middle">H6b supported</td>
</tr>
<tr>
<td align="left" valign="middle">RA</td>
<td align="left" valign="middle">SC</td>
<td align="center" valign="middle">0.144 (4.662)</td>
<td align="center" valign="middle">0.043 (2.030)</td>
<td align="center" valign="middle">0.187</td>
<td align="center" valign="middle">23.00%</td>
<td align="left" valign="middle">H7a supported</td>
</tr>
<tr>
<td align="left" valign="middle">EA</td>
<td align="left" valign="middle">SC</td>
<td align="center" valign="middle">0.208 (7.433)</td>
<td align="center" valign="middle">0.053 (2.634)</td>
<td align="center" valign="middle">0.261</td>
<td align="center" valign="middle">20.31%</td>
<td align="left" valign="middle">H7b supported</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>VAF&#x2009;&#x003C;&#x2009;20%: no intermediary effect; 20%&#x2009;&#x003C;&#x2009;VAF&#x2009;&#x003C;&#x2009;80%: partial mediation effect.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec25">
<label>4.5.</label>
<title>Verification of the effect of chain mediation</title>
<p>The psychological process of consumers&#x2019; purchase intention is complex, and multiple mediating variables are often needed to more clearly explain the effect of independent variables on dependent variables (<xref ref-type="bibr" rid="ref56">MacKinnon, 2012</xref>). Through data analysis (<xref rid="tab7" ref-type="table">Table 7</xref>), this study found that the influence of host language appeal on users&#x2019; purchase intention is realized through two ways: &#x201C;Rational appeal&#x2014;self-referencing&#x2014;self-brand congruity&#x2014;purchase intention&#x201D; and &#x201C;emotional appeal&#x2014;self-referencing&#x2014;self-brand congruity&#x2014;purchase intention,&#x201D; assuming that H8a and H8b are valid, the VAF is 21.31 and 22.39%, respectively, that is, the mediating variables self-referencing and self-brand congruity play a chain mediating role.</p>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Analysis results of chain mediation effect.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Independent variables</th>
<th align="left" valign="top" colspan="2">Mediation variables</th>
<th align="left" valign="top">Dependent variable</th>
<th align="center" valign="top">Direct effects (<italic>T</italic> statistics)</th>
<th align="center" valign="top">Indirect effects (<italic>T</italic> statistics)</th>
<th align="center" valign="top">Total effects</th>
<th align="center" valign="top">VAF</th>
<th align="left" valign="top">Results</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">RA</td>
<td align="left" valign="middle" rowspan="2">SR</td>
<td align="left" valign="middle" rowspan="2">SC</td>
<td align="left" valign="middle" rowspan="2">PI</td>
<td align="center" valign="top">0.144 (4.662)</td>
<td align="center" valign="top">0.039 (3.308)</td>
<td align="center" valign="top">0.183</td>
<td align="center" valign="top">21.31%</td>
<td align="left" valign="top">H8a supported</td>
</tr>
<tr>
<td align="left" valign="middle">EA</td>
<td align="center" valign="top">0.208 (7.433)</td>
<td align="center" valign="top">0.060 (3.923)</td>
<td align="center" valign="top">0.268</td>
<td align="center" valign="top">22.39%</td>
<td align="left" valign="top">H8b supported</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>VAF&#x2009;&#x003C;&#x2009;20%: no intermediary effect; 20%&#x2009;&#x003C;&#x2009;VAF&#x2009;&#x003C;&#x2009;80%: partial mediation effect.</p>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="sec26" sec-type="discussions">
<label>5.</label>
<title>Discussion</title>
<sec id="sec27">
<label>5.1.</label>
<title>Research conclusions</title>
<p>This paper studies the influence mechanism of anchors&#x2019; language demands on users&#x2019; purchase intention in the scenario of live streaming e-commerce, as well as the role of self-referencing and self-brand congruity. After data analysis and structural equation model verification, we draw the following conclusions.</p>
<sec id="sec28">
<label>5.1.1.</label>
<title>Anchors&#x2019; language appeals have a positive impact on users&#x2019; self-referencing</title>
<p>The validity of hypotheses H1a and H1b indicates that the rational and emotional demands of anchors have a significant positive influence on users&#x2019; self-referencing. After receiving the information about the rational and emotional demands of anchors, users will compare these information with their own relevant experiences. <xref ref-type="bibr" rid="ref82">Sun et al. (2019)</xref> showed that rational appeal from anchors increases consumers&#x2019; informational perceptions, triggering them to refer to factual information about the brand with their own experiences and influencing their subsequent purchase behavior, while <xref ref-type="bibr" rid="ref24">Dong and Wang (2018)</xref> showed that emotional appeal from anchors increases the emotional connection with users, facilitating them to associate themselves with emotional information about the brand and increasing their favorability toward the brand. Increase users&#x2019; favorable perception of the brand. It is worth noting that according to the path coefficients, the path coefficient of the influence of the anchor&#x2019;s language rational appeal on self-referencing is 0.241, while the path coefficient of the influence of the emotional appeal on self-referencing is 0.371, indicating that the influence of the emotional appeal is more significant than that of the rational appeal. This is consistent with research on online sales (<xref ref-type="bibr" rid="ref1">Ahn et al., 2022</xref>). This result explains some of the impulsive consumption behavior that occurs in the broadcast room.</p>
</sec>
<sec id="sec29">
<label>5.1.2.</label>
<title>Anchors&#x2019; language appeals have a positive impact on users&#x2019; self-brand congruity</title>
<p>The establishment of H2a and H2b holds that anchors&#x2019; rational and emotional appeal have a positive impact on users&#x2019; self-brand congruity. In live e-commerce shopping, users are influenced by the linguistic appeals of the anchor, and their perception of the brand image changes, believing that the brand image described by the anchor through language is in line with their self-image. Once the user develops this perception of convergence between self-image and brand image, a self-brand congruence effect occurs. The results showed that the path coefficients of rational appeal and emotional appeal on self-brand congruity were 0.135 and 0.165, respectively, with no significant difference. This result shows that, no matter whether the host uses a rational way or an emotional way, users will compare the information with their self-image after receiving the information. Therefore, the language appeal of anchors should adopt a combination of rationality and sensibility, so as to generate self-brand consistency among users under the joint action of both. This conclusion is consistent with the research results of <xref ref-type="bibr" rid="ref14">Chen et al. (2022)</xref>.</p>
</sec>
<sec id="sec30">
<label>5.1.3.</label>
<title>Self-referencing and self-brand congruity have a positive impact on users&#x2019; purchase intentions</title>
<p>The validity of hypothesis H3, that self-referencing has a positive effect on users&#x2019; purchase intention, suggests that, after receiving recommendations from anchors, users will process information related to their self-concept and the resulting results will influence their consumption decisions. Consumers&#x2019; purchases are often motivated by a desire to obtain symbolic meaning for a product or service, and the self-reference effect arises when the symbolic meaning of a product is consistent with the consumer&#x2019;s existing or desired sense of self (<xref ref-type="bibr" rid="ref45">Lee and Heo, 2016</xref>; <xref ref-type="bibr" rid="ref47">Lee and Mackert, 2017</xref>; <xref ref-type="bibr" rid="ref17">de Graaf, 2022</xref>). Under the self-reference effect, consumers will tend to purchase products that are consistent with their self-identity or status. Therefore, anchors should strive to align the products they recommend with the user&#x2019;s sense of self through verbal appeals. This is consistent with the results of <xref ref-type="bibr" rid="ref88">Yaniv et al. (2011)</xref>.</p>
<p>The validity of hypothesis H4, that self-brand congruity has a positive effect on users&#x2019; purchase intentions, suggests that users who receive recommendations from anchors process them in relation to their self-image, and the resulting results influence their consumption decisions. Anchors establish some connection between their recommended brands and users through language appeals. The higher the consistency between self-image and brand image of users, the higher their purchase intention will be. This result is consistent with the findings of <xref ref-type="bibr" rid="ref31">Gr&#x00E9;nman et al. (2019)</xref> and <xref ref-type="bibr" rid="ref3">Aw et al. (2019)</xref>, which is better explains that in the practice of live streaming e-commerce, anchors constantly interact and communicate with users through language to enhance their understanding of users. The purpose is to establish a connection with the recommended products according to the self-image cognition of users, form a self-brand consistency, and then enhance the purchase intention of users.</p>
</sec>
<sec id="sec31">
<label>5.1.4.</label>
<title>Self-referencing have a positive impact on self-brand congruity</title>
<p>The validity of hypothesis H5, that self-referencing positively influences users&#x2019; self-brand congruity, suggests that after receiving a recommendation from the anchor, users first compare it with their self-experience for referencing, then make a comparison of congruity between their self-image and brand image. Users of live e-commerce are influenced by the linguistic appeals of the anchor in watching the live broadcast, creating a self-referential effect by connecting with information that already exists. Under the self-referencing effect, the user&#x2019;s self-image perception will in turn influence product (brand) preference, i.e., the user tends to choose products (brands) that match their self-image, thus building self-brand consistency. The higher this self-brand consistency is, the more likely they are to buy. This is consistent with the findings of <xref ref-type="bibr" rid="ref59">Mehta (1999)</xref>. This result tells us that in the practice of live e-commerce, the anchor should trigger the user&#x2019;s self-reference through verbal appeals, forming a perception and construction of self-image and being able to associate self-image with brand image, generating self-brand congruence.</p>
</sec>
<sec id="sec32">
<label>5.1.5.</label>
<title>Self-referencing and self-brand congruity mediate between language appeals and purchase intentions</title>
<p>The validity of hypotheses H6a and H6b, that self-referencing plays a partially mediating role between anchors&#x2019; language appeals and users&#x2019; purchase intentions, suggests that anchors recommend products to users by means of rational appeal and emotional appeal, and these messages cause users to engage in self-referential processing, and when self-referential congruity is high, users&#x2019; purchase intentions are also high. This result, is in line with the studies of <xref ref-type="bibr" rid="ref12">Chang and Lee (2011)</xref> and <xref ref-type="bibr" rid="ref2">Ahn et al. (2017)</xref>. This result suggests that the linguistic appeal of the anchor prompts users to recall information about their selves in their memories, that they associate this information about their self-image with the product recommended by the anchor, and that they are inclined to buy the product when they find that the symbolic meaning of the product recommended by the anchor is consistent with the self-image that the consumer already has or wishes to acquire.</p>
<p>The validity of hypotheses H7a and H7b, that self-brand congruity partially mediates the relationship between anchors&#x2019; language appeals and users&#x2019; purchase intentions, suggests that anchors recommend products to users by means of rational and emotional appeal, and that this information causes users to engage in comparative processing of self-brand congruity, and that when congruence is high, users&#x2019; purchase intentions are also high. When users buy commodities in the broadcast room, they are not only based on quality, price and practical performance, but also whether the brand characteristics are in line with self-image as an important selection criteria (<xref ref-type="bibr" rid="ref71">Phua and Kim, 2018</xref>; <xref ref-type="bibr" rid="ref85">Wijnands and Gill, 2020</xref>). If users can find the consistency between the brand and self-image or evaluation from the anchor language appeal, that is, the self-brand consistency is high, consumers will buy the product. This result is in line with the studies by <xref ref-type="bibr" rid="ref79">Sop and Kozak (2019)</xref> and <xref ref-type="bibr" rid="ref36">Holmes (2021)</xref>.</p>
</sec>
<sec id="sec33">
<label>5.1.6.</label>
<title>The chain mediating role of self-referencing and self-brand congruity</title>
<p>Hypotheses H8a and H8b, where self-referencing and self-brand congruity play a chain mediating role between the anchor&#x2019;s language appeal and the user&#x2019;s purchase intention, are established. The study shows that there is a relationship in which the anchor&#x2019;s language appeal triggers self-referential processing by the user, and when self-referential consistency is high, the user also makes a comparison of self-brand congruity, and when the user feels that self-brand congruity is high, his or her purchase intention is also high. This result is in line with the study of <xref ref-type="bibr" rid="ref71">Phua and Kim (2018)</xref>. The existence of chain mediation suggests a complex process from the verbal stimulation of the anchor to the generation of willingness to purchase, with a series of psychological changes that occur in between, and suggests that there is a complex processing of product information and purchase decision making behavior in live shopping.</p>
</sec>
</sec>
<sec id="sec34">
<label>5.2.</label>
<title>Theoretical implications</title>
<list list-type="order">
<list-item><p>The &#x201C;black box&#x201D; of consumer purchasing behavior refers to the fact that before consumers make purchases, merchants do not understand the mechanism of consumers&#x2019; purchasing behavior and purchase intention (<xref ref-type="bibr" rid="ref61">Michon and Chebat, 2008</xref>). This study constructs a model of the mechanism of the influence of anchors&#x2019; language appeals on users&#x2019; purchase intention, which deepens our knowledge of the mechanism involved and enriches theoretical studies of consumer behavior. Revealing the &#x201C;black box&#x201D; consumers buy in the marketing mode of live streaming e-commerce is an innovative exploration of the black box consumers buy in live streaming e-commerce.</p></list-item>
<list-item><p>This study finds that self-referencing and self-brand congruity play an important role in promoting users&#x2019; purchase intention in the live broadcast. As <xref ref-type="bibr" rid="ref46">Lee et al. (2020)</xref> and <xref ref-type="bibr" rid="ref87">Yang (2018)</xref> showed, self-reference and self-brand congruence play a mediating role between external stimuli and consumers&#x2019; purchase intentions, and we further sub-found that these two factors are also important mediators of consumer roles in live e-commerce. The findings can provide theoretical references for the design of anchors&#x2019; communication discourse to users.</p></list-item>
<list-item><p>This study constructs a new research model with good explanatory power based on the SOR theoretical framework, which broadens the applicable scenarios of the SOR theoretical framework.</p></list-item>
</list>
</sec>
<sec id="sec35">
<label>5.3.</label>
<title>Practical significance</title>
<list list-type="order">
<list-item><p>Anchors&#x2019; rational and emotional appeal are able to trigger users&#x2019; processing behavior of product information, which indicates that anchors should consider the rational and emotional appeal strategies when planning live communication discourse. The use of rational and emotional appeal strategies should be taken into account in the planning of live communication, neither can be neglected. The impact of emotional appeal is more significant. Therefore, anchors should pay emotional appeal strategies to recommend to users in the live broadcast room.</p></list-item>
<list-item><p>The study reveals the important role of self-reference effect in live marketing. The significant role of self-referencing in the model suggests that anchors should focus on stimulating consumers to produce self-referencing. On the one hand this will help promote consumers&#x2019; awareness of the product brand, on the other hand this can also promote consumers&#x2019; willingness to buy.</p></list-item>
<list-item><p>The study found that live marketing should focus on brand promotion at the same time when promoting products. The significant role of self-brand congruity in the model suggests that anchors should focus on self-brand congruity assessment of the product when designing promotion tactics, which can help enhance users&#x2019; purchase intention.</p></list-item>
</list>
</sec>
<sec id="sec36">
<label>5.4.</label>
<title>Limitations and future research</title>
<p>As with any research, this study has some limitations.</p>
<p>First, this study is based on convenience sampling for data collection, although this method has been widely adopted by the academic community for its convenience and low cost advantages. To draw more general conclusions, researchers may conduct surveys in different countries or regions under various situations in the future.</p>
<p>Second, all of the data in this study was collected by way of self-reporting, which may involve a subjective bias. Future studies should attempt to use different methods (e.g., psychological experiments, internet ethnography) and different types of data (e.g., objective data) to improve the results&#x2019; validity.</p>
<p>Third, we tested the mediating of self-referencing and self-brand congruity, and more work is thus needed to validate other factors which may also act as mediators.</p>
</sec>
</sec>
<sec id="sec37" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="sec38">
<title>Ethics statement</title>
<p>According to national legislation and institutional requirements, this study does not require written informed consent.</p>
</sec>
<sec id="sec39">
<title>Author contributions</title>
<p>EM: conceptualization, funding acquisition, project administration, resources, and supervision. EM and JL: data curation, investigation, and writing&#x2014;original draft. KL and JL: formal analysis and methodology. EM and KL: validation and writing&#x2014;review and editing. JL: visualization. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="sec40" sec-type="funding-information">
<title>Funding</title>
<p>This work was supported by National Social Science Foundation Project: &#x201C;Research on Advertising Algorithm Trap and its Governance&#x201D; (22AXW009) and Henan University Philosophy and Social Science Application Research Major Project: &#x201C;Research on Internet Platform Algorithm Governance in the Digital Economy Era&#x201D; (2023-YYZD-23).</p>
</sec>
<sec id="conf1" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="sec100" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<sec id="sec42" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1109092/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1109092/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.DOCX" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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