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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Sustain. Food Syst.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Sustainable Food Systems</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Sustain. Food Syst.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2571-581X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fsufs.2025.1604602</article-id><article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading"><subject>Original Research</subject></subj-group>
</article-categories>
<title-group>
<article-title>Will food quality and safety issues hurt consumer behavior toward pro-social agrifood consumption?</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Yu</surname>
<given-names>Yuyu</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/3023366"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="methodology" vocab-term-identifier="https://credit.niso.org/contributor-roles/methodology/">Methodology</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &#x0026; editing</role>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Meng</surname>
<given-names>Xiaozhi</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="software" vocab-term-identifier="https://credit.niso.org/contributor-roles/software/">Software</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label><institution>College of Economics, Guizhou University</institution>, <city>Guiyang</city>, <country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Guizhou Development System to Guarantee High-end Think Tanks</institution>, <city>Guiyang</city>, <country country="cn">China</country></aff>
<author-notes><corresp id="c001"><label>&#x002A;</label>Correspondence: Yuyu Yu, <email xlink:href="mailto:yuyuyu85181988@163.com">yuyuyu85181988@163.com</email></corresp></author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-12-24">
<day>24</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>9</volume>
<elocation-id>1604602</elocation-id>
<history>
<date date-type="received">
<day>07</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>03</day>
<month>11</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Yu and Meng.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Yu and Meng</copyright-holder>
<license><ali:license_ref start_date="2025-12-24">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<p>Consumer behavior is essential in aiding socially responsible agricultural producers, as customers gain satisfaction from enhancing public welfare through their purchases. This study examines the influence of quality and safety concerns on customers&#x2019; propensity to buy altruistic pro-poor agrifoods, utilizing poverty alleviation agrifoods in China as a case study. A survey was administered to Chinese consumers to assess their responses. The empirical investigation indicates that experiencing quality and safety issues when acquiring poverty-alleviation agrifoods results in a 12% decrease in repurchase intention. Furthermore, customers who possess heightened concerns regarding brand and product evaluation can more effectively alleviate the adverse effects of quality and safety difficulties on their intention to repurchase. Consumer altruism mitigates the adverse effects of quality and safety apprehensions on consumers&#x2019; propensity for repeat purchases. The research indicated that the quality and safety of fruits and vegetables markedly diminished consumers&#x2019; propensity to repurchase. The quality and safety of cereals, oils, tea, fresh meat, and fresh milk did not substantially influence repurchase intent. These findings improve understanding of altruistic and pro-social consumer behavior and provide significant insights for revitalizing public welfare and encouraging altruistic consumer behavior among individuals impacted by quality and safety concerns.</p>
</abstract>
<kwd-group>
<kwd>food quality and safety issues</kwd>
<kwd>consumers</kwd>
<kwd>agrifood</kwd>
<kwd>altruistic</kwd>
<kwd>consumer behavior</kwd>
</kwd-group><funding-group><funding-statement>The author(s) declare that financial support was received for the research and/or publication of this article. This article received support from the China Social Science Youth Fund project &#x201C;Research on the Synergistic Mechanism for Innovative Transformation of Traditional Cultural Resources and Comprehensive Rural Revitalization in Southwest Ethnic Regions (Project No. 25CMZ029)&#x201D; and the Guizhou Provincial Department of Agriculture and Rural Affairs Rural Project &#x201C;Study on the Effects of Participatory Approaches in Promoting Rural Construction and Development under the World Bank-Financed Guizhou Green Agriculture and Rural Revitalization Project (Project No.: GZSJ-FW-2025-05)&#x201D;.</funding-statement></funding-group>
<counts>
<fig-count count="1"/>
<table-count count="8"/>
<equation-count count="5"/>
<ref-count count="50"/>
<page-count count="15"/>
<word-count count="10164"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Agricultural and Food Economics</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>Despite the absence of tangible personal advantages, consumers&#x2019; altruistic, pro-social, and pro-environmental acts yield a warm glow that enhances their utility. Rational economic theory posits that individuals&#x2019; conduct primarily hinges on the maximization of utility (<xref ref-type="bibr" rid="ref12">Fuller et al., 2022</xref>). Individuals&#x2019; socially advantageous and environmentally conscious actions may not inherently confer personal benefits. Nonetheless, values, beliefs, or social contexts may compel consumers to participate in altruistic, socially advantageous, and environmentally supportive consumption. When individuals contribute to the community, a sense of warmth is linked to altruism, which manifests in altruistic conduct (<xref ref-type="bibr" rid="ref2">Andreoni, 1989</xref>). Prestige, social acknowledgment, and affirmative emotions drive individuals to contribute to or endorse social and environmental initiatives. Nonetheless, when altruistic activity in consumers results in personal harm and loss, can it influence their altruistic tendencies and diminish the warmth typically associated with such behavior? This study investigates whether consumers&#x2019; altruistic behavior is deterred when they face quality and safety concerns that adversely affect them when engaging in altruistic actions toward pro-poor agrifoods.</p>
<p>The consumption of items bearing sustainable quality labels might facilitate the development of impoverished regions and marginalized groups, hence potentially eradicating poverty. The globe confronts the challenge of poverty (<xref ref-type="bibr" rid="ref29">Liu et al., 2017</xref>; <xref ref-type="bibr" rid="ref40">Tollefson, 2015</xref>), making its eradication a worldwide objective, with the United Nations designating it as one of the 2030 Sustainable Development Goals. Poverty eradication has yielded some outcomes (<xref ref-type="bibr" rid="ref23">Hulme and Shepherd, 2003</xref>; <xref ref-type="bibr" rid="ref28">Liao et al., 2021</xref>; <xref ref-type="bibr" rid="ref37">Schotte et al., 2018</xref>). Due to the rapid expansion of emerging economies and widening socioeconomic disparities, the 2024 World Bank report indicates that nearly 700 million people worldwide currently live on less than $2.15 per day, accounting for 8.5% of the global population. It is projected that by 2030, 7.3% of the population will still be living in extreme poverty. Some middle-to-high-income consumers wish to support impoverished farmers by purchasing food that they produce (<xref ref-type="bibr" rid="ref1">Alm&#x00E5;s, 2012</xref>). Consequently, a proficient way to alleviate poverty is to connect impoverished producers with consumers (<xref ref-type="bibr" rid="ref4">Barham and Weber, 2012</xref>; <xref ref-type="bibr" rid="ref39">Skarmeas et al., 2020</xref>). Certified sustainability labels can safeguard marginalized producers by guaranteeing them elevated prices for their products. For instance, through fair trade labeling, customers are inclined to pay elevated rates to enterprises to guarantee that producers receive equitable compensation (<xref ref-type="bibr" rid="ref17">Grunert et al., 2014</xref>; <xref ref-type="bibr" rid="ref41">Valkila et al., 2010</xref>).</p>
<p>In China, pro-poor agrifoods, similar to fair trade-labeled products, support low-income producers and are seen as ethical commodities (<xref ref-type="bibr" rid="ref19">Hindsley et al., 2020</xref>). Consumers inclined to assist marginalized producers can indicate their preference by acquiring pro-poor items (<xref ref-type="bibr" rid="ref27">Lee et al., 2015</xref>; <xref ref-type="bibr" rid="ref46">Yang et al., 2012</xref>). Moreover, companies that actively engage in fulfilling their social duties receive consumer support through the consumption of their products (<xref ref-type="bibr" rid="ref22">Howie et al., 2018</xref>). Although current research emphasizes consumers&#x2019; willingness to pay a premium and methods to increase this premium for pro-poor agrifoods (<xref ref-type="bibr" rid="ref25">Jiang et al., 2023</xref>; <xref ref-type="bibr" rid="ref49">Zhang et al., 2023</xref>), the enduring character of ethical and socially responsible consumer behaviors is crucial for attaining the intended results. Consistent procurement at a premium is essential for pro-poor agrifoods to offer enduring support to low-income producers. Currently, additional study is required on the repurchase behavior of pro-poor agrifoods and further examination of the propensity to repurchase linked to the consumption experience of these goods. This study examines the impact of quality and safety characteristics on consumers&#x2019; repurchase intentions while investigating the roles of consumer information and altruism.</p>
<p>Existing research has explored the impact of food quality and safety issues on consumer behavior. Food safety is a primary determinant influencing the purchase and consumption of edible products (<xref ref-type="bibr" rid="ref18">Hena et al., 2021</xref>; <xref ref-type="bibr" rid="ref34">Omari and Frempong, 2016</xref>; <xref ref-type="bibr" rid="ref21">Horsk&#x00E1; et al., 2011</xref>; <xref ref-type="bibr" rid="ref6">Behrens et al., 2009</xref>). Consumers primarily consider chemical, microbiological, and technical issues, as well as product origin, when assessing food quality and safety (<xref ref-type="bibr" rid="ref42">van Putten et al., 2005</xref>). It is widely recognized that consumers reduce their willingness and behavior to consume foods that may cause health-related problems with long-term consumption (<xref ref-type="bibr" rid="ref33">Olsen and Tuu, 2017</xref>; <xref ref-type="bibr" rid="ref14">Geeroms et al., 2008</xref>). Concurrently, Hoek et al. specifically emphasize that government regulatory bodies responsible for formulating food laws and regulations should prioritize the health-related attributes of food (<xref ref-type="bibr" rid="ref20">Hoek et al., 2017</xref>). For instance, studies revealed that following media coverage of Japan&#x2019;s radioactive leakage incident, consumer purchasing behavior declined due to food safety concerns. Notably, many consumers reduced their willingness to purchase seafood, driven by fears of food safety issues (<xref ref-type="bibr" rid="ref26">Kyung et al., 2025</xref>). Although previous studies have examined how food quality and safety issues reduce consumption behavior and willingness to consume, no research has investigated the impact of such issues on consumption behavior toward altruistic products.</p>
<p>This study makes two contributions. Initially, it explores the propensity to repurchase products possessing ethical characteristics, namely pro-poor items. Presently, pertinent research on these products, including pro-environmental behaviors, fair trade, and pro-poor products, predominantly focuses on the assessment of willingness to pay and purchasing behaviors, with limited examination of repurchase willingness. Secondly, it is feasible to assess if customers&#x2019; motivation diminishes when their interests are undermined in the use of ethical products. This study examines whether the altruistic act of purchasing pro-poor agrifoods counteracts the self-defeating behaviors related to quality and safety that deter consumers. It also examines methods to enhance quality and safety concerns after diminishing consumers&#x2019; incentive for pro-poor agrifoods.</p>
</sec>
<sec id="sec2">
<label>2</label>
<title>Theoretical analysis and hypothesis formulation</title>
<sec id="sec3">
<label>2.1</label>
<title>Theoretical analysis</title>
<p>Quality and safety concerns in agrifoods present intrinsic hazards due to unpredictability, potentially resulting in food waste and increased healthcare costs. When customers identify such faults and consider the product inedible, they are likely to dispose of it, hence increasing the total cost of food consumption. Consuming products with quality and safety issues can exacerbate healthcare costs. Consequently, worries regarding quality and safety in pro-poor agrifoods can lower consumers&#x2019; incomes, thereby reducing purchases, consumption, and utility theories. The income effect resulting from these risks causes alterations in customers&#x2019; purchasing patterns to conform to revised budget limitations. Individuals endeavor to optimize utility through their consumption patterns; hence, when confronted with quality and safety hazards in pro-poor agrifoods, they may pursue alternatives that offer superior utility. Ultimately, the elevated costs and the ensuing income and substitution impacts lead to a diminished demand for pro-poor agrifoods.</p>
<p>Assuming customers exclusively purchase conventional agrifoods g and pro-poor agrifoods f, with their income being constant at I1 in the short term, quality and safety issues induce substitution and income effects, hence diminishing consumer demand. As illustrated in <xref ref-type="fig" rid="fig1">Figure 1</xref>, the escalation in costs attributable to quality and safety issues alters the consumers&#x2019; effective budget line from I1 to I2. The demand for pro-poor agrifoods transitions from f1 to f2, indicating a general decline in consumer purchasing desire due to income and substitution impacts. This is exemplified by the transition from point A to point B on the utility maximization curve. A compensation budget line I3 is established using the tangent translation of I2 and U1, where f2f3 denotes the income effect, and f3f1 signifies the substitution effect. <xref ref-type="fig" rid="fig1">Figure 1</xref> illustrates that quality and safety issues exert an &#x201C;income effect&#x201D; and a &#x201C;substitution effect&#x201D; on consumers, diminishing their propensity to repurchase.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Income and substitution effects due to quality and safety problems of pro-poor agrifoods.</p>
</caption>
<graphic xlink:href="fsufs-09-1604602-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Graph showing an indifference curve analysis. Curves U1 and U2 demonstrate utility levels with axes labeled as g and f. Points a, b, and c indicate different consumption combinations. Income effect and substitution effect are illustrated between lines f1, f2, and f3.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>Research hypothesis</title>
<p>Food quality and safety constitute integral components of the consumer experience; generally, customers favor high-quality products, with quality and safety being pivotal variables influencing their food choices and decisions (<xref ref-type="bibr" rid="ref11">Feng et al., 2012</xref>; <xref ref-type="bibr" rid="ref15">Grunert, 2005</xref>; <xref ref-type="bibr" rid="ref36">R&#x00F6;hr et al., 2005</xref>). The prevalence of food quality and safety issues markedly heightens consumer apprehensions regarding products (<xref ref-type="bibr" rid="ref47">Yin et al., 2016</xref>), leading consumers to perceive an elevated risk associated with pro-poor edible goods when purchasing such items following experiences with quality and safety concerns. Due to risk aversion, consumers will exercise greater caution in their selections and decisions about pro-poor agrifoods. The consumer experience is crucial to product consumption as it affects the enduring relationship between the product and its use (<xref ref-type="bibr" rid="ref38">Siqueira et al., 2019</xref>). In instances of recurring purchases, the consumer&#x2019;s utility function accounts for the &#x201C;memory effect&#x201D; (<xref ref-type="bibr" rid="ref48">Yuan et al., 2023</xref>). The recollection of product consumption and its distinction from competing products influences consumer utility; specifically, the utility derived from the initial consumption experience impacts the decision to repurchase the product (<xref ref-type="bibr" rid="ref35">Robson and Samuelson, 2011</xref>; <xref ref-type="bibr" rid="ref44">Warlop et al., 2005</xref>). Consequently, consumers acquiring pro-poor agrifoods face quality and safety issues, leading to a negative experience that diminishes the utility derived from their consumption. This reduction in decision-making utility subsequently decreases consumers&#x2019; willingness to repurchase. Consequently, the first hypothesis is posited:</p>
<disp-quote>
<p><italic>H1</italic>: There is a negative impact of quality and safety issues on consumer purchases of pro-poor agrifoods.</p>
</disp-quote>
<p>Consumer purchase intention is also affected by information (<xref ref-type="bibr" rid="ref45">Xu et al., 2023</xref>); due to information asymmetry, consumers are unable to understand the product situation fully and may waver on the purchase decision. The professionalism and trustworthiness of information can increase consumer intention by promoting consumer confidence, and information can affect consumer purchase intention by influencing consumer perception (<xref ref-type="bibr" rid="ref32">Meng et al., 2021</xref>). Consumer quality and safety evaluation attention and brand attention are the degree of access to and attention to evaluation and brand information. Quality and safety evaluation attention is the way consumers search for information. Social media has become an indispensable communication medium through which consumers seek product information and discuss and share their consumption experiences. As a result, consumer evaluations can also serve as endorsements or recommendations to other customers, which in turn influence the purchase intentions of these customers (<xref ref-type="bibr" rid="ref43">Wang et al., 2012</xref>). On the one hand, consumers&#x2019; attention to product evaluation will weaken their experience memory, and they will form new product perceptions of the purchased pro-poor agrifoods, which will provide information for the decision-making of the next purchase, thus affecting the consumers&#x2019; willingness to repurchase. Therefore, hypothesis 2 is proposed:</p>
<disp-quote>
<p><italic>H2</italic>: Attention to quality and safety evaluation weakens the negative impact of quality and safety issues on consumers&#x2019; purchase of pro-poor agrifoods.</p>
</disp-quote>
<p>Branding is a specific quality cue because consumers can use it to identify products (<xref ref-type="bibr" rid="ref9">Crawford, 1997</xref>; <xref ref-type="bibr" rid="ref16">Grunert, 2022</xref>). Brands have been shown to influence the perceived quality of a product significantly (<xref ref-type="bibr" rid="ref10">Dodds and Monroe, 1985</xref>; <xref ref-type="bibr" rid="ref24">Jacoby et al., 1971</xref>), e.g., as a basis for expected diet quality and expected health quality (<xref ref-type="bibr" rid="ref7">Bredahl, 2004</xref>). When consumers pay more attention to the brand, they will have a more comprehensive understanding of the information and quality of the product, which will form a new perception in combination with the experience of the pro-poor agrifoods they have purchased, attenuating the solidified memories of the previous pro-poor agrifoods they have purchased, and thus affecting the consumers&#x2019; willingness to repurchase. Hypothesis 3 is thus proposed:</p>
<disp-quote>
<p><italic>H3</italic>: Brand attention weakens the negative impact of quality and safety issues on consumers&#x2019; repurchase of pro-poor agrifoods.</p>
</disp-quote>
<p>Based on social preference theory, individuals are more willing to help or donate to lower-income people (<xref ref-type="bibr" rid="ref001">Bolton and Ockenfels, 2000</xref>; <xref ref-type="bibr" rid="ref8">Charness and Holder, 2019</xref>). So, for pro-poor edible produce, consumers are more willing to buy and pay a premium, and this altruism of giving back to the community will make them feel the warm glow effect (<xref ref-type="bibr" rid="ref2">Andreoni, 1989</xref>). The warm glow generated by repurchasing pro-poor produce is more significant for more public-spirited consumers. Therefore, the utility of purchasing pro-poor agrifoods is higher for public-spirited consumers. This can be partially offset by the reduced utility of food quality and safety issues, thus affecting consumers&#x2019; willingness to repurchase pro-poor agrifoods. Therefore, hypothesis 4 is proposed.</p>
<disp-quote>
<p><italic>H4</italic>: altruism weakens the negative impact of quality and safety problems on consumers&#x2019; repurchase of pro-poor agrifoods.</p>
</disp-quote>
</sec>
</sec>
<sec id="sec5">
<label>3</label>
<title>Research methodology and data</title>
<sec id="sec6">
<label>3.1</label>
<title>Sample and data</title>
<p>The research team collected data for the study through a consumer behavioral data survey. The questionnaire was conducted from January to March 2022 through the online questionnaire survey Questionstar platform.<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> The questionnaire was open to consumers in China, who were randomly invited to fill out the questionnaire through locations such as supermarkets and markets in municipalities directly under the central government and provincial capitals and through online dissemination. The survey employed stratified sampling based on population proportions across provinces, with random sampling conducted in each provincial capital city. The questionnaire covered consumers&#x2019; characteristics, household characteristics, and consumption behavior of pro-poor agrifoods. To address potential non-participating and inattentive participants, we excluded respondents whose questionnaires needed longer. We removed survey responses from the dataset that took less than 5&#x202F;min to complete, with the average time for all responses being 13.8&#x202F;min. Our study population was consumers who had purchased pro-poor agrifoods, so we excluded consumers who had not. In order to deal with missing values in the questionnaire, respondents with missing for many important variables in the survey were removed from the dataset. Finally, 533 observations were retained and used in the study. The sample of respondents was drawn from every province in China (except two regions, Tibet and Taiwan). The sample sources cover economically developed large and medium-sized cities and represent the study of consumer behavior. The distribution of the sample is shown in <xref ref-type="table" rid="tab1">Table 1</xref>.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Sample distribution.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Individual characteristics</th>
<th align="left" valign="top">Classifications</th>
<th align="center" valign="top">Frequency</th>
<th align="center" valign="top">Proportions (%)</th>
<th align="center" valign="top"><italic>N</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="2">Genders</td>
<td align="left" valign="top">Male</td>
<td align="center" valign="top">234</td>
<td align="char" valign="top" char=".">43.90</td>
<td align="center" valign="middle" rowspan="2">533</td>
</tr>
<tr>
<td align="left" valign="top">Female</td>
<td align="center" valign="top">299</td>
<td align="char" valign="top" char=".">56.10</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="5">Age</td>
<td align="left" valign="top">Under 25&#x202F;years</td>
<td align="center" valign="top">75</td>
<td align="char" valign="middle" char=".">13.56</td>
<td align="center" valign="middle" rowspan="5">533</td>
</tr>
<tr>
<td align="left" valign="top">25&#x2013;34&#x202F;years</td>
<td align="center" valign="top">253</td>
<td align="char" valign="middle" char=".">45.75</td>
</tr>
<tr>
<td align="left" valign="top">35&#x2013;44&#x202F;years</td>
<td align="center" valign="top">109</td>
<td align="char" valign="middle" char=".">19.71</td>
</tr>
<tr>
<td align="left" valign="top">45&#x2013;54&#x202F;years</td>
<td align="center" valign="top">79</td>
<td align="char" valign="middle" char=".">14.29</td>
</tr>
<tr>
<td align="left" valign="top">55&#x202F;years and over</td>
<td align="center" valign="top">17</td>
<td align="char" valign="middle" char=".">3.07</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Marital status</td>
<td align="left" valign="top">Unmarried</td>
<td align="center" valign="top">190</td>
<td align="char" valign="top" char=".">35.65</td>
<td align="center" valign="middle" rowspan="2">533</td>
</tr>
<tr>
<td align="left" valign="top">Married</td>
<td align="center" valign="top">343</td>
<td align="char" valign="top" char=".">64.35</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="5">Educational level</td>
<td align="left" valign="top">Secondary schools</td>
<td align="center" valign="top">6</td>
<td align="char" valign="top" char=".">1.13</td>
<td align="center" valign="middle" rowspan="5">533</td>
</tr>
<tr>
<td align="left" valign="top">Junior high school</td>
<td align="center" valign="top">22</td>
<td align="char" valign="top" char=".">4.13</td>
</tr>
<tr>
<td align="left" valign="top">Senior high school</td>
<td align="center" valign="top">35</td>
<td align="char" valign="top" char=".">6.57</td>
</tr>
<tr>
<td align="left" valign="top">University degree</td>
<td align="center" valign="top">87</td>
<td align="char" valign="top" char=".">16.32</td>
</tr>
<tr>
<td align="left" valign="top">Postgraduate or above</td>
<td align="center" valign="top">282</td>
<td align="char" valign="top" char=".">52.91</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="6">Household size</td>
<td align="left" valign="top">1 person</td>
<td align="center" valign="top">10</td>
<td align="char" valign="top" char=".">1.88</td>
<td align="center" valign="middle" rowspan="6">533</td>
</tr>
<tr>
<td align="left" valign="top">2 person</td>
<td align="center" valign="top">35</td>
<td align="char" valign="top" char=".">6.57</td>
</tr>
<tr>
<td align="left" valign="top">3 person</td>
<td align="center" valign="top">188</td>
<td align="char" valign="top" char=".">35.27</td>
</tr>
<tr>
<td align="left" valign="top">4 person</td>
<td align="center" valign="top">126</td>
<td align="char" valign="top" char=".">23.64</td>
</tr>
<tr>
<td align="left" valign="top">5 person</td>
<td align="center" valign="top">103</td>
<td align="char" valign="top" char=".">19.32</td>
</tr>
<tr>
<td align="left" valign="top">5 person and over</td>
<td align="center" valign="top">71</td>
<td align="char" valign="top" char=".">13.34</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="8">Monthly per capita household income</td>
<td align="left" valign="top">Less than 3,000 RMB</td>
<td align="center" valign="top">27</td>
<td align="char" valign="top" char=".">5.07</td>
<td align="center" valign="middle" rowspan="8">533</td>
</tr>
<tr>
<td align="left" valign="top">3,001&#x2013;6,000 RMB</td>
<td align="center" valign="top">142</td>
<td align="char" valign="top" char=".">26.64</td>
</tr>
<tr>
<td align="left" valign="top">6,001&#x2013;9,000 RMB</td>
<td align="center" valign="top">107</td>
<td align="char" valign="top" char=".">20.08</td>
</tr>
<tr>
<td align="left" valign="top">9,001&#x2013;12,000 RMB</td>
<td align="center" valign="top">93</td>
<td align="char" valign="top" char=".">17.45</td>
</tr>
<tr>
<td align="left" valign="top">12,001&#x2013;15,000 RMB</td>
<td align="center" valign="top">72</td>
<td align="char" valign="top" char=".">13.51</td>
</tr>
<tr>
<td align="left" valign="top">15,001&#x2013;18,000 RMB</td>
<td align="center" valign="top">24</td>
<td align="char" valign="top" char=".">4.50</td>
</tr>
<tr>
<td align="left" valign="top">18,001&#x2013;21,000 RMB</td>
<td align="center" valign="top">21</td>
<td align="char" valign="top" char=".">3.94</td>
</tr>
<tr>
<td align="left" valign="top">Above RMB 21000</td>
<td align="center" valign="top">47</td>
<td align="char" valign="top" char=".">8.82</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The research sample includes more women than men because most agrifoods home buyers are women. More than 60% of the sample is married, and a significantly higher proportion is highly educated, which may be related to the fact that the survey was in municipalities and provincial capitals. Household size was concentrated between three and five members, and household income was between 3,000 and 15,000 yuan.</p>
</sec>
<sec id="sec7">
<label>3.2</label>
<title>Variable selection</title>
<sec id="sec8">
<label>3.2.1</label>
<title>Explanatory variables</title>
<p>The primary explanatory variable of this study is the quality and safety problems of pro-poor agrifoods, mainly measured by consumers&#x2019; actual experiences of buying pro-poor fruits and whether they have encountered quality and safety problems. In the questionnaire, the question &#x201C;Have you encountered the following quality and safety problems when buying pro-poor fruits?&#x201D; was used, and eight categories of quality and safety problems were set in the responses. Consumers&#x2019; responses were processed by assigning a value of 1 to the purchase of pro-poor fruits that encountered the eight categories of quality and safety problems and a value of 0 to the purchase of pro-poor fruits that encountered no quality and safety problems, and then regressing the question &#x201C;Have consumers encountered any quality and safety problems in the purchase of pro-poor agrifoods&#x201D; on the following categories: grain, oil, vegetables, fruits, meats, milk, and tea to test the robustness of the benchmark regression, the robustness of the baseline regression.</p>
</sec>
<sec id="sec9">
<label>3.2.2</label>
<title>Explained variables</title>
<p>The explanatory variable of this study is consumers&#x2019; willingness to repurchase pro-poor products. In the questionnaire, we asked, &#x201C;Will you repurchase pro-poor fruits?&#x201D; The question was set to measure the respondents&#x2019; willingness to purchase pro-poor products, which was assigned a value of &#x201C;1&#x2033; and &#x201C;0&#x2033; according to the consumers&#x2019; answers.</p>
</sec>
<sec id="sec10">
<label>3.2.3</label>
<title>Control variables</title>
<p>This study mainly controls consumers&#x2019; characteristics, family characteristics, and the location of purchasing poverty alleviation products, so gender, age, marital status, and education level were selected to reflect the individual characteristics of the respondents. Whether they live in towns and cities, the total number of people in the family, whether the family income is middle or high income, the number of children in the family, the number of elderly people in the family, and whether they are from the western region reflect the family characteristics. Finally, since the location of purchasing pro-poor agrifoods has a more significant impact on the quality of pro-poor products and consumers&#x2019; willingness to buy, the channel of purchasing pro-poor agrifoods was also controlled.</p>
</sec>
<sec id="sec11">
<label>3.2.4</label>
<title>Moderating variables</title>
<p>This study uses evaluative attention, brand attention, and philanthropy as moderating variables. Evaluation of attention is set in the questionnaire, &#x201C;How much attention do you pay to the brand?&#x201D; In the questionnaire, the question &#x201C;How much do you care about the brand?&#x201D; was set as &#x201C;Very little concern&#x201D; as 1, &#x201C;No concern&#x201D; as 2, &#x201C;Average concern&#x201D; as 3, &#x201C;More concern&#x201D; as 4, and &#x201C;Very much concern&#x201D; as 5. &#x201C;How much do you care about other people&#x2019;s appraisal?&#x201D; was set as &#x201C;Brand concern.&#x201D; The brand concern in the questionnaire, &#x201C;How much do you care about other people&#x2019;s evaluation?&#x201D; is assigned as 1, 2, 3, 4, 5, and 5, respectively (see <xref ref-type="table" rid="tab2">Table 2</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Descriptive statistics.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="left" valign="top">Descriptions</th>
<th align="center" valign="top"><italic>N</italic></th>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">S.E.</th>
<th align="center" valign="top">Min</th>
<th align="center" valign="top">Max</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Repurchase intention</td>
<td align="left" valign="middle">Willingness to repurchase pro-poor agrifoods (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.746</td>
<td align="char" valign="top" char=".">0.435</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Food safety issues</td>
<td align="left" valign="middle">Whether the purchase of pro-poor agrifoods has encountered food safety problems (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.222</td>
<td align="char" valign="top" char=".">0.415</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Genders</td>
<td align="left" valign="middle">Gender (male&#x202F;=&#x202F;1, female&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.439</td>
<td align="char" valign="top" char=".">0.497</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="left" valign="middle">Age of respondents</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">33.645</td>
<td align="char" valign="top" char=".">9.905</td>
<td align="center" valign="top">13</td>
<td align="center" valign="top">68</td>
</tr>
<tr>
<td align="left" valign="middle">Marital status</td>
<td align="left" valign="middle">Married&#x202F;=&#x202F;1, unmarried&#x202F;=&#x202F;0</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.644</td>
<td align="char" valign="top" char=".">0.479</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Educational level</td>
<td align="left" valign="middle">Educational level of respondents (years)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">15.741</td>
<td align="char" valign="top" char=".">2.452</td>
<td align="center" valign="top">6</td>
<td align="center" valign="top">19</td>
</tr>
<tr>
<td align="left" valign="middle">Residential address</td>
<td align="left" valign="middle">Whether living in town (yes&#x202F;=&#x202F;1, no&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.818</td>
<td align="char" valign="top" char=".">0.386</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Household size</td>
<td align="left" valign="middle">Total household size</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">4.004</td>
<td align="char" valign="top" char=".">1.601</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">22</td>
</tr>
<tr>
<td align="left" valign="middle">Household income</td>
<td align="left" valign="middle">Whether household income is upper-middle income (yes&#x202F;=&#x202F;1, no&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.482</td>
<td align="char" valign="top" char=".">0.5</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Child</td>
<td align="left" valign="middle">Number of 5-year-old children in households</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.296</td>
<td align="char" valign="top" char=".">0.526</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">3</td>
</tr>
<tr>
<td align="left" valign="middle">Elder</td>
<td align="left" valign="middle">Number of persons over 60&#x202F;years of age in households</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.572</td>
<td align="char" valign="top" char=".">0.834</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">4</td>
</tr>
<tr>
<td align="left" valign="middle">Purchase Channels</td>
<td align="left" valign="middle">Are the channels for purchasing pro-poor agrifoods mainly online (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.719</td>
<td align="char" valign="top" char=".">0.45</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Area</td>
<td align="left" valign="middle">Whether Western Region (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.231</td>
<td align="char" valign="top" char=".">0.422</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Product Classification</td>
<td align="left" valign="middle">Fruit&#x202F;=&#x202F;1, Vegetables&#x202F;=&#x202F;2, Grains and oils&#x202F;=&#x202F;3, Fresh meat&#x202F;=&#x202F;4, Fresh milk&#x202F;=&#x202F;5, Tea&#x202F;=&#x202F;6</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">3.263</td>
<td align="char" valign="top" char=".">1.717</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">6</td>
</tr>
<tr>
<td align="left" valign="middle">Quality and safety issues</td>
<td align="left" valign="middle">Encounter quality and safety problems (inconsistent quality standards; substandard labeling or marking; adulteration and counterfeiting; mildew, rot; excessive disease-causing microorganisms; excessive heavy metals; excessive pesticide and veterinary drug residues; and unqualified quarantine) types of summing up</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.316</td>
<td align="char" valign="top" char=".">0.827</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">8</td>
</tr>
<tr>
<td align="left" valign="middle">Brand attention</td>
<td align="left" valign="middle">Level of concern about the brand (very little concern&#x202F;=&#x202F;1, no concern&#x202F;=&#x202F;2, average&#x202F;=&#x202F;3, more concern&#x202F;=&#x202F;4, very much concern&#x202F;=&#x202F;5)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">3.908</td>
<td align="char" valign="top" char=".">1.03</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">5</td>
</tr>
<tr>
<td align="left" valign="middle">Evaluative attention</td>
<td align="left" valign="middle">Level of concern as assessed by others (very little concern&#x202F;=&#x202F;1, no concern&#x202F;=&#x202F;2, fair&#x202F;=&#x202F;3, more concern&#x202F;=&#x202F;4, very much concern&#x202F;=&#x202F;5)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">4.161</td>
<td align="char" valign="top" char=".">0.938</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">5</td>
</tr>
<tr>
<td align="left" valign="middle">Altruism</td>
<td align="left" valign="middle">Participation in donations (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">533</td>
<td align="char" valign="top" char=".">0.856</td>
<td align="char" valign="top" char=".">0.352</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Quality Standards</td>
<td align="left" valign="top">Problems with inconsistent quality standards (yes&#x202F;=&#x202F;1, no&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.077</td>
<td align="char" valign="top" char=".">0.267</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Labeling issues</td>
<td align="left" valign="top">Failed labeling or marking encountered (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.045</td>
<td align="char" valign="top" char=".">0.208</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Adulteration and Falsification</td>
<td align="left" valign="top">Experiencing quality and safety problems with adulteration (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.036</td>
<td align="char" valign="top" char=".">0.187</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Mildew and rot</td>
<td align="left" valign="top">Quality and safety problems with mold and rot (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.075</td>
<td align="char" valign="top" char=".">0.264</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Excess Pathogenic microorganisms</td>
<td align="left" valign="top">Quality and safety issues with pathogenic microorganisms (yes&#x202F;=&#x202F;1, no&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.017</td>
<td align="char" valign="top" char=".">0.13</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Heavy Metal Exceedance</td>
<td align="left" valign="top">Encountered quality and safety problems with excess heavy metals (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.018</td>
<td align="char" valign="top" char=".">0.133</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Excessive pesticides and veterinary drugs</td>
<td align="left" valign="top">Encountering quality and safety problems with excess pesticides and veterinary drugs (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.028</td>
<td align="char" valign="top" char=".">0.164</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="middle">Fail quarantine</td>
<td align="left" valign="top">Encountered quarantine failures for quality and safety (Yes&#x202F;=&#x202F;1, No&#x202F;=&#x202F;0)</td>
<td align="center" valign="top">2099</td>
<td align="char" valign="top" char=".">0.02</td>
<td align="char" valign="top" char=".">0.138</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The questionnaire survey on consumers&#x2019; willingness to purchase pro-poor agrifoods focused on pro-poor agrifoods. It is divided into six categories based on the total categories of agrifoods: cereals and oils, vegetables, fruits, meat, fresh milk, and tea. In this study, in order to investigate the impact of food quality and safety on the repurchase intention of all pro-poor food products, the data on food quality and safety conditions and repurchase intention of the six categories of food products, namely, grain and oil, vegetables, fruits, meat, fresh milk, and tea, in each returned questionnaire were summed up, and the data on each category of pro-poor food products that had not purchased excluded, so that the final data related to the quality and safety problems and repurchase intention of pro-poor food products were 2099 items. There are 2099 pieces of data related to the quality and safety of pro-poor agrifoods and willingness to repurchase, so the sample size in the descriptive statistics is different.</p>
<p>According to the descriptive statistics results, more than 22% of consumers in all categories who had purchased pro-poor agrifoods had encountered quality and safety problems with them. Moreover, according to the mean value of the variable of purchasing pro-poor agrifoods, 75% of consumers are willing to purchase pro-poor agrifoods again. It found that 75% of the consumers who purchased pro-poor agrifoods did so mainly online.</p>
</sec>
</sec>
<sec id="sec12">
<label>3.3</label>
<title>Model analysis</title>
<p>How do quality and safety issues of pro-poor agrifoods affect consumers&#x2019; willingness to repurchase? The article uses a probit model to analyze this. This model is mainly used to estimate the situation in which consumer I chooses between two mutually exclusive alternatives J. In this study, consumers&#x2019; willingness to repurchase pro-poor agrifoods under different circumstances are &#x201C;yes&#x201D; and &#x201C;no.&#x201D; In this study, consumers&#x2019; willingness to repurchase pro-poor agrifoods under different circumstances is &#x201C;yes&#x201D; and &#x201C;no.&#x201D; Based on the individual utility function assumption proposed by McFadden (<xref ref-type="bibr" rid="ref31">McFadden, 1972</xref>), it is assumed that quality and safety attributes and individual characteristics influence consumer utility. If a consumer chooses item j out of two choices, his utility function is:</p>
<disp-formula id="EQ1"><mml:math id="M1"><mml:msub><mml:mi>U</mml:mi><mml:mi mathvariant="italic">ij</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="italic">ij</mml:mi></mml:msub><mml:mo stretchy="true">(</mml:mo><mml:mi>Q</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi mathvariant="italic">ij</mml:mi></mml:msub><mml:mo stretchy="true">)</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>&#x03B5;</mml:mi><mml:mi mathvariant="italic">ij</mml:mi></mml:msub></mml:math><label>(1)</label></disp-formula>
<p>In <xref ref-type="disp-formula" rid="EQ1">Equation 1</xref>, i denotes the ith individual consumer; j denotes the jth choice in the choice set consisting of two mutually exclusive alternatives (J&#x202F;=&#x202F;0 or J&#x202F;=&#x202F;1); <inline-formula><mml:math id="M2"><mml:mi>Q</mml:mi></mml:math></inline-formula> denotes the product quality; <inline-formula><mml:math id="M3"><mml:msub><mml:mi>X</mml:mi><mml:mover><mml:mi>v</mml:mi><mml:mo>&#x00A8;</mml:mo></mml:mover></mml:msub></mml:math></inline-formula> denotes a series of individual feature vectors for the ith individual; and <inline-formula><mml:math id="M4"><mml:msub><mml:mi>&#x03B5;</mml:mi><mml:mi mathvariant="italic">ij</mml:mi></mml:msub></mml:math></inline-formula> is a randomized perturbation term.</p>
<p>Individual consumers seeking to maximize utility implies that when <inline-formula><mml:math id="M5"><mml:msub><mml:mi>U</mml:mi><mml:mi mathvariant="italic">ij</mml:mi></mml:msub><mml:mo>&#x003E;</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mi mathvariant="italic">ik</mml:mi></mml:msub><mml:mo stretchy="true">(</mml:mo><mml:mi>k</mml:mi><mml:mo>&#x2260;</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy="true">)</mml:mo></mml:math></inline-formula>, individual i will choose j. The probability that individual i chooses j can be expressed as:</p>
<disp-formula id="EQ2"><mml:math id="M6"><mml:msub><mml:mi>p</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>p</mml:mi><mml:mo stretchy="true">(</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mi mathvariant="italic">ij</mml:mi></mml:msub><mml:mo>&#x003E;</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mi mathvariant="italic">ik</mml:mi></mml:msub><mml:mo stretchy="true">)</mml:mo><mml:mo stretchy="true">(</mml:mo><mml:mi>k</mml:mi><mml:mo>&#x2260;</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy="true">)</mml:mo></mml:math><label>(2)</label></disp-formula>
<p>In <xref ref-type="disp-formula" rid="EQ2">Equation 2</xref>, <inline-formula><mml:math id="M7"><mml:msub><mml:mi>p</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></inline-formula> is the probability that consumers choose to buy pro-poor agrifoods. Quality and safety When consumers are willing to buy pro-poor agrifoods under the constraints of food quality and safety and individual characteristics, then consumers have higher utility.</p>
<p>Since consumers&#x2019; willingness to buy pro-poor food products is a (0, 1) dichotomous variable and depends on the research needs of its influence factor explanatory variables, this study uses a probit model for econometric analysis. The primary expression of the probit model is as follows:</p>
<disp-formula id="EQ3"><mml:math id="M8"><mml:msup><mml:mi>Y</mml:mi><mml:mo>&#x2217;</mml:mo></mml:msup><mml:mo>=</mml:mo><mml:mi>&#x03B1;</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant="italic">&#x03B2;X</mml:mi><mml:mo>+</mml:mo><mml:mi>&#x03BC;</mml:mi></mml:math><label>(3)</label></disp-formula>
<p>In <xref ref-type="disp-formula" rid="EQ3">Equation 3</xref>, <inline-formula><mml:math id="M9"><mml:mi>&#x03BC;</mml:mi></mml:math></inline-formula> is a perturbation term that obeys a standard normal distribution so that the binary discrete choice model that affects consumers&#x2019; purchase of pro-poor agrifoods can be expressed as follows:</p>
<disp-formula id="EQ4"><mml:math id="M10"><mml:mtable columnalign="left" equalrows="true" equalcolumns="true" displaystyle="true"><mml:mtr><mml:mtd><mml:mtext mathvariant="italic">prob</mml:mtext><mml:mo stretchy="true">(</mml:mo><mml:mi>Y</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>&#x2223;</mml:mo><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy="true">)</mml:mo><mml:mo>=</mml:mo><mml:mtext mathvariant="italic">prob</mml:mtext><mml:mo stretchy="true">(</mml:mo><mml:msup><mml:mi>Y</mml:mi><mml:mo>&#x2217;</mml:mo></mml:msup><mml:mo>&#x003E;</mml:mo><mml:mn>0</mml:mn><mml:mo>&#x2223;</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy="true">)</mml:mo><mml:mo>=</mml:mo><mml:mtext mathvariant="italic">prob</mml:mtext><mml:mo stretchy="true">[</mml:mo><mml:mo stretchy="true">(</mml:mo><mml:mi>&#x03BC;</mml:mi><mml:mo>&#x003E;</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant="italic">&#x03B2;x</mml:mi><mml:mo stretchy="true">)</mml:mo><mml:mo>&#x2223;</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy="true">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>&#x2212;</mml:mo><mml:mi>&#x03A6;</mml:mi><mml:mo stretchy="true">[</mml:mo><mml:mo stretchy="true">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant="italic">&#x03B2;x</mml:mi><mml:mo stretchy="true">)</mml:mo><mml:mo stretchy="true">]</mml:mo><mml:mo>=</mml:mo><mml:mi>&#x03A6;</mml:mi><mml:mo stretchy="true">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant="italic">&#x03B2;x</mml:mi><mml:mo stretchy="true">)</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math><label>(4)</label></disp-formula>
<p>In <xref ref-type="disp-formula" rid="EQ4">Equation 4</xref>, <inline-formula><mml:math id="M11"><mml:mi>&#x03A6;</mml:mi></mml:math></inline-formula> is the standard normal cumulative distribution function; <inline-formula><mml:math id="M12"><mml:msup><mml:mi>Y</mml:mi><mml:mo>&#x2217;</mml:mo></mml:msup></mml:math></inline-formula> is the unobservable latent variable, and <inline-formula><mml:math id="M13"><mml:mi>Y</mml:mi></mml:math></inline-formula> is the actual observed dependent variable, which is used to indicate whether consumers choose to repurchase pro-poor agrifoods; 0 means &#x201C;do not buy,&#x201D; and one means &#x201C;buy&#x201D;; <inline-formula><mml:math id="M14"><mml:mi>X</mml:mi></mml:math></inline-formula> is food quality and safety issues, and <inline-formula><mml:math id="M15"><mml:mi>x</mml:mi></mml:math></inline-formula> is the actual observed influencing factor. Therefore, the probit model of the influence of quality and safety issues on consumers&#x2019; decision to purchase pro-poor agrifoods can be built as follows:</p>
<disp-formula id="EQ5"><mml:math id="M16"><mml:mtext mathvariant="italic">prob</mml:mtext><mml:mo stretchy="true">(</mml:mo><mml:mi>Y</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>&#x2223;</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy="true">)</mml:mo><mml:mo>=</mml:mo><mml:mi>&#x03A6;</mml:mi><mml:mo stretchy="true">(</mml:mo><mml:msub><mml:mi>&#x03B1;</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mi>X</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:msub><mml:mi>X</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>&#x03B5;</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo stretchy="true">)</mml:mo></mml:math><label>(5)</label></disp-formula>
<p>In <xref ref-type="disp-formula" rid="EQ5">Equation 5</xref>, <inline-formula><mml:math id="M17"><mml:mtext mathvariant="italic">prob</mml:mtext><mml:mo stretchy="true">(</mml:mo><mml:mi>Y</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>&#x2223;</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy="true">)</mml:mo></mml:math></inline-formula> is the probability that a consumer will purchase a pro-poor product (i.e., Y&#x202F;=&#x202F;1). <inline-formula><mml:math id="M18"><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></inline-formula> is the independent variable; <inline-formula><mml:math id="M19"><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></inline-formula> indicates whether the purchase of pro-poor agrifoods has encountered a food safety problem; <inline-formula><mml:math id="M20"><mml:msub><mml:mi>X</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math></inline-formula> represents the control variables, consumer personal characteristics, household characteristics, and purchasing channel; <inline-formula><mml:math id="M21"><mml:msub><mml:mi>&#x03B1;</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math></inline-formula> is the parameter to be estimated; <inline-formula><mml:math id="M22"><mml:msub><mml:mi>&#x03B5;</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math></inline-formula> is the disturbance term, i.e., the effect of the other independent variables that are not included (which has a mean of 0, has or does not have equal variance and <inline-formula><mml:math id="M23"><mml:mi>&#x03B5;</mml:mi></mml:math></inline-formula> is statistically independent of each other and its distribution is normal). <inline-formula><mml:math id="M24"><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></inline-formula> depends on the linear relationship between the independent variable and the dependent variable, reflecting the magnitude of the independent variable&#x2019;s influence on the probability of the dependent variable. A positive <inline-formula><mml:math id="M25"><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></inline-formula> indicates that an increase in the independent variable raises the probability of the event occurring; a negative value indicates the opposite.</p>
</sec>
</sec>
<sec id="sec13">
<label>4</label>
<title>Empirical results and analysis</title>
<sec id="sec14">
<label>4.1</label>
<title>Benchmark regression</title>
<p>Benchmark regression explores the impact of pro-poor product quality and safety issues on consumers&#x2019; repurchase intentions. <xref ref-type="table" rid="tab3">Table 3</xref> shows the results of regression coefficients and marginal effects before and after adding control variables. According to the results in <xref ref-type="table" rid="tab3">Table 3</xref>, encountering quality and safety problems reduces consumers&#x2019; repurchase intention, which is significant at a 1% significance level. According to the results of marginal effects, encountering quality and safety problems decreases consumers&#x2019; willingness to repurchase by 12.3% of consumers. After adding the control variables of consumers&#x2019; personal and family characteristics, the effect of experiencing quality and safety problems on consumers&#x2019; willingness to repurchase is still negative and significant at a 1% significance level. The marginal effect of adding the control variables shows that encountering quality and safety problems will reduce the willingness to repurchase for 11.8% of consumers.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Impact of pro-poor product quality and safety issues on willingness to repurchase.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="2">Probit</th>
<th align="center" valign="top" colspan="2">Marginal effect</th>
<th align="center" valign="top" colspan="2">Probit</th>
<th align="center" valign="top" colspan="2">Marginal effect</th>
</tr>
<tr>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Food safety issues</td>
<td align="center" valign="top">&#x2212;0.390&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.069</td>
<td align="center" valign="top">&#x2212;0.123</td>
<td align="center" valign="top">0.021</td>
<td align="center" valign="top">&#x2212;0.379&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.070</td>
<td align="center" valign="top">&#x2212;0.118</td>
<td align="center" valign="top">0.021</td>
</tr>
<tr>
<td align="left" valign="middle">Genders</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.214&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.062</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.019</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.0005</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">&#x2212;0.000</td>
<td align="center" valign="top">0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Marital status</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.046</td>
<td align="center" valign="top">0.087</td>
<td align="center" valign="top">&#x2212;0.014</td>
<td align="center" valign="top">0.027</td>
</tr>
<tr>
<td align="left" valign="middle">Educational level</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.016</td>
<td align="center" valign="top">0.015</td>
<td align="center" valign="top">&#x2212;0.005</td>
<td align="center" valign="top">0.005</td>
</tr>
<tr>
<td align="left" valign="middle">Residential address</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.012</td>
<td align="center" valign="top">0.086</td>
<td align="center" valign="top">&#x2212;0.006</td>
<td align="center" valign="top">0.027</td>
</tr>
<tr>
<td align="left" valign="middle">Household Size</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.003</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">&#x2212;0.001</td>
<td align="center" valign="top">0.009</td>
</tr>
<tr>
<td align="left" valign="middle">Household income</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.194&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.060</td>
<td align="center" valign="top">0.020</td>
</tr>
<tr>
<td align="left" valign="middle">Child</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.030</td>
<td align="center" valign="top">0.065</td>
<td align="center" valign="top">&#x2212;0.009</td>
<td align="center" valign="top">0.020</td>
</tr>
<tr>
<td align="left" valign="middle">Elder</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.020</td>
<td align="center" valign="top">0.045</td>
<td align="center" valign="top">0.006</td>
<td align="center" valign="top">0.014</td>
</tr>
<tr>
<td align="left" valign="middle">Purchase Channels</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.0003</td>
<td align="center" valign="top">0.047</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">0.015</td>
</tr>
<tr>
<td align="left" valign="middle">Area</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.318&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.078</td>
<td align="center" valign="top">0.099</td>
<td align="center" valign="top">0.024</td>
</tr>
<tr>
<td align="left" valign="top">Constant</td>
<td align="center" valign="top">0.757&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.035</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">0.809&#x002A;&#x002A;</td>
<td align="center" valign="top">0.314</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;, &#x002A;&#x002A;, &#x002A;&#x002A;&#x002A; represent statistically significant levels at 10%, 5%, and 1%.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec15">
<label>4.2</label>
<title>Robustness test 1&#x2014;effect of the total number of types of quality and safety problems encountered on repurchase</title>
<p>This study conducts a robustness test in two aspects to verify the robustness of the results on the impact of encountering quality and safety problems on consumers&#x2019; willingness to purchase pro-poor agrifoods. The first is to conduct regression analyses of consumers&#x2019; encounters with quality standards, labeling problems, adulteration and counterfeiting, mold and rot, excessive pathogenic microorganisms, excessive heavy metals, excessive pesticides and veterinary drugs, and quarantine failures as explanatory variables, respectively. The second is to test the average treatment effect of the impact of quality and safety issues on consumer purchase intentions using propensity score matching.</p>
<p><xref ref-type="table" rid="tab4">Tables 4</xref>, <xref ref-type="table" rid="tab5">5</xref> show the effects of different kinds of quality and safety problems on consumers&#x2019; willingness to repurchase, and the effects of adulteration and mold and rot on consumers&#x2019; willingness to repurchase when buying pro-poor agrifoods are all negatively significant at the 1% significance level. It further indicates that consumers&#x2019; willingness to repurchase pro-poor agrifoods will be reduced when they encounter adulteration and moldy and rotten conditions. Combined with the marginal coefficients, consumers&#x2019; willingness to repurchase decreases by 16.81% in the case of adulteration and 25.36% in the case of mold and rot. This result further validates the robustness of the benchmark regression.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Robustness test results&#x2014;effect of different quality and safety issues on repurchase.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="2">(1)</th>
<th align="center" valign="top" colspan="2">(2)</th>
<th align="center" valign="top" colspan="2">(3)</th>
<th align="center" valign="top" colspan="2">(4)</th>
</tr>
<tr>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Quality standards</td>
<td align="center" valign="top">&#x2212;0.150</td>
<td align="center" valign="top">0.110</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Labeling issues</td>
<td/>
<td/>
<td align="center" valign="top">0.013</td>
<td align="center" valign="top">0.144</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Adulteration and falsification</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.537&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.150</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Mildew and rot</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.829&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.106</td>
</tr>
<tr>
<td align="left" valign="middle">Genders</td>
<td align="center" valign="top">0.193&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.061</td>
<td align="center" valign="top">0.194&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.061</td>
<td align="center" valign="top">0.218&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.062</td>
<td align="center" valign="top">0.222&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.062</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">&#x2212;0.001</td>
<td align="center" valign="top">0.004</td>
</tr>
<tr>
<td align="left" valign="middle">Marital status</td>
<td align="center" valign="top">&#x2212;0.072</td>
<td align="center" valign="top">0.087</td>
<td align="center" valign="top">&#x2212;0.063</td>
<td align="center" valign="top">0.087</td>
<td align="center" valign="top">&#x2212;0.054</td>
<td align="center" valign="top">0.087</td>
<td align="center" valign="top">&#x2212;0.073</td>
<td align="center" valign="top">0.088</td>
</tr>
<tr>
<td align="left" valign="middle">Educational level</td>
<td align="center" valign="top">&#x2212;0.017</td>
<td align="center" valign="top">0.015</td>
<td align="center" valign="top">&#x2212;0.016</td>
<td align="center" valign="top">0.015</td>
<td align="center" valign="top">&#x2212;0.019</td>
<td align="center" valign="top">0.015</td>
<td align="center" valign="top">&#x2212;0.02</td>
<td align="center" valign="top">0.015</td>
</tr>
<tr>
<td align="left" valign="middle">Residential address</td>
<td align="center" valign="top">&#x2212;0.003</td>
<td align="center" valign="top">0.085</td>
<td align="center" valign="top">&#x2212;0.006</td>
<td align="center" valign="top">0.085</td>
<td align="center" valign="top">&#x2212;0.006</td>
<td align="center" valign="top">0.085</td>
<td align="center" valign="top">&#x2212;0.029</td>
<td align="center" valign="top">0.086</td>
</tr>
<tr>
<td align="left" valign="middle">Household Size</td>
<td align="center" valign="top">0.001</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">0.003</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">&#x2212;0.003</td>
<td align="center" valign="top">0.027</td>
</tr>
<tr>
<td align="left" valign="middle">Household income</td>
<td align="center" valign="top">0.204&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.207&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.208&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.204&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
</tr>
<tr>
<td align="left" valign="middle">Child</td>
<td align="center" valign="top">&#x2212;0.040</td>
<td align="center" valign="top">0.064</td>
<td align="center" valign="top">&#x2212;0.044</td>
<td align="center" valign="top">0.064</td>
<td align="center" valign="top">&#x2212;0.052</td>
<td align="center" valign="top">0.064</td>
<td align="center" valign="top">&#x2212;0.054</td>
<td align="center" valign="top">0.065</td>
</tr>
<tr>
<td align="left" valign="middle">Elder</td>
<td align="center" valign="top">0.027</td>
<td align="center" valign="top">0.044</td>
<td align="center" valign="top">0.029</td>
<td align="center" valign="top">0.044</td>
<td align="center" valign="top">0.020</td>
<td align="center" valign="top">0.044</td>
<td align="center" valign="top">0.025</td>
<td align="center" valign="top">0.044</td>
</tr>
<tr>
<td align="left" valign="middle">Purchase channels</td>
<td align="center" valign="top">0.011</td>
<td align="center" valign="top">0.047</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.047</td>
<td align="center" valign="top">&#x2212;0.012</td>
<td align="center" valign="top">0.048</td>
<td align="center" valign="top">&#x2212;0.015</td>
<td align="center" valign="top">0.048</td>
</tr>
<tr>
<td align="left" valign="middle">Area</td>
<td align="center" valign="top">0.333&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.077</td>
<td align="center" valign="top">0.334&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.077</td>
<td align="center" valign="top">0.319&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.077</td>
<td align="center" valign="top">0.318&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.078</td>
</tr>
<tr>
<td align="left" valign="top">Constant</td>
<td align="center" valign="top">0.695&#x002A;&#x002A;</td>
<td align="center" valign="top">0.311</td>
<td align="center" valign="top">0.673&#x002A;&#x002A;</td>
<td align="center" valign="top">0.312</td>
<td align="center" valign="top">0.762&#x002A;&#x002A;</td>
<td align="center" valign="top">0.311</td>
<td align="center" valign="top">0.931&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.313</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;, &#x002A;&#x002A;, &#x002A;&#x002A;&#x002A; represent statistically significant levels at 10%, 5%, and 1%.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Robustness test results&#x2014;effect of different quality and safety issues on repurchase.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="2">(5)</th>
<th align="center" valign="top" colspan="2">(6)</th>
<th align="center" valign="top" colspan="2">(7)</th>
<th align="center" valign="top" colspan="2">(8)</th>
</tr>
<tr>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Excess pathogenic microorganisms</td>
<td align="center" valign="top">&#x2212;0.281</td>
<td align="center" valign="top">0.225</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Heavy metal exceedance</td>
<td/>
<td/>
<td align="center" valign="top">0.067</td>
<td align="center" valign="top">0.231</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Excessive pesticides and veterinary drugs</td>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x2212;0.209</td>
<td align="center" valign="top">0.179</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Fail quarantine</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.204</td>
<td align="center" valign="top">0.231</td>
</tr>
<tr>
<td align="left" valign="middle">Genders</td>
<td align="center" valign="top">0.202&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.061</td>
<td align="center" valign="top">0.193&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.061</td>
<td align="center" valign="top">0.200&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.061</td>
<td align="center" valign="top">0.190&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.061</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="top">&#x2212;0.001</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.000</td>
<td align="center" valign="top">0.004</td>
</tr>
<tr>
<td align="left" valign="middle">Marital status</td>
<td align="center" valign="top">&#x2212;0.060</td>
<td align="center" valign="top">0.087</td>
<td align="center" valign="top">&#x2212;0.063</td>
<td align="center" valign="top">0.087</td>
<td align="center" valign="top">&#x2212;0.062</td>
<td align="center" valign="top">0.087</td>
<td align="center" valign="top">&#x2212;0.062</td>
<td align="center" valign="top">0.087</td>
</tr>
<tr>
<td align="left" valign="middle">Educational level</td>
<td align="center" valign="top">&#x2212;0.019</td>
<td align="center" valign="top">0.015</td>
<td align="center" valign="top">&#x2212;0.016</td>
<td align="center" valign="top">0.015</td>
<td align="center" valign="top">&#x2212;0.018</td>
<td align="center" valign="top">0.015</td>
<td align="center" valign="top">&#x2212;0.015</td>
<td align="center" valign="top">0.015</td>
</tr>
<tr>
<td align="left" valign="middle">Residential address</td>
<td align="center" valign="top">&#x2212;0.006</td>
<td align="center" valign="top">0.085</td>
<td align="center" valign="top">&#x2212;0.007</td>
<td align="center" valign="top">0.085</td>
<td align="center" valign="top">&#x2212;0.006</td>
<td align="center" valign="top">0.085</td>
<td align="center" valign="top">&#x2212;0.005</td>
<td align="center" valign="top">0.085</td>
</tr>
<tr>
<td align="left" valign="middle">Household size</td>
<td align="center" valign="top">0.003</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">0.003</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.028</td>
</tr>
<tr>
<td align="left" valign="middle">Household income</td>
<td align="center" valign="top">0.207&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.208&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.201&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
<td align="center" valign="top">0.208&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.066</td>
</tr>
<tr>
<td align="left" valign="middle">Child</td>
<td align="center" valign="top">&#x2212;0.040</td>
<td align="center" valign="top">0.064</td>
<td align="center" valign="top">&#x2212;0.046</td>
<td align="center" valign="top">0.064</td>
<td align="center" valign="top">&#x2212;0.038</td>
<td align="center" valign="top">0.064</td>
<td align="center" valign="top">&#x2212;0.045</td>
<td align="center" valign="top">0.064</td>
</tr>
<tr>
<td align="left" valign="middle">Elder</td>
<td align="center" valign="top">0.030</td>
<td align="center" valign="top">0.044</td>
<td align="center" valign="top">0.029</td>
<td align="center" valign="top">0.044</td>
<td align="center" valign="top">0.030</td>
<td align="center" valign="top">0.044</td>
<td align="center" valign="top">0.029</td>
<td align="center" valign="top">0.044</td>
</tr>
<tr>
<td align="left" valign="middle">Purchase Channels</td>
<td align="center" valign="top">0.001</td>
<td align="center" valign="top">0.047</td>
<td align="center" valign="top">0.004</td>
<td align="center" valign="top">0.047</td>
<td align="center" valign="top">0.005</td>
<td align="center" valign="top">0.047</td>
<td align="center" valign="top">0.003</td>
<td align="center" valign="top">0.047</td>
</tr>
<tr>
<td align="left" valign="middle">Area</td>
<td align="center" valign="top">0.329&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.077</td>
<td align="center" valign="top">0.334&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.077</td>
<td align="center" valign="top">0.329&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.077</td>
<td align="center" valign="top">0.337&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">0.077</td>
</tr>
<tr>
<td align="left" valign="top">Constant</td>
<td align="center" valign="top">0.743&#x002A;&#x002A;</td>
<td align="center" valign="top">0.315</td>
<td align="center" valign="top">0.665&#x002A;&#x002A;</td>
<td align="center" valign="top">0.313</td>
<td align="center" valign="top">0.725&#x002A;&#x002A;</td>
<td align="center" valign="top">0.314</td>
<td align="center" valign="top">0.630&#x002A;&#x002A;</td>
<td align="center" valign="top">0.316</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
<td align="center" valign="top">2099</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;, &#x002A;&#x002A;, &#x002A;&#x002A;&#x002A; represent statistically significant levels at 10%, 5%, and 1%.</p>
</table-wrap-foot>
</table-wrap>
<p>However, the effects of different quality and safety problems on consumers&#x2019; willingness to repurchase differ. The regression results in <xref ref-type="table" rid="tab4">Tables 4</xref>, <xref ref-type="table" rid="tab5">5</xref> show that the effects of quality and safety problems encountered by consumers purchasing pro-poor agrifoods, such as quality standards, labeling problems, exceeding of disease-causing microorganisms, exceeding of heavy metals, exceeding of pesticide and veterinary drugs, and failing of quarantine, on the willingness of consumers to repurchase pro-poor agrifoods are not statistically significant. There are two main reasons for this. Firstly, consumers have a deeper understanding of quality and safety issues that can be empirically determined and significantly impact consumers&#x2019; willingness to repurchase. These quality and safety issues are categorized into quality and safety issues that consumers can perceive explicitly and quality and safety issues that consumers cannot easily perceive. Food safety refers to all those chronic or acute hazards that can make food products damaging to consumers&#x2019; health. Food quality includes all other attributes that affect the product&#x2019;s value to the consumer. This distinction between safety and quality has significant implications for policy formation (<xref ref-type="bibr" rid="ref30">Lusk et al., 2014</xref>). Consumers can rely on their own experience to determine the quality standards, adulteration, and mold and rot these three quality and safety issues if they encounter these problems of bad consumer experience to consumers to bring more negative impact. For consumers, it is not easy to perceive the quality and safety issues, labeling problems, pathogenic microorganisms, heavy metals, pesticides, veterinary drugs qua, routine failure, and other issues require professional knowledge and professional tools to assist in order to conclude; the average consumer is rarely equipped with this professional knowledge and equipment to judge, consumers do not have a profound experience of the judgment of these quality and safety issues and just the existence of mistrust. Therefore, the effect on consumers&#x2019; repurchase intention is not significant. Secondly, for consumers, the impact of food safety issues on purchase intention is more significant than the impact of quality issues, so for the quality and safety issues that consumers can quickly identify the quality standard, it is more about the size, appearance, freshness, and taste of the product, which does not have any actual negative impact on the consumers, and therefore the impact on the consumers&#x2019; willingness to repurchase is also not significant.</p>
</sec>
<sec id="sec16">
<label>4.3</label>
<title>Robustness test 2&#x2014;average treatment effect of quality and safety issues on consumer purchase intention</title>
<p>Propensity score matching (PSM) can effectively help researchers control for confounders by comparing the characteristics of the control and treatment samples to ensure that extraneous factors can more accurately excluded from estimating treatment effects. By matching subjects (individuals who received the treatment) with non-subjects (individuals who did not), the researcher can more accurately assess the actual impact of the treatment on the outcome. In this study, propensity score matching (PSM) was used to analyze the effect of consumers experiencing quality and safety problems on purchase intentions, and the results are displayed in <xref ref-type="table" rid="tab6">Table 6</xref>. Based on the results of nearest neighbor matching, the treatment group&#x2019;s mean value is reduced by 0.159 compared to the mean illumination value, and the t-value is significant at a 1% significance level. To further verify that consumers encountering quality and safety problems reduce consumers&#x2019; repurchase intention, caliper matching, kernel matching, and local linear regression matching are used to verify the robustness of the results and corroborate the robustness of the underlying regression.</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Average treatment effects of quality and safety issues on consumer purchase intentions.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Matching method</th>
<th align="center" valign="top">Treatment group mean</th>
<th align="center" valign="top">Control group mean</th>
<th align="center" valign="top">ATT<xref ref-type="table-fn" rid="tfn1"><sup>a</sup></xref></th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top"><italic>T</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Nearest neighbor matching</td>
<td align="char" valign="top" char=".">0.643</td>
<td align="char" valign="top" char=".">0.802</td>
<td align="char" valign="top" char=".">&#x2212;0.159</td>
<td align="char" valign="top" char=".">0.039</td>
<td align="char" valign="top" char=".">&#x2212;4.11&#x002A;&#x002A;&#x002A;</td>
</tr>
<tr>
<td align="left" valign="top">Caliper Matching</td>
<td align="char" valign="top" char=".">0.638</td>
<td align="char" valign="top" char=".">0.780</td>
<td align="char" valign="top" char=".">&#x2212;0.141</td>
<td align="char" valign="top" char=".">0.026</td>
<td align="char" valign="top" char=".">&#x2212;5.50&#x002A;&#x002A;&#x002A;</td>
</tr>
<tr>
<td align="left" valign="top">nuclear matching</td>
<td align="char" valign="top" char=".">0.643</td>
<td align="char" valign="top" char=".">0.758</td>
<td align="char" valign="top" char=".">&#x2212;0.115</td>
<td align="char" valign="top" char=".">0.025</td>
<td align="char" valign="top" char=".">&#x2212;4.62&#x002A;&#x002A;&#x002A;</td>
</tr>
<tr>
<td align="left" valign="top">Local linear regression matching</td>
<td align="char" valign="top" char=".">0.643</td>
<td align="char" valign="top" char=".">0.746</td>
<td align="char" valign="top" char=".">&#x2212;0.103</td>
<td align="char" valign="top" char=".">0.039</td>
<td align="char" valign="top" char=".">&#x2212;2.66&#x002A;&#x002A;&#x002A;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>ATT is the average treatment effect of the treated group.</p>
<fn id="tfn1">
<label>a</label>
<p>ATT stands for Average Treatment Effect, which refers to the average treatment effect calculated solely among the population that received the treatment. The results in the table indicate the impact of encountering quality and safety issues on consumers&#x2019; purchase intentions.</p>
<p>&#x002A;, &#x002A;&#x002A;, &#x002A;&#x002A;&#x002A; represent statistically significant levels at 10%, 5%, and 1%.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec17">
<label>4.4</label>
<title>Analysis of regulatory mechanisms</title>
<p>Based on the baseline regression, three moderating variables, brand attention, evaluation attention, and altruism, and the interaction term with pro-poor agricultural product quality and safety issues are added to explore the moderating roles of brand attention, evaluation attention, and altruism in the influence of pro-poor agricultural product quality and safety issues on consumers&#x2019; purchase intention. The results are displayed in <xref ref-type="table" rid="tab7">Table 7</xref>.</p>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Results of regulation.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="2">(1)</th>
<th align="center" valign="top" colspan="2">(2)</th>
<th align="center" valign="top" colspan="2">(3)</th>
</tr>
<tr>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Quality and safety issues</td>
<td align="char" valign="top" char=".">&#x2212;1.266 &#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.272</td>
<td align="char" valign="top" char=".">&#x2212;0.937 &#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.312</td>
<td align="char" valign="top" char=".">&#x2212;0.851&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.194</td>
</tr>
<tr>
<td align="left" valign="top">Brand attention</td>
<td align="char" valign="top" char=".">0.010</td>
<td align="char" valign="top" char=".">0.037</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Quality and safety issues&#x002A; Brand attention</td>
<td align="char" valign="top" char=".">0.225&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.067</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Evaluative attention</td>
<td/>
<td/>
<td align="char" valign="top" char=".">&#x2212;0.079&#x002A;</td>
<td align="char" valign="top" char=".">0.042</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Quality and safety issues&#x002A; Evaluative attention</td>
<td/>
<td/>
<td align="char" valign="top" char=".">0.132&#x002A;</td>
<td align="char" valign="top" char=".">0.073</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Altruism</td>
<td/>
<td/>
<td/>
<td/>
<td align="char" valign="top" char=".">&#x2212;0.128</td>
<td align="char" valign="top" char=".">0.118</td>
</tr>
<tr>
<td align="left" valign="top">Quality and safety issues&#x002A; Altruism</td>
<td/>
<td/>
<td/>
<td/>
<td align="char" valign="top" char=".">0.545&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.208</td>
</tr>
<tr>
<td align="left" valign="middle">Genders</td>
<td align="char" valign="top" char=".">0.234&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.062</td>
<td align="char" valign="top" char=".">0.212 &#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.062</td>
<td align="char" valign="top" char=".">0.214&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.062</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="char" valign="top" char=".">0.000</td>
<td align="char" valign="top" char=".">0.004</td>
<td align="char" valign="top" char=".">0.000</td>
<td align="char" valign="top" char=".">0.004</td>
<td align="char" valign="top" char=".">0.000</td>
<td align="char" valign="top" char=".">0.004</td>
</tr>
<tr>
<td align="left" valign="middle">Marital status</td>
<td align="char" valign="top" char=".">&#x2212;0.055</td>
<td align="char" valign="top" char=".">0.088</td>
<td align="char" valign="top" char=".">&#x2212;0.033</td>
<td align="char" valign="top" char=".">0.088</td>
<td align="char" valign="top" char=".">&#x2212;0.042</td>
<td align="char" valign="top" char=".">0.088</td>
</tr>
<tr>
<td align="left" valign="middle">Educational level</td>
<td align="char" valign="top" char=".">&#x2212;0.008</td>
<td align="char" valign="top" char=".">0.015</td>
<td align="char" valign="top" char=".">&#x2212;0.017</td>
<td align="char" valign="top" char=".">0.015</td>
<td align="char" valign="top" char=".">&#x2212;0.016</td>
<td align="char" valign="top" char=".">0.015</td>
</tr>
<tr>
<td align="left" valign="middle">Residential address</td>
<td align="char" valign="top" char=".">&#x2212;0.034</td>
<td align="char" valign="top" char=".">0.086</td>
<td align="char" valign="top" char=".">&#x2212;0.027</td>
<td align="char" valign="top" char=".">0.086</td>
<td align="char" valign="top" char=".">&#x2212;0.030</td>
<td align="char" valign="top" char=".">0.086</td>
</tr>
<tr>
<td align="left" valign="middle">Household size</td>
<td align="char" valign="top" char=".">&#x2212;0.009</td>
<td align="char" valign="top" char=".">0.028</td>
<td align="char" valign="top" char=".">&#x2212;0.001</td>
<td align="char" valign="top" char=".">0.028</td>
<td align="char" valign="top" char=".">0.003</td>
<td align="char" valign="top" char=".">0.028</td>
</tr>
<tr>
<td align="left" valign="middle">Household income</td>
<td align="char" valign="top" char=".">0.183&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.067</td>
<td align="char" valign="top" char=".">0.206&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.066</td>
<td align="char" valign="top" char=".">0.191&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.066</td>
</tr>
<tr>
<td align="left" valign="middle">Child</td>
<td align="char" valign="top" char=".">&#x2212;0.031</td>
<td align="char" valign="top" char=".">0.065</td>
<td align="char" valign="top" char=".">&#x2212;0.029</td>
<td align="char" valign="top" char=".">0.065</td>
<td align="char" valign="top" char=".">&#x2212;0.028</td>
<td align="char" valign="top" char=".">0.065</td>
</tr>
<tr>
<td align="left" valign="middle">Elder</td>
<td align="char" valign="top" char=".">0.022</td>
<td align="char" valign="top" char=".">0.045</td>
<td align="char" valign="top" char=".">0.018</td>
<td align="char" valign="top" char=".">0.045</td>
<td align="char" valign="top" char=".">0.020</td>
<td align="char" valign="top" char=".">0.045</td>
</tr>
<tr>
<td align="left" valign="middle">Purchase channels</td>
<td align="char" valign="top" char=".">&#x2212;0.027</td>
<td align="char" valign="top" char=".">0.048</td>
<td align="char" valign="top" char=".">0.000</td>
<td align="char" valign="top" char=".">0.048</td>
<td align="char" valign="top" char=".">0.000</td>
<td align="char" valign="top" char=".">0.048</td>
</tr>
<tr>
<td align="left" valign="middle">Area</td>
<td align="char" valign="top" char=".">0.317 &#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.078</td>
<td align="char" valign="top" char=".">0.311&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.078</td>
<td align="char" valign="top" char=".">0.324&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.078</td>
</tr>
<tr>
<td align="left" valign="top">Constant</td>
<td align="char" valign="top" char=".">0.703&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.356</td>
<td align="char" valign="top" char=".">1.146&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.365</td>
<td align="char" valign="top" char=".">0.906&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.324</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;, &#x002A;&#x002A;, &#x002A;&#x002A;&#x002A; represent statistically significant levels at 10%, 5%, and 1%.</p>
</table-wrap-foot>
</table-wrap>
<p>According to the results of model (1) in <xref ref-type="table" rid="tab7">Table 7</xref>, the effect of quality and safety issues on consumers&#x2019; repurchase intention is negative. The effect of the interaction term between quality and safety issues and brand attention is positive and significant at the 1% significance level, meaning that brand attention attenuates the negative effect of quality and safety issues on repurchase intention. According to model (2) in <xref ref-type="table" rid="tab7">Table 7</xref>, the effect of the interaction term of quality and safety issues and evaluation attention on repurchase intention is positive and significant at the 10% significance level, which further proves that the evaluation attention of consumers on pro-poor agrifoods attenuates the negative effect of quality and safety issues on repurchase intention of consumers. According to the model (3) in <xref ref-type="table" rid="tab7">Table 7</xref>, the results show that the interaction term between quality and safety issues and public interest has a positive effect on consumers&#x2019; willingness to repurchase, which is significant at the 1% level of significance, and further proves that the stronger the consumers&#x2019; public interest is can weaken the negative effect of quality and safety issues on consumers&#x2019; willingness to repurchase. To a certain extent, consumers&#x2019; concern for brand and product evaluation and altruism can repair the harm caused by quality and safety problems in purchasing poverty-alleviating agrifoods.</p>
</sec>
<sec id="sec18">
<label>4.5</label>
<title>Heterogeneity analysis</title>
<p>According to the classification of different agrifoods, the effect of quality and safety problems on the willingness to repurchase six categories of pro-poor agrifoods, such as fruits, vegetables, grains and oils, tea, fresh meat, and fresh milk, was analyzed for heterogeneity. According to the results of the heterogeneity analysis in <xref ref-type="table" rid="tab8">Table 8</xref>, the quality and safety problems encountered by consumers of two categories of agrifoods, fruits, and vegetables significantly reduce consumers&#x2019; willingness to repurchase. In comparison, the regression results for four categories of products, namely, grains and oils, tea, fresh meat, and fresh milk, are insignificant. This may be because fruits and vegetables are the most frequently purchased agrifoods for poverty alleviation, and the risk of quality and safety problems in fruits and vegetables is more and more intuitive, which has the most significant impact on consumers&#x2019; consumption experience and has the greatest influence on consumers&#x2019; willingness to repurchase.</p>
<table-wrap position="float" id="tab8">
<label>Table 8</label>
<caption>
<p>Heterogeneity analysis.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Fruit</th>
<th align="center" valign="top">Vegetable</th>
<th align="center" valign="top">Grain and oil</th>
<th align="center" valign="top">Tea</th>
<th align="center" valign="top">Fresh meat</th>
<th align="center" valign="top">Pecorino</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Quality and safety issues</td>
<td align="center" valign="top">&#x2212;0.695&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">&#x2212;0.494&#x002A;&#x002A;</td>
<td align="center" valign="top">&#x2212;0.297</td>
<td align="center" valign="top">&#x2212;0.336</td>
<td align="center" valign="top">0.052</td>
<td align="center" valign="top">&#x2212;0.296</td>
</tr>
<tr>
<td align="left" valign="middle">Genders</td>
<td align="center" valign="top">0.248&#x002A;</td>
<td align="center" valign="top">0.177</td>
<td align="center" valign="top">0.155</td>
<td align="center" valign="top">0.332&#x002A;</td>
<td align="center" valign="top">0.188</td>
<td align="center" valign="top">0.075</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="top">0.015&#x002A;</td>
<td align="center" valign="top">&#x2212;0.011</td>
<td align="center" valign="top">&#x2212;0.011</td>
<td align="center" valign="top">0.008</td>
<td align="center" valign="top">&#x2212;0.005</td>
<td align="center" valign="top">&#x2212;0.007</td>
</tr>
<tr>
<td align="left" valign="middle">Marital status</td>
<td align="center" valign="top">&#x2212;0.009</td>
<td align="center" valign="top">&#x2212;0.082</td>
<td align="center" valign="top">&#x2212;0.265</td>
<td align="center" valign="top">&#x2212;0.216</td>
<td align="center" valign="top">&#x2212;0.033</td>
<td align="center" valign="top">&#x2212;0.04</td>
</tr>
<tr>
<td align="left" valign="middle">Educational level</td>
<td align="center" valign="top">&#x2212;0.052&#x002A;</td>
<td align="center" valign="top">&#x2212;0.053</td>
<td align="center" valign="top">&#x2212;0.004</td>
<td align="center" valign="top">&#x2212;0.004</td>
<td align="center" valign="top">0.018</td>
<td align="center" valign="top">0.014</td>
</tr>
<tr>
<td align="left" valign="middle">Residential address</td>
<td align="center" valign="top">&#x2212;0.087</td>
<td align="center" valign="top">0.123</td>
<td align="center" valign="top">0.237</td>
<td align="center" valign="top">&#x2212;0.152</td>
<td align="center" valign="top">&#x2212;0.108</td>
<td align="center" valign="top">0.121</td>
</tr>
<tr>
<td align="left" valign="middle">Household size</td>
<td align="center" valign="top">&#x2212;0.018</td>
<td align="center" valign="top">&#x2212;0.073</td>
<td align="center" valign="top">0.041</td>
<td align="center" valign="top">&#x2212;0.084</td>
<td align="center" valign="top">0.028</td>
<td align="center" valign="top">&#x2212;0.003</td>
</tr>
<tr>
<td align="left" valign="middle">Household income</td>
<td align="center" valign="top">0.284&#x002A;&#x002A;</td>
<td align="center" valign="top">0.485&#x002A;&#x002A;</td>
<td align="center" valign="top">0.016</td>
<td align="center" valign="top">0.232</td>
<td align="center" valign="top">0.222</td>
<td align="center" valign="top">0.101</td>
</tr>
<tr>
<td align="left" valign="middle">Child</td>
<td align="center" valign="top">0.078</td>
<td align="center" valign="top">&#x2212;0.193</td>
<td align="center" valign="top">0.059</td>
<td align="center" valign="top">&#x2212;0.127</td>
<td align="center" valign="top">&#x2212;0.144</td>
<td align="center" valign="top">&#x2212;0.179</td>
</tr>
<tr>
<td align="left" valign="middle">Elder</td>
<td align="center" valign="top">0.034</td>
<td align="center" valign="top">0.021</td>
<td align="center" valign="top">0.017</td>
<td align="center" valign="top">0.055</td>
<td align="center" valign="top">&#x2212;0.070</td>
<td align="center" valign="top">&#x2212;0.014</td>
</tr>
<tr>
<td align="left" valign="middle">Purchase channels</td>
<td align="center" valign="top">&#x2212;0.028</td>
<td align="center" valign="top">&#x2212;0.001</td>
<td align="center" valign="top">0.053</td>
<td align="center" valign="top">0.083</td>
<td align="center" valign="top">&#x2212;0.053</td>
<td align="center" valign="top">0.013</td>
</tr>
<tr>
<td align="left" valign="middle">Area</td>
<td align="center" valign="top">0.230</td>
<td align="center" valign="top">0.557&#x002A;&#x002A;</td>
<td align="center" valign="top">0.559&#x002A;&#x002A;</td>
<td align="center" valign="top">0.522&#x002A;&#x002A;</td>
<td align="center" valign="top">0.442&#x002A;</td>
<td align="center" valign="top">0.493&#x002A;</td>
</tr>
<tr>
<td align="left" valign="top">Constant</td>
<td align="center" valign="top">&#x2212;0.041</td>
<td align="center" valign="top">2.309&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="top">1.029</td>
<td align="center" valign="top">0.973</td>
<td align="center" valign="top">0.730</td>
<td align="center" valign="top">0.958</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">441</td>
<td align="center" valign="top">363</td>
<td align="center" valign="top">403</td>
<td align="center" valign="top">289</td>
<td align="center" valign="top">302</td>
<td align="center" valign="top">301</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;, &#x002A;&#x002A;, &#x002A;&#x002A;&#x002A; represent statistically significant levels at 10%, 5%, and 1%.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec19">
<label>4.6</label>
<title>Discussion</title>
<p>Studies have shown that quality and safety issues can frustrate consumers&#x2019; public interest. Suppose consumers encounter quality and safety problems when purchasing pro-poor agrifoods. In that case, their willingness to repurchase significantly decreases by 12%, which means that food safety incidents force consumers to reduce their consumption of this type of product and look for alternatives with similar functions in other categories, which is consistent with the findings of other scholars (<xref ref-type="bibr" rid="ref3">Bai et al., 2024</xref>; <xref ref-type="bibr" rid="ref5">Barnett et al., 2016</xref>). For different kinds of food quality and safety problems, due to consumers&#x2019; limited knowledge and skills in judging quality and safety problems, they experience and feel more deeply about quality and safety problems than their existing knowledge and experience can judge, so adulteration and mold and decay have a more significant negative impact on consumers&#x2019; willingness to repurchase pro-poor agrifoods. At the same time, consumers&#x2019; negative perception of pro-poor agrifoods is more related to safety rather than quality. Hence, the effect of quality standards on consumers&#x2019; willingness to repurchase is insignificant.</p>
<p>This lousy experience for consumers is not static and will gradually change their perception of pro-poor agrifoods, according to their information acquisition. This is especially true for consumers who pay attention to brand and product evaluation because the brand can bring certain high-quality information and reputation assurance; consumers&#x2019; attention to product evaluation can also update the lousy influence of previous products. Therefore, the greater the consumers&#x2019; attention to brand and product evaluation, the more negative impact on repurchase intention can be attenuated when consumers encounter quality and safety problems.</p>
<p>Altruism can make consumers prefer agrifoods with altruism or warmth (<xref ref-type="bibr" rid="ref13">Gangadharan et al., 2023</xref>). That is, consumers with high altruism are more willing to buy pro-poor agrifoods with altruism and will still be willing to support these products despite some defects. Therefore, the higher the level of altruism of consumers, the lower the negative impact of consumer behavior due to quality and safety problems encountered in consumption.</p>
<p>There is a significant difference in the negative effect of quality and safety issues on consumers&#x2019; repurchase intention for two categories of agrifoods: fruits and vegetables. In comparison, the regression results for four categories of products, grain and oil, tea, fresh meat, and fresh milk, are insignificant. There are two reasons for the difference. First, consumers usually buy fruits and vegetables periodically, so they are more sensitive to the quality and safety issues of fruits and vegetables than other products. Second, the quality and safety issues of products such as food and oil, tea, fresh meat, and fresh milk may be relatively abstract or infrequent factors for consumers to consider; therefore, the impact of quality issues of fruits and vegetables on consumers&#x2019; consumption experience may be more direct and obvious, and the impact on consumers&#x2019; repurchase intention is more significant.</p>
<p>The findings of this study align with previous research on consumer behavior, product quality and safety, and altruistic consumption in their overall direction, while also revealing several unique differences and theoretical extensions. Existing studies generally hold that perceptions of product quality and safety are core variables influencing consumer purchasing decisions, and that adverse incidents significantly weaken purchase intent and brand trust. Our empirical results similarly validate this conclusion: when quality and safety issues arise with poverty-alleviation agricultural products, consumer repurchase intent declines by 12%. This indicates that even in altruistic consumption contexts with public welfare connotations, rational evaluation still dominates certain purchasing decisions. Similar to general market goods, consumer concern for product reliability remains the foundation for sustaining long-term purchasing relationships. This study further reveals the moderating roles of altruism and information attention during quality safety shocks. Consumers&#x2019; altruistic tendencies mitigate the negative impact of quality safety concerns on repeat purchases. This suggests that in socially responsible consumption scenarios, such as poverty-alleviation products, moral motivations can partially substitute for functional trust, thereby maintaining purchase stability. When consumers place greater emphasis on brand and product review information, their sensitivity to quality issues is relatively lower. Brand trust and external reputation mechanisms can partially substitute for product attribute trust, thereby mitigating the adverse effects of risk perception. This aligns with perspectives from trust transfer and brand attachment theories. Additionally, another innovation of this study compared to previous literature lies in revealing differential impacts across different categories of agricultural and sideline products. Findings indicate that safety concerns significantly diminish repurchase intent for fruits and vegetables&#x2014;products characterized by high perceptibility of quality and elevated risk exposure&#x2014;while standardized goods like grains, cooking oils, tea, fresh meat, and fresh milk experience limited impact. This result supplements previous research shortcomings regarding the &#x201C;product homogeneity&#x201D; assumption, highlighting the interactive relationship between perceived quality risks and product characteristics.</p>
</sec>
</sec>
<sec sec-type="conclusions" id="sec20">
<label>5</label>
<title>Conclusion</title>
<p>This paper takes the example of pro-poor agrifoods. It analyzes the influence of pro-poor agrifoods&#x2019; food quality and safety issues on pro-poor agrifoods&#x2019; repurchase willingness by designing a questionnaire to count the consumption behavior of 533 consumers. It also reveals the influence mechanism of quality and safety concern, brand concern, and public welfare heart in the influence of pro-poor agrifoods&#x2019; food quality and safety problems on pro-poor agrifoods&#x2019; willingness to repurchase. They, furthermore, explored the heterogeneity of different kinds of agrifoods. Moreover, the following conclusions are drawn: first, for products with moral attributes, if the consumers themselves are harmed, it will make the consumers harmed. Quality and safety issues show a significant negative impact on consumers&#x2019; willingness to repurchase pro-poor agrifoods. Second, the greater attention to consumer quality and safety evaluation will weaken the negative impact of quality and safety issues on consumers&#x2019; willingness to repurchase, the greater attention to consumer brand will weaken the negative impact of quality and safety issues on consumers&#x2019; willingness to repurchase, and the higher public-spirited consumers will weaken the impact of quality and safety issues on consumers&#x2019; willingness to repurchase. Third, the quality and safety problems in the classification of agrifoods have more and more intuitive risks, which have a more significant impact on consumers&#x2019; consumption experience and have the greatest influence on consumers&#x2019; willingness to repurchase. According to the classification of different agrifoods, the impact of quality and safety problems of six poverty-alleviating agrifoods, including fruits, vegetables, grain and oil, tea, fresh meat, and fresh milk, on the willingness to repurchase was analyzed for heterogeneity. It was found that the quality and safety problems encountered by consumers of two categories of agrifoods, fruits and vegetables, significantly reduced their willingness to repurchase. In comparison, the regression results for four categories of products, namely grain and oil, tea, fresh meat, and fresh milk, were insignificant.</p>
<p>Based on the above conclusions, this paper gets the following insights. First, we should do an excellent job of quality and safety supervision from the source of the products to reduce the excess of heavy metals and pesticide residues. The key lies in strengthening the management and monitoring of the production link, establishing a perfect quality and safety management system, enhancing the supervision and inspection of the production link, and ensuring that the products meet the safety standards and quality requirements.</p>
<p>Second, to strengthen the market end of the supervision, agrifoods need to meet the standard products can not enter the market. It is recommended that the testing of agrifoods for unqualified products be strengthened for strict penalties and publicity. At the same time, it should strengthen the supervision of agrifoods sales link, the business license, practitioners, business premises, and other audit efforts to improve agrifoods sales market standardization. Strengthening the supervision of agrifoods with public welfare and moral attributes can protect the rights and interests of consumers and avoid consuming consumers&#x2019; public welfare and poking the enthusiasm of consumers.</p>
<p>Third, the construction of agrifoods after consuming the comment display platform. The platform allows consumers to evaluate the purchased agrifoods, including product quality, taste, and service, and also allows consumers to share the use of agrifoods and their purchase experience. At the same time, the government should also build a corresponding regulatory platform to audit and manage the reviews to ensure that they are accurate and reliable. These reviews can provide references for potential consumers and regain the confidence of consumers affected by quality and safety issues. Moreover, it can also improve the transparency and credibility of agrifoods, which also helps promote the agrifoods industry&#x2019;s healthy development.</p>
<p>There are also some shortcomings in this study; However, this paper illustrates that the consumption behavior of agrifoods with public welfare behavior and moral attributes will be frustrated by quality and safety problems but constrained by factors such as research design and data availability, the empirical part of the paper is mainly to verify that quality and safety problems will reduce consumer purchasing behavior and frustrate consumers&#x2019; public welfare by using fruits of pro-poor agrifoods as an example. However, the study puts the consumers in the category of public welfare behavior and moral attributes, and the above research flaws can be improved in subsequent research.</p>
<p>Although this study reveals the underlying mechanisms linking quality and safety issues, altruism, and consumer repurchase intention, several directions warrant further exploration. Future research could broaden the sample scope to compare consumer responses to poverty-alleviation agricultural products across different regions, age groups, and cultural contexts, thereby testing the cross-cultural and situational applicability of the findings. Additionally, behavioral experiments or neuroimaging studies could be employed to explore the psychological mechanisms linking consumers&#x2019; intrinsic moral motivations, risk perceptions, and purchasing decisions, offering a more nuanced understanding of the logic underlying altruistic consumption behavior.</p>
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</body>
<back>
<sec sec-type="data-availability" id="sec21">
<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 sec-type="ethics-statement" id="sec22">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Guizhou University Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="sec23">
<title>Author contributions</title>
<p>YY: Data curation, Methodology, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. XM: Software, Writing &#x2013; original draft.</p>
</sec>

<sec sec-type="COI-statement" id="sec25">
<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>
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<title>Generative AI statement</title>
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<title>Publisher&#x2019;s note</title>
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<fn id="fn0002" fn-type="custom" custom-type="edited-by"><p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3038182/overview">Divesh Kumar</ext-link>, Malaviya National Institute of Technology, Jaipur, India</p></fn>
<fn id="fn0003" fn-type="custom" custom-type="reviewed-by"><p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1796520/overview">Mara Braga</ext-link>, University of Coimbra, Portugal</p><p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3075868/overview">Rakotoarisoa Maminiaina Heritiana Sedera</ext-link>, ISCAM Business School, Madagascar</p></fn>
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<fn id="fn0001"><p><sup>1</sup><ext-link xlink:href="http://www.wjx.cn" ext-link-type="uri">www.wjx.cn</ext-link></p></fn>
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