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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Phys.</journal-id>
<journal-title>Frontiers in Physics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Phys.</abbrev-journal-title>
<issn pub-type="epub">2296-424X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">857751</article-id>
<article-id pub-id-type="doi">10.3389/fphy.2022.857751</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Physics</subject>
<subj-group>
<subject>Brief Research Report</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Exploring Factors of Preschool Parents&#x2019; Behavioral Intention to Use Face Recognition Technology on Campus</article-title>
<alt-title alt-title-type="left-running-head">Liu et al.</alt-title>
<alt-title alt-title-type="right-running-head">Face Recognition Technology</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Yinsheng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1642054/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhao</surname>
<given-names>Li</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1159519/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Su</surname>
<given-names>Yu-Sheng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/856839/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>School of Education Science</institution>, <institution>Nanjing Normal University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Computer Science and Engineering</institution>, <institution>National Taiwan Ocean University</institution>, <addr-line>Keelung</addr-line>, <country>Taiwan</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/924220/overview">Jian Gao</ext-link>, Northwestern University, United States</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1494248/overview">Tao Liu</ext-link>, Hangzhou Dianzi University, China</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1458365/overview">Yung-Chia Hsiao</ext-link>, Baise University, China</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Yu-Sheng Su, <email>ntouaddisonsu@gmail.com</email>
</corresp>
<fn fn-type="other">
<p>This article was submitted to Social Physics, a section of the journal Frontiers in Physics</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>11</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>10</volume>
<elocation-id>857751</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>03</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Liu, Zhao and Su.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Liu, Zhao and Su</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Face recognition technology (FRT) is being increasingly used to record the trajectory of human behavior due to its non-contact nature and high accuracy. When the technology is extended to education, it is applied to manage students&#x2019; access to campus, to analyze learning behaviors, and to monitor students&#x2019; campus activities. It is important to note that the use of face recognition technology for students on campus should be approved by the students&#x2019; guardians. Therefore, this study aimed to determine what factors affect the behavioral intentions of preschool parents&#x2019; adoption of facial recognition systems on campus. Unlike previous studies, the model of this study was designed to focus not only on the affective dimension, but also on the parenting style. The model was validated with data from an online questionnaire completed by 419 preschool parents. AMOS was used to analyze various assumptions of the model. The analysis revealed that innate consumer innovativeness positively influenced experience values and helicopter parenting which directly affected their behavioral intentions. The results imply that in the application of face recognition technology, parents&#x2019; behavioral intentions depend not only on the prior experiential value of the product and helicopter parenting, but also on parents&#x2019; innate consumer innovativeness. Therefore, for campus management and technology application, this study is useful to understand the behavioral intention of guardians to use the new technology. For operators and users of face recognition technology, this study provides several guidelines for exploring parental attitudes toward child supervision and improving products and services to value information security.</p>
</abstract>
<kwd-group>
<kwd>face recognition technology</kwd>
<kwd>innate consumer innovativeness</kwd>
<kwd>experiential value</kwd>
<kwd>helicopter parenting</kwd>
<kwd>behavioral intention</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>Analysis of human behavior and its behavioral intentions is a common research method in social science. Technology is not only a tool for innovation in the service of research, but also an element that can facilitate the emergence of innovation in new contexts [<xref ref-type="bibr" rid="B1">1</xref>]. Based on the support of deep learning and big data, the speed and accuracy of face recognition technology in analyzing human emotions and states are rapidly improving [<xref ref-type="bibr" rid="B2">2</xref>], and its application areas have been extended to education, human-computer interaction, epidemic prevention, and other aspects closely related to our lives [<xref ref-type="bibr" rid="B3">3</xref>&#x2013;<xref ref-type="bibr" rid="B5">5</xref>]. However, most of previous studies on face recognition technology have focused on practical applications, such as improving the accuracy and scale of face recognition [<xref ref-type="bibr" rid="B6">6</xref>, <xref ref-type="bibr" rid="B7">7</xref>]. In fact, the unique feature of face recognition technology that does not require active human cooperation raises certain security and ethical issues in the application process. Lai et al. [<xref ref-type="bibr" rid="B8">8</xref>] summarized some of the controversies and concerns arising from the misuse of face recognition technology. As people become more aware of information data protection, it is necessary to actively explore the factors that influence the use of face recognition technology.</p>
<p>As education and emerging technologies continue to integrate and innovate, there is growing demand for face recognition technology in education [<xref ref-type="bibr" rid="B9">9</xref>]. The development of technology in new areas is essential to increase and update existing knowledge [<xref ref-type="bibr" rid="B10">10</xref>]. One of the main applications of face recognition technology relies on deep learning for classroom management and monitoring of students&#x2019; learning behavior [<xref ref-type="bibr" rid="B11">11</xref>]. On a campus or in a classroom, the target of face recognition is students who are not yet adults, and their privacy and facial data should be more effectively protected. Therefore, when face recognition technology is carried out in a classroom or on campus, seeking the behavioral intention of their guardians regarding face recognition technology is a key step in the implementation of the technology. In fact, whether adopting technology or consuming a product, behavioral intentions are influenced by many factors, including both the user&#x2019;s pre-existing personality traits and prior use experiences. For example, Jing et al. [<xref ref-type="bibr" rid="B12">12</xref>] found that parents&#x2019; personal traits and innate innovation influence their choice of autonomous vehicles for their children&#x2019;s safety. In other words, parents&#x2019; personal traits and innate innovation tend to directly influence their usage behavior. The importance of user experience value has been widely used in acceptance studies of new technology products, and previous studies have shown that prior emotional and functional experiences have a significant impact on consumer attitudes toward using such products, and will influence consumer choices long into the future [<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>]. There has, however, been little research on the experiential value of using face recognition systems. In addition, parenting style has become an important consideration when exploring parenting and parental supervision. Helicopter parenting, as a parenting style that emphasizes parental intervention and protection, is often considered when exploring supervision of children and their academic stress [<xref ref-type="bibr" rid="B15">15</xref>, <xref ref-type="bibr" rid="B16">16</xref>].</p>
<p>This study examined guardians&#x2019; willingness to have their children supervised with face recognition. Thus, innate consumer innovativeness was considered in the study, as a trait-like variable, while the experiential value of the technology and parenting style, an important dimension of educational research, was considered. The study aimed to explore the factors which affect preschool children&#x2019;s parents&#x2019; behavioral intention regarding face recognition technology, and to provide some reference for the preparation of face recognition technology before it is implemented.</p>
</sec>
<sec id="s2">
<title>2 Theoretical Background and Hypotheses</title>
<sec id="s2-1">
<title>2.1 Innate Consumer Innovativeness</title>
<p>Innate consumer innovativeness (ICI) refers to a generalized but non-consensual perceptual characteristic that is usually expressed as an individual&#x2019;s intrinsic cognitive style and innovative personality [<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>]. ICI as a factor leading to innovative behavior is often cited and studied in studies on innovation diffusion [<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>]. When individuals uphold a strong ICI, they will have a greater willingness to accept new products at an early stage than other individuals. According to Vandecasteele and Geuens [<xref ref-type="bibr" rid="B21">21</xref>], exploring the relationship between the two is often analyzed using experiential value as a mediator. However, this often creates more limitations due to differences in product functionality and application scenarios. Therefore, when exploring the consumer-product connection, it is important to focus not only on the relational ties between the two, but also to emphasize the intervention of other factors on usage behavior.</p>
</sec>
<sec id="s2-2">
<title>2.2 Experiential Value</title>
<p>Mathwick et al. [<xref ref-type="bibr" rid="B22">22</xref>] defined experiential value as an individual&#x2019;s perception of a type of product or way of behaving that is formed through direct experience or indirect observation. In contrast to other values, experience value (EV) is concerned with the value that individuals retain from different kinds of experiences and can be thought of as perceived, relative preferences for product or behavioral attributes.</p>
<p>Meanwhile, Sheth et al. [<xref ref-type="bibr" rid="B14">14</xref>] used experience value theory to explore the reasons why individuals would continue to use certain types of products. Subsequent studies have also shown that experience value is an important reference point that influences whether a user will use a product or service again [<xref ref-type="bibr" rid="B13">13</xref>]. In the field of education, Burke et al. [<xref ref-type="bibr" rid="B23">23</xref>] found that parents&#x2019; experiences growing up internalize their traits and directly influence the way they treat their children. In exploring the relationship between ICI and EV, Lowe and Alpert [<xref ref-type="bibr" rid="B24">24</xref>] assessed the overall innovativeness of individuals and found that ICI can influence the experience value of individuals.</p>
<p>However, little research has been conducted on the role of EV in FRT applications, due to the fact that some application contexts of FRT tend to capture people&#x2019;s EV directly without considering them, especially in the educational domain. Therefore, in exploring FRT-oriented EV, the following hypotheses were proposed in this study.</p>
<p>
<statement content-type="hypothesis" id="Hypothesis_1">
<label>Hypothesis 1</label>
<p>Preschool parents&#x2019; ICI is positively related to their EV for FRT.</p>
</statement>
</p>
</sec>
<sec id="s2-3">
<title>2.3 Helicopter Parenting</title>
<p>Helicopter parenting (HP) is considered to be a parenting style that excessively regulates and constructs the child&#x2019;s behavioral world, interfering in the child&#x2019;s daily activities and homework, which intrudes on and manipulates the child&#x2019;s thoughts, feelings, and attachment to the parent [<xref ref-type="bibr" rid="B25">25</xref>]. This type of parenting is different from the control of behavior and psychology in that it shows more emphasis on deep parental involvement in their children&#x2019;s lives [<xref ref-type="bibr" rid="B26">26</xref>].</p>
<p>According to [<xref ref-type="bibr" rid="B15">15</xref>], HP is associated with intrusive, stressful aspects of controlling children&#x2019;s behavior. This over-focused parenting style often begins early in childhood and has long-term effects on children [<xref ref-type="bibr" rid="B27">27</xref>]. However, this parenting pattern is formed unconsciously and is largely due to parental traits [<xref ref-type="bibr" rid="B28">28</xref>]. [<xref ref-type="bibr" rid="B29">29</xref>] demonstrated that parental traits have a direct impact on the parenting style they choose, and that the trait of intrinsic innovativeness is unique in that it promotes experimentation with new products or lifestyles [<xref ref-type="bibr" rid="B30">30</xref>]. This suggests that parenting style has become an important consideration when exploring parent-child relationships and supervisory behaviors, which are often influenced by parental traits. HP, as a more controlling parenting style, has largely been absent from studies of campus supervision. Therefore, the following hypothesis was proposed.</p>
<p>
<statement content-type="hypothesis" id="Hypothesis_2">
<label>Hypothesis 2</label>
<p>Preschool parents&#x2019; ICI is positively related to HP on FRT.</p>
</statement>
</p>
</sec>
<sec id="s2-4">
<title>2.4 Behavioral Intention</title>
<p>Engel et al. [<xref ref-type="bibr" rid="B31">31</xref>] proposed that behavioral intention (BI) is derived from attitude. It refers to the specific activities or behavioral tendencies that consumers are likely to adopt for a product or service after consumption, and is an accurate indicator of future consumer behavior. In the field of face recognition technology use, positive behavioral intention refers to the meaning possessed by the use, willingness to use, or belief in face recognition technology.</p>
<p>Researchers have often analyzed the influencing factors of BI when exploring consumer acceptance of new technologies. For example, [<xref ref-type="bibr" rid="B32">32</xref>] explored influencing factors in terms of primary influences, experiential value, positive meaning, and relative advantage dimensions to explore the behavioral intentions affecting the adoption of broadband among Malaysian consumers. The influence of experience value is not only used to explain consumers&#x2019; offline purchasing behavior, but is also widely used in the field of digital goods research. [<xref ref-type="bibr" rid="B33">33</xref>] found the influence of the hedonic and utilitarian constructs of experience value on consumers&#x2019; intention to use smartphones, while [<xref ref-type="bibr" rid="B13">13</xref>] studied the effect of EV on consumers&#x2019; viewing of IP movies. Therefore, Hypothesis 3 was proposed as follows:</p>
<p>
<statement content-type="hypothesis" id="Hypothesis_3">
<label>Hypothesis 3</label>
<p>Preschool parents&#x2019; EV is positively related to BI regarding FRT.</p>
<p>In addition, because face recognition technology in public settings is often not consulted for intention to use, fewer studies have explored BI regarding face recognition technology in public settings. However, this does not mean that this research perspective is not meaningful, as the misuse of face recognition technology has gradually become a concern [<xref ref-type="bibr" rid="B8">8</xref>]. In addition, helicopter parenting is a unique parenting type that reflects deep parental involvement in their children&#x2019;s lives, even to the point of exhibiting overprotection or overcontrol [<xref ref-type="bibr" rid="B16">16</xref>]. Using parenting style as a predictor of willingness to use, he suggested that an important predictor of parents&#x2019; willingness to use is their propensity to control. This study therefore proposed Hypothesis 4:</p>
</statement>
</p>
<p>
<statement content-type="hypothesis" id="Hypothesis_4">
<label>Hypothesis 4</label>
<p>Preschool parents&#x2019; HP is positively related to their BI regarding FRT</p>
<p>Combining the above literature, this study used the perceived dimension of experiential value and the educational dimension of parenting style as state-like variables to explore the factors that influence preschool parents&#x2019; behavioral intention to apply FRT on campus (see <xref ref-type="fig" rid="F1">Figure 1</xref>).</p>
</statement>
</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Research model.</p>
</caption>
<graphic xlink:href="fphy-10-857751-g001.tif"/>
</fig>
</sec>
</sec>
<sec id="s3">
<title>3 Methodology</title>
<sec id="s3-1">
<title>3.1 Participants</title>
<p>The study investigated the interaction between preschool parents&#x2019; ICI, EV, HP, and BI to use face recognition technology on campus. All participants were informed that the online questionnaire would only be used for this study and that their privacy would be protected. For the questionnaire distribution, we chose more economically developed regions for this study, as these regions tend to apply face recognition technology earlier for management purposes. Snowball sampling was used to collect the data for this study. An online questionnaire was posted on the platform &#x201c;Questionnaire Star&#x201d; (<ext-link ext-link-type="uri" xlink:href="https://www.wjx.cn/">https://www.wjx.cn/</ext-link>) and a link to the questionnaire was sent to parents through kindergarten teachers in different schools.</p>
<p>A total of 447 responses were collected from June to July 2021. After eliminating invalid questionnaires, 419 valid questionnaires were retained for statistical analysis, with a valid return rate of 94%. Respondents included 101 men (24.1%) and 318 women (75.9%), with 417 (99.5%) respondents having heard of or used face recognition technology. Of these respondents, 79.4% were between 31 and 40&#xa0;years old, 61.3% had received college education, and more than half of respondents (67%) had been using face recognition technology for more than a year. The most common occasions they used facial recognition were for bank activation authentication and inbound security checks (train stations, airports, etc.) (see <xref ref-type="table" rid="T1">Table 1</xref>).</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Descriptive statistical analysis.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="7" align="left">The participants&#x2019; basic information</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="2" align="left">Gender</td>
<td colspan="2" align="left">Male</td>
<td colspan="4" align="left">Female</td>
</tr>
<tr>
<td colspan="2" align="left">101</td>
<td colspan="4" align="left">318</td>
</tr>
<tr>
<td rowspan="2" align="left">Age</td>
<td align="left">&#x3c;30</td>
<td align="left">31&#x2013;40</td>
<td align="left">41&#x2013;50</td>
<td colspan="3" align="left">&#x3e;50</td>
</tr>
<tr>
<td align="left">8</td>
<td align="left">333</td>
<td align="left">70</td>
<td colspan="3" align="left">8</td>
</tr>
<tr>
<td rowspan="2" align="left">Educational level</td>
<td align="left">Middle school and below</td>
<td align="left">High school</td>
<td colspan="3" align="left">Undergraduate</td>
<td align="left">Graduate and above</td>
</tr>
<tr>
<td align="left">1</td>
<td align="left">9</td>
<td colspan="3" align="left">257</td>
<td align="left">152</td>
</tr>
<tr>
<td rowspan="2" align="left">Occupation</td>
<td align="left">Manual laborer</td>
<td align="left">Company employee</td>
<td align="left">Civil servant</td>
<td align="left">Freelancer</td>
<td align="left">Full-time at home</td>
<td align="left">Other</td>
</tr>
<tr>
<td align="left">1</td>
<td align="left">179</td>
<td align="left">169</td>
<td align="left">14</td>
<td align="left">35</td>
<td align="left">21</td>
</tr>
<tr>
<td colspan="7" align="left">Face recognition usage</td>
</tr>
<tr>
<td colspan="4" align="left"/>
<td align="left">Yes</td>
<td align="left"/>
<td align="left">No</td>
</tr>
<tr>
<td colspan="4" align="left">Have you heard of face recognition technology?</td>
<td align="left">417</td>
<td align="left"/>
<td align="left">2</td>
</tr>
<tr>
<td colspan="4" align="left">Have you ever used face recognition technology?</td>
<td align="left">396</td>
<td align="left"/>
<td align="left">23</td>
</tr>
<tr>
<td colspan="7" align="left">Where do you use face recognition technology most frequently?</td>
</tr>
<tr>
<td align="left">Schools</td>
<td align="left">Face scan payment</td>
<td align="left">Activate authentication</td>
<td align="left">Station security check</td>
<td align="left">Conference check-in</td>
<td align="left">Home access control</td>
<td align="left">Others</td>
</tr>
<tr>
<td align="left">22</td>
<td align="left">40</td>
<td align="left">143</td>
<td align="left">120</td>
<td align="left">19</td>
<td align="left">23</td>
<td align="left">52</td>
</tr>
<tr>
<td colspan="7" align="left">How long have you been using face recognition technology?</td>
</tr>
<tr>
<td align="left">Less than 1&#xa0;month</td>
<td colspan="2" align="left">1&#x2013;6&#xa0;months</td>
<td colspan="2" align="left">7&#x2013;12&#xa0;months</td>
<td colspan="2" align="left">More than 1&#xa0;year</td>
</tr>
<tr>
<td align="left">48</td>
<td colspan="2" align="left">37</td>
<td colspan="2" align="left">53</td>
<td colspan="2" align="left">281</td>
</tr>
<tr>
<td colspan="7" align="left">How often do you use face recognition technology in a week&#x003e;</td>
</tr>
<tr>
<td align="left">3 times or less</td>
<td colspan="2" align="left">4&#x2013;6 times</td>
<td colspan="2" align="left">7&#x2013;10 times</td>
<td colspan="2" align="left">11 times or more</td>
</tr>
<tr>
<td align="left">236</td>
<td colspan="2" align="left">61</td>
<td colspan="2" align="left">49</td>
<td colspan="2" align="left">7</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-2">
<title>3.2 Instrument</title>
<p>A questionnaire was distributed online in the study. The survey was conducted voluntarily and anonymously. The answers to the questionnaire were for the use of the researchers and were not used for commercial or any other purpose. The questionnaire had three parts, including the basic information about the participants, the participants&#x2019; use and attitude toward face recognition technology through their use in daily life, and the scale of factors affecting the acceptance of face recognition technology by preschool parents on campus. The items were designed in the form of a 6-point Likert scale, ranging from 1 (<italic>strongly disagree</italic>) to 6 (<italic>strongly agree</italic>), indicating the respondent&#x2019;s level of agreement with the items. The original questionnaire had four latent variables, namely consumer innate innovativeness (7 items), experience value (8 items), helicopter parents (8 items) and behavioral intention (6 items), for a total of 29 items.</p>
<sec id="s3-2-1">
<title>3.2.1 Innate Consumer Innovativeness</title>
<p>To measure preschool parents&#x2019; ICI, this study synthesized Kirton&#x2019;s [<xref ref-type="bibr" rid="B18">18</xref>] and [<xref ref-type="bibr" rid="B17">17</xref>] questionnaires for measuring ICI, in which the former first proposed an instrument for measuring ICI and was continuously cited in subsequent related studies, and the latter empirically linked ICI to a new product adoption behavior study, which is close to the case of this study. The subscale contains seven items (e.g., &#x201c;If I knew a new technology product, I would find a way to experience it&#x201d;). Higher scores on this subscale reflected a stronger ICI. The Cronbach&#x2019;s alpha of this variable was 0.854.</p>
</sec>
<sec id="s3-2-2">
<title>3.2.2 Experiential Value</title>
<p>To assess experiential value, this study designed an Experiential Value Questionnaire by combining a number of questionnaires [<xref ref-type="bibr" rid="B34">34</xref>]. To make the scale specific to face recognition, this study adapted all items by adding wording related to face recognition (e.g., &#x201c;I think the face recognition system is working well&#x201d;). The adapted experience value scale comprised eight items and consisted of two subscales: Functional Value (4 items, e.g., &#x201c;I think the face recognition system is highly secure and does not reveal personal information&#x201d;) and Emotional value (4 items, e.g., &#x201c;I enjoy using the face recognition system&#x201d;). The Cronbach&#x2019;s alpha for experiential value was 0.906.</p>
</sec>
<sec id="s3-2-3">
<title>3.2.3 Helicopter Parenting</title>
<p>The helicopter parenting questionnaire was adapted from [<xref ref-type="bibr" rid="B35">35</xref>]. Since HP often manifests as excessive parental attention to children, this study also referred to Hong et al. regarding parental monitoring and helicopter parenting [<xref ref-type="bibr" rid="B25">25</xref>]. The Helicopter Parenting scale comprised eight items, an example of which is &#x201c;I would really like to be able to monitor my child&#x2019;s school situation at all times.&#x201d; The Cronbach&#x2019;s alpha of this variable was 0.887.</p>
</sec>
<sec id="s3-2-4">
<title>3.2.4 Behavioral Intention</title>
<p>To assess behavioral intention, this study adapted the Behavioral Intention Scale [<xref ref-type="bibr" rid="B36">36</xref>]. The scale was designed based on Fishbein and Ajzen&#x2019;s [<xref ref-type="bibr" rid="B37">37</xref>] definition of behavioral intention, which states that &#x201c;behavioral intention is the process necessary for behavioral performance and the decision that precedes the emergence of the behaviors.&#x201d; It comprises six items (e.g., &#x201c;If the future campus environment permits, I would choose face recognition as a campus management method&#x201d;). Higher total scores on this scale indicated stronger BI. The Cronbach&#x2019;s alpha of this variable was 0.902.</p>
</sec>
</sec>
<sec id="s3-3">
<title>3.3 Reliability and Validity Test</title>
<p>The third part of the questionnaire was the scale of factors affecting the acceptance of face recognition technology by preschool parents on campus. (See <xref ref-type="table" rid="T2">Table 2</xref>).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Data of the scale.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="7" align="left">Variable measurement questionnaire</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left"/>
<td align="char" char=".">1</td>
<td align="char" char=".">2</td>
<td align="char" char=".">3</td>
<td align="char" char=".">4</td>
<td align="char" char=".">5</td>
<td align="char" char=".">6</td>
</tr>
<tr>
<td colspan="7" align="left">Innate Consumer Innovativeness</td>
</tr>
<tr>
<td align="left">I am open to new ideas</td>
<td align="char" char=".">3</td>
<td align="char" char=".">7</td>
<td align="char" char=".">19</td>
<td align="char" char=".">183</td>
<td align="char" char=".">174</td>
<td align="char" char=".">33</td>
</tr>
<tr>
<td align="left">I usually take risks to do different things</td>
<td align="char" char=".">3</td>
<td align="char" char=".">50</td>
<td align="char" char=".">131</td>
<td align="char" char=".">141</td>
<td align="char" char=".">89</td>
<td align="char" char=".">5</td>
</tr>
<tr>
<td align="left">I don&#x2019;t like to stay the same</td>
<td align="char" char=".">&#x2014;</td>
<td align="char" char=".">18</td>
<td align="char" char=".">75</td>
<td align="char" char=".">154</td>
<td align="char" char=".">147</td>
<td align="char" char=".">25</td>
</tr>
<tr>
<td align="left">I like to pursue excitement</td>
<td align="char" char=".">3</td>
<td align="char" char=".">27</td>
<td align="char" char=".">162</td>
<td align="char" char=".">159</td>
<td align="char" char=".">61</td>
<td align="char" char=".">7</td>
</tr>
<tr>
<td align="left">I often come up with different ideas to share with people around me</td>
<td align="char" char=".">&#x2014;</td>
<td align="char" char=".">16</td>
<td align="char" char=".">123</td>
<td align="char" char=".">172</td>
<td align="char" char=".">90</td>
<td align="char" char=".">18</td>
</tr>
<tr>
<td align="left">I often have new ideas in my head</td>
<td align="char" char=".">2</td>
<td align="char" char=".">27</td>
<td align="char" char=".">106</td>
<td align="char" char=".">192</td>
<td align="char" char=".">74</td>
<td align="char" char=".">18</td>
</tr>
<tr>
<td align="left">I can constantly come up with new ideas about the same issue</td>
<td align="char" char=".">&#x2014;</td>
<td align="char" char=".">14</td>
<td align="char" char=".">95</td>
<td align="char" char=".">206</td>
<td align="char" char=".">84</td>
<td align="char" char=".">20</td>
</tr>
<tr>
<td colspan="7" align="left">Experiential Value</td>
</tr>
<tr>
<td align="left">I think the face recognition system is easy to use</td>
<td align="char" char=".">2</td>
<td align="char" char=".">7</td>
<td align="char" char=".">16</td>
<td align="char" char=".">177</td>
<td align="char" char=".">172</td>
<td align="char" char=".">15</td>
</tr>
<tr>
<td align="left">I think the face recognition system is versatile and can meet my needs</td>
<td align="char" char=".">2</td>
<td align="char" char=".">8</td>
<td align="char" char=".">22</td>
<td align="char" char=".">191</td>
<td align="char" char=".">155</td>
<td align="char" char=".">41</td>
</tr>
<tr>
<td align="left">I think face recognition is more efficient than other verification methods for admission</td>
<td align="char" char=".">&#x2014;</td>
<td align="char" char=".">2</td>
<td align="char" char=".">39</td>
<td align="char" char=".">170</td>
<td align="char" char=".">154</td>
<td align="char" char=".">54</td>
</tr>
<tr>
<td align="left">I think it is valuable to use the face recognition system for campus management</td>
<td align="char" char=".">5</td>
<td align="char" char=".">13</td>
<td align="char" char=".">80</td>
<td align="char" char=".">175</td>
<td align="char" char=".">117</td>
<td align="char" char=".">29</td>
</tr>
<tr>
<td align="left">I think it is interesting to use face recognition</td>
<td align="char" char=".">4</td>
<td align="char" char=".">5</td>
<td align="char" char=".">43</td>
<td align="char" char=".">180</td>
<td align="char" char=".">146</td>
<td align="char" char=".">41</td>
</tr>
<tr>
<td align="left">I find using face recognition enjoyable</td>
<td align="char" char=".">2</td>
<td align="char" char=".">10</td>
<td align="char" char=".">58</td>
<td align="char" char=".">203</td>
<td align="char" char=".">110</td>
<td align="char" char=".">36</td>
</tr>
<tr>
<td align="left">I enjoy using the face recognition system</td>
<td align="char" char=".">4</td>
<td align="char" char=".">12</td>
<td align="char" char=".">57</td>
<td align="char" char=".">195</td>
<td align="char" char=".">114</td>
<td align="char" char=".">37</td>
</tr>
<tr>
<td align="left">I feel relaxed after using face recognition</td>
<td align="char" char=".">4</td>
<td align="char" char=".">11</td>
<td align="char" char=".">53</td>
<td align="char" char=".">191</td>
<td align="char" char=".">127</td>
<td align="char" char=".">33</td>
</tr>
<tr>
<td colspan="7" align="left">Helicopter parenting</td>
</tr>
<tr>
<td align="left">I would like to be able to monitor my child&#x2019;s school performance at all times</td>
<td align="char" char=".">4</td>
<td align="char" char=".">22</td>
<td align="char" char=".">55</td>
<td align="char" char=".">144</td>
<td align="char" char=".">134</td>
<td align="char" char=".">60</td>
</tr>
<tr>
<td align="left">I would like to be able to monitor my child&#x2019;s development every step of the way</td>
<td align="char" char=".">4</td>
<td align="char" char=".">10</td>
<td align="char" char=".">50</td>
<td align="char" char=".">154</td>
<td align="char" char=".">124</td>
<td align="char" char=".">77</td>
</tr>
<tr>
<td align="left">If my child is in trouble at school, I would go to the school immediately to see the teacher to find out what is going on</td>
<td align="char" char=".">&#x2014;</td>
<td align="char" char=".">9</td>
<td align="char" char=".">93</td>
<td align="char" char=".">165</td>
<td align="char" char=".">105</td>
<td align="char" char=".">47</td>
</tr>
<tr>
<td align="left">I would like my child to call or text me regularly to let me know how he/she is doing at school, if possible</td>
<td align="char" char=".">1</td>
<td align="char" char=".">22</td>
<td align="char" char=".">127</td>
<td align="char" char=".">125</td>
<td align="char" char=".">100</td>
<td align="char" char=".">44</td>
</tr>
<tr>
<td align="left">I would like to be able to monitor my child&#x2019;s food and drink at school</td>
<td align="char" char=".">5</td>
<td align="char" char=".">24</td>
<td align="char" char=".">98</td>
<td align="char" char=".">155</td>
<td align="char" char=".">101</td>
<td align="char" char=".">36</td>
</tr>
<tr>
<td align="left">I would like to be able to monitor who my child is playing with at school</td>
<td align="char" char=".">9</td>
<td align="char" char=".">29</td>
<td align="char" char=".">134</td>
<td align="char" char=".">136</td>
<td align="char" char=".">81</td>
<td align="char" char=".">30</td>
</tr>
<tr>
<td align="left">If my child has a problem with a classmate, I will intervene immediately</td>
<td align="char" char=".">19</td>
<td align="char" char=".">85</td>
<td align="char" char=".">241</td>
<td align="char" char=".">51</td>
<td align="char" char=".">40</td>
<td align="char" char=".">10</td>
</tr>
<tr>
<td align="left">I am always personally involved in my child&#x2019;s school life</td>
<td align="char" char=".">&#x2014;</td>
<td align="char" char=".">10</td>
<td align="char" char=".">101</td>
<td align="char" char=".">179</td>
<td align="char" char=".">103</td>
<td align="char" char=".">26</td>
</tr>
<tr>
<td colspan="7" align="left">Behavioral Intention</td>
</tr>
<tr>
<td align="left">I will choose face recognition as the campus management method if the campus environment permits in the future</td>
<td align="char" char=".">8</td>
<td align="char" char=".">2</td>
<td align="char" char=".">28</td>
<td align="char" char=".">160</td>
<td align="char" char=".">170</td>
<td align="char" char=".">51</td>
</tr>
<tr>
<td align="left">I feel that once I have tried using face recognition as a method of entry verification, I will encourage other students&#x2019; parents to use it</td>
<td align="char" char=".">4</td>
<td align="char" char=".">2</td>
<td align="char" char=".">57</td>
<td align="char" char=".">160</td>
<td align="char" char=".">149</td>
<td align="char" char=".">49</td>
</tr>
<tr>
<td align="left">I think the sooner I use face recognition in classrooms to verify children&#x2019;s entry and exit, the better</td>
<td align="char" char=".">11</td>
<td align="char" char=".">6</td>
<td align="char" char=".">60</td>
<td align="char" char=".">161</td>
<td align="char" char=".">130</td>
<td align="char" char=".">51</td>
</tr>
<tr>
<td align="left">I would like to suggest that educational institutions use facial recognition systems to manage schools</td>
<td align="char" char=".">8</td>
<td align="char" char=".">4</td>
<td align="char" char=".">65</td>
<td align="char" char=".">152</td>
<td align="char" char=".">141</td>
<td align="char" char=".">49</td>
</tr>
<tr>
<td align="left">I would like to convince other parents to accept the face recognition system to manage the school</td>
<td align="char" char=".">2</td>
<td align="char" char=".">10</td>
<td align="char" char=".">88</td>
<td align="char" char=".">157</td>
<td align="char" char=".">115</td>
<td align="char" char=".">47</td>
</tr>
<tr>
<td align="left">Overall, my evaluation of using the face recognition system is positive and my willingness to use it is high</td>
<td align="char" char=".">2</td>
<td align="char" char=".">3</td>
<td align="char" char=".">39</td>
<td align="char" char=".">162</td>
<td align="char" char=".">162</td>
<td align="char" char=".">51</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Reliability is verified by internal consistency reliability (Cronbach&#x2019;s alpha) and composite reliability (CR). The values of the Cronbach&#x2019;s alpha all exceeded 0.916, and the values of CR for all constructs were greater than 0.820 (ranging from 0.820 to 0.969), showing that the study constructs had acceptable reliability [<xref ref-type="bibr" rid="B38">38</xref>]. <xref ref-type="table" rid="T3">Table 3</xref> shows that all AVEs were between 0.533 and 0.885. Therefore, the convergent validity of the model was satisfied.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Results of confirmatory factor analysis.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Latent variable</th>
<th align="center">Measure item</th>
<th align="center">Standardized factor loading</th>
<th align="center">Composite reliability (CR)</th>
<th align="center">Average variance extracted (AVE)</th>
<th align="center">Cronbach&#x2019;s alpha</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="4" align="left">Innate consumer innovativeness</td>
<td align="left">ICI2</td>
<td align="char" char=".">0.702</td>
<td rowspan="4" align="char" char=".">0.820</td>
<td rowspan="4" align="char" char=".">0.533</td>
<td rowspan="4" align="char" char=".">0.817</td>
</tr>
<tr>
<td align="left">ICI3</td>
<td align="char" char=".">0.685</td>
</tr>
<tr>
<td align="left">ICI4</td>
<td align="char" char=".">0.755</td>
</tr>
<tr>
<td align="left">ICI5</td>
<td align="char" char=".">0.775</td>
</tr>
<tr>
<td rowspan="4" align="left">Experiential value</td>
<td align="left">EV4</td>
<td align="char" char=".">0.717</td>
<td rowspan="4" align="char" char=".">0.933</td>
<td rowspan="4" align="char" char=".">0.784</td>
<td rowspan="4" align="char" char=".">0.927</td>
</tr>
<tr>
<td align="left">EV6</td>
<td align="char" char=".">0.897</td>
</tr>
<tr>
<td align="left">EV7</td>
<td align="char" char=".">0.964</td>
</tr>
<tr>
<td align="left">EV8</td>
<td align="char" char=".">0.934</td>
</tr>
<tr>
<td rowspan="4" align="left">Helicopter parenting</td>
<td align="left">HP3</td>
<td align="char" char=".">0.802</td>
<td rowspan="4" align="char" char=".">0.863</td>
<td rowspan="4" align="char" char=".">0.613</td>
<td rowspan="4" align="char" char=".">0.860</td>
</tr>
<tr>
<td align="left">HP4</td>
<td align="char" char=".">0.865</td>
</tr>
<tr>
<td align="left">HP6</td>
<td align="char" char=".">0.744</td>
</tr>
<tr>
<td align="left">HP8</td>
<td align="char" char=".">0.712</td>
</tr>
<tr>
<td rowspan="4" align="left">Behavioral intention</td>
<td align="left">BI2</td>
<td align="char" char=".">0.910</td>
<td rowspan="4" align="char" char=".">0.969</td>
<td rowspan="4" align="char" char=".">0.885</td>
<td rowspan="4" align="char" char=".">0.968</td>
</tr>
<tr>
<td align="left">BI4</td>
<td align="char" char=".">0.977</td>
</tr>
<tr>
<td align="left">BI5</td>
<td align="char" char=".">0.937</td>
</tr>
<tr>
<td align="left">BI6</td>
<td align="char" char=".">0.939</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-4">
<title>3.4 Data Analysis</title>
<p>In the study, structural equation modelling (SEM) was applied for data analysis because it allows for the simultaneous analysis of multiple variables as needed for the study and in conjunction with factor analysis [<xref ref-type="bibr" rid="B39">39</xref>]. Firstly, confirmatory factor analysis (CFA) tests were applied to confirm the validity and reliability of the variables. Secondly, correlation analyses were conducted for ICI, EV, HP, and BI in order to explore potential relationships among the variables. Thirdly, model fitting was performed. Finally, a path analysis of the structural relationships was performed.</p>
</sec>
</sec>
<sec id="s4">
<title>4 Results</title>
<sec id="s4-1">
<title>4.1 Item Analysis</title>
<p>The original questionnaire had 29 items, including seven items for ICI, eight each for EV and HP, and six for BI. First, those with factor loading values less than 0.5 were removed. Second, a first-order CFA was applied to test the suitability of the items, removing those with the highest residual values in each construct until the values of these first-order CFAs reached the threshold suggested by [<xref ref-type="bibr" rid="B38">38</xref>]. Finally, the remaining 16 items were retained for further analysis.</p>
</sec>
<sec id="s4-2">
<title>4.2 Correlational Analysis</title>
<p>According to [<xref ref-type="bibr" rid="B40">40</xref>], when the square root of AVE is greater than the correlation values of the other constructs, construct discriminant validity (CDV) can be determined. All square roots of the AVEs (the diagonal elements) exceed the correlation of the factor and another factor (the off-diagonal elements) (see <xref ref-type="table" rid="T4">Table 4</xref>). CDV is therefore considered to be acceptable.</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Correlation coefficient matrix and square roots of AVE (<italic>n</italic> &#x3d; 419).</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Construct</th>
<th align="center">ICI</th>
<th align="center">EV</th>
<th align="center">HP</th>
<th align="center">BI</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">ICI</td>
<td colspan="4" align="char" char=".">
<bold>0.730</bold>
</td>
</tr>
<tr>
<td align="left">EV</td>
<td align="char" char=".">0.418&#x2a;&#x2a;</td>
<td colspan="3" align="char" char=".">
<bold>0.885</bold>
</td>
</tr>
<tr>
<td align="left">HP</td>
<td align="char" char=".">0.550&#x2a;&#x2a;</td>
<td align="char" char=".">0.395&#x2a;&#x2a;</td>
<td colspan="2" align="char" char=".">
<bold>0.783</bold>
</td>
</tr>
<tr>
<td align="left">BI</td>
<td align="char" char=".">0.288&#x2a;&#x2a;</td>
<td align="char" char=".">0.744&#x2a;&#x2a;</td>
<td align="char" char=".">0.444&#x2a;&#x2a;</td>
<td align="char" char=".">
<bold>0.940</bold>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note. <italic>&#x2a;&#x2a;p &#x3c; 0.01.</italic> The diagonal elements are the square roots of AVE, and the off-diagonal elements are values of the inter-construct correlations.</p>
</fn>
<fn>
<p>The results of correlational analysis showed that there was a significant correlation among ICI, EV, HP, and BI (see <xref ref-type="table" rid="T4">Table 4</xref>). ICI was positively correlated with EV (r &#x3d; 0.418<sup>&#x2a;&#x2a;</sup>) and with HP (r &#x3d; 0.550<sup>&#x2a;&#x2a;</sup>). EV was positively correlated with BI (r &#x3d; 0.744<sup>&#x2a;&#x2a;</sup>). HP was positively correlated with BI (r &#x3d; 0.444<sup>&#x2a;&#x2a;</sup>).</p>
</fn>
<fn>
<p>According to [<xref ref-type="bibr" rid="B40">40</xref>], when the square root of AVE is greater than the correlation values of the other constructs, construct discriminant validity (CDV) can be determined. In this study, as shown in <xref ref-type="table" rid="T4">Table 4</xref>, the values marked as bold showed that all square roots of the AVEs (the diagonal elements) exceed the correlation of the factor and another factor (the off-diagonal elements). For example, the square root of AVE of ICI is greater than the correlation value of ICI with other factors (0.730 &#x3e; 0.418, 0.550, and 0.288). CDV is therefore considered to be acceptable.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s4-3">
<title>4.3 Model Fit Analysis</title>
<p>The fit indices and causal path analysis of the model were evaluated by AMOS. The indices exhibited perfect fitness in terms of the model of this study (&#x3c7;<sup>2</sup>/<italic>df</italic> &#x3d; 2.739, TLI &#x3d; 0.964, IFI &#x3d; 0.970, NFI &#x3d; 0.954, CFI &#x3d; 0.970, RMSEA &#x3d; 0.064) (see <xref ref-type="table" rid="T5">Table 5</xref>).</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>Fit indices of the structural model.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Fit indices</th>
<th align="center">&#x3c7;<sup>2</sup>/<italic>df</italic>
</th>
<th align="center">TLI</th>
<th align="center">IFI</th>
<th align="center">NFI</th>
<th align="center">CFI</th>
<th align="center">RMSEA</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Threshold</td>
<td align="char" char=".">&#x3c;3.00</td>
<td align="char" char=".">&#x3e;0.90</td>
<td align="char" char=".">&#x3e;0.90</td>
<td align="char" char=".">&#x3e;0.90</td>
<td align="char" char=".">&#x3e;0.90</td>
<td align="char" char=".">&#x3c;0.80</td>
</tr>
<tr>
<td align="left">Result values</td>
<td align="char" char=".">2.739</td>
<td align="char" char=".">0.964</td>
<td align="char" char=".">0.970</td>
<td align="char" char=".">0.954</td>
<td align="char" char=".">0.970</td>
<td align="char" char=".">0.064</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s4-4">
<title>4.4 Path Analysis</title>
<p>This study aimed to identify the relationship among preschool parents&#x2019; BI regarding face recognition technology, ICI, EV, and HP. The verification results showed that ICI was positively related to EV (&#x3b2; &#x3d; 0.436, <italic>p</italic> &#x3c; 0.001) and HP (&#x3b2; &#x3d; 0.564, <italic>p</italic> &#x3c; 0.001), supporting H1 and H2, respectively. EV and HP were positively associated with BI (EV &#x2192; BI: &#x3b2; &#x3d; 0.688, <italic>p</italic> &#x3c; 0.001; HP &#x2192; BI: &#x3b2; &#x3d; 0.175, <italic>p</italic> &#x3c; 0.001), supporting H3 and H4, respectively (see <xref ref-type="table" rid="T6">Table 6</xref>).</p>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>Coefficients of the hypothesized model.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Hypothesis</th>
<th align="center">Path</th>
<th align="center">&#x3b2;</th>
<th align="center">
<italic>SE</italic>
</th>
<th align="center">
<italic>t</italic>
</th>
<th align="center">Supported</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">H1</td>
<td align="left">ICI&#x2192;EV</td>
<td align="char" char=".">0.436</td>
<td align="char" char=".">0.059</td>
<td align="char" char=".">7.359&#x2a;</td>
<td align="left">Yes</td>
</tr>
<tr>
<td align="left">H2</td>
<td align="left">ICI&#x2192;HP</td>
<td align="char" char=".">0.564</td>
<td align="char" char=".">0.075</td>
<td align="char" char=".">8.890&#x2a;</td>
<td align="left">Yes</td>
</tr>
<tr>
<td align="left">H3</td>
<td align="left">EV&#x2192;BI</td>
<td align="char" char=".">0.688</td>
<td align="char" char=".">0.065</td>
<td align="char" char=".">13.324&#x2a;</td>
<td align="left">Yes</td>
</tr>
<tr>
<td align="left">H4</td>
<td align="left">HP&#x2192;BI</td>
<td align="char" char=".">0.175</td>
<td align="char" char=".">0.043</td>
<td align="char" char=".">4.324</td>
<td align="left">Yes</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note. <italic>&#x2a;p</italic> &#x3c; 0.05.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The coefficient of determination (R<sup>2</sup>) and effect size (<italic>f</italic>
<sup>2</sup>) are the measures of predictive structural models [<xref ref-type="bibr" rid="B38">38</xref>]. The R<sup>2</sup> values range from 0 to 1. As shown in <xref ref-type="fig" rid="F2">Figure 2</xref>, the explanatory power of ICI on EV is 19%, with an effect size <italic>f</italic>
<sup>2</sup> of 0.23; the explanatory power of ICI on HP is 32%, with an effect size <italic>f</italic>
<sup>2</sup> of 0.47; the explanatory power of EV and HP on BI is 56%, with an effect size <italic>f</italic>
<sup>2</sup> of 1.27. All statistical values were thus acceptable.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>The structural model with standardized coefficients (Note: &#x2a;&#x2a;&#x2a;<italic>p</italic> &#x3c; 0.001).</p>
</caption>
<graphic xlink:href="fphy-10-857751-g002.tif"/>
</fig>
</sec>
<sec id="s4-5">
<title>4.5 Direct and Indirect Effect Analyses</title>
<p>We conducted 5,000 resample bootstrapping with 95% bias-corrected confidence intervals. From <xref ref-type="table" rid="T7">Table 7</xref>, for direct effects, each 95% confidence interval (CI) did not include zero, which reveals that the direct effect existed in the research model. Furthermore, the analysis showed that the indirect effect between ICI and BI to use face recognition technology was significant [&#x3b2; &#x3d; 0.455, <italic>p</italic> &#x3c; 0.001; 95% CI, (0.37, 0.58)]. This not only suggests that there is indeed a mediating effect of EV and HP between parents&#x2019; ICI and behavioral intentions to use face recognition technology to supervise their children. It also suggests that all variables in this study had good predictive power [<xref ref-type="bibr" rid="B38">38</xref>].</p>
<table-wrap id="T7" position="float">
<label>TABLE 7</label>
<caption>
<p>Direct and indirect effect analyses.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center"/>
<th colspan="2" align="center">ICI</th>
<th colspan="2" align="center">EV</th>
<th colspan="2" align="center">HP</th>
</tr>
<tr>
<th align="center">&#x3b2;</th>
<th align="center">95% CI</th>
<th align="center">&#x3b2;</th>
<th align="center">95% CI</th>
<th align="center">&#x3b2;</th>
<th align="center">95% CI</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Direct effect</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="left">EV</td>
<td align="center">0.427&#x2a;&#x2a;&#x2a;</td>
<td align="center">[0.33,0.54]</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="left">HP</td>
<td align="center">0.562&#x2a;&#x2a;&#x2a;</td>
<td align="center">[0.48.0.65]</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="left">BI</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">0.791&#x2a;&#x2a;&#x2a;</td>
<td align="center">[0.63,0.89]</td>
<td align="center">0.237&#x2a;&#x2a;&#x2a;</td>
<td align="center">[0.15,0.39]</td>
</tr>
<tr>
<td align="left">Indirect effect</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="left">EV</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="left">HP</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="left">BI</td>
<td align="center">0.455&#x2a;&#x2a;&#x2a;</td>
<td align="center">[0.37,0.58]</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
<td align="center">&#x2014;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note. &#x2a;&#x2a;&#x2a;<italic>p</italic> &#x3c; 0.001<italic>.</italic>
</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s5">
<title>5 Discussion</title>
<p>The study aimed to explore the factors influencing the behavioral intentions of preschool parents to apply FRT on campus. All of the hypotheses were supported. It can now be confirmed that ICI was an antecedent of EV and HP in this study, and that both could directly predict parents&#x2019; BI regarding the use of FRT.</p>
<sec id="s5-1">
<title>5.1 Preschool Parents&#x2019; ICI is Positively Related to Their EV of FRT</title>
<p>In the study, the results showed that preschool parents&#x2019; ICI was positively associated with their EV of FRT (&#x3b2; &#x3d; 0.436, <italic>p</italic> &#x3c; 0.001; H1 supported), which is consistent with the previous studies on the impact of ICI on EV, especially those of Hartman et al. [<xref ref-type="bibr" rid="B41">41</xref>] and Lowe and Alpert [<xref ref-type="bibr" rid="B24">24</xref>], indicating that individuals&#x2019; innovative traits positively affected the experience value, whether it was the acceptance of a new product or service. These similar findings may be due to the fact that when we discuss decision making styles, an individual&#x2019;s innovative ability directly influences the cognitive dimension of decision making, thus enriching the individual&#x2019;s experience value [<xref ref-type="bibr" rid="B42">42</xref>]. Thus, when preschool parents have stronger ICI, they show stronger EV for the latest products or services.</p>
</sec>
<sec id="s5-2">
<title>5.2 Preschool Parents&#x2019; ICI is Positively Related to the Influence of HP on Attitudes Towards FRT</title>
<p>In this study, when preschool parents had stronger ICI, they also had stronger HP when confronted with face recognition technology (&#x3b2; &#x3d; 0.564, <italic>p</italic> &#x3c; 0.001; supported by H2). In this study, ICI, while encouraging users to try new products or lifestyles, also influenced parents to try new techniques and models in their parenting. This is partly an expression of the generalizing influence of ICI on decision-making styles, where users with strong ICI are happy to intervene in their own lives in ways that are within their control in different scenarios [<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B43">43</xref>]. In the context of this study, face recognition technology can be a convenient technological tool, and when parents have stronger ICI, they will also have stronger HP and tend to use new technologies to enhance the supervision of their children.</p>
</sec>
<sec id="s5-3">
<title>5.3 Preschool Parents&#x2019; EV is Positively Related to Their BI Regarding FRT</title>
<p>The results of this study showed that EV of preschool parents had a positive effect on their BI regarding FRT (&#x3b2; &#x3d; 0.688, <italic>p</italic> &#x3c; 0.001; H3 was supported). Previous studies had similar findings, such as [<xref ref-type="bibr" rid="B44">44</xref>] study on consumer acceptance of technology, which showed a significant association between experiential value and intention to adopt. Emotion and experience were used by Song [<xref ref-type="bibr" rid="B45">45</xref>], and [<xref ref-type="bibr" rid="B46">46</xref>] as factors to explore behavioral intention, revealing that the factor had a positive impact on consumers&#x2019; behavioral intention regarding mobile communication technology and smartwatch devices. [<xref ref-type="bibr" rid="B33">33</xref>] discussed the significant impact of EV on BI in the smartphone domain through a refinement of the EV dimension. Over time, the internalized perceptions spread to influence the individual&#x2019;s attitudes toward other areas. In this study, parents&#x2019; prior experience and attitude towards face recognition were found to directly influence their intention to use face recognition technology for supervision on campus.</p>
</sec>
<sec id="s5-4">
<title>5.4 Preschool Parents&#x2019; HP is Positively Related to Their BI Regarding FRT</title>
<p>The result of this study showed that Preschool parents&#x2019; HP positively affected their BI regarding FRT (&#x3b2; &#x3d; 0.175, <italic>p</italic> &#x3c; 0.001; H4 supported). Parental control of behavior is considered an important factor influencing parental behavior [<xref ref-type="bibr" rid="B25">25</xref>]. In turn, HP will often further enhance monitoring and intervention in the parenting process for protection and monitoring purposes [<xref ref-type="bibr" rid="B47">47</xref>]. [<xref ref-type="bibr" rid="B48">48</xref>] argued that the use of monitoring systems is a novel form of parental monitoring, and by comparing different parenting styles, they found that parents with more control were more likely to adopt devices to supervise their children. Similarly, the stronger the parent&#x2019;s HP, the stronger their BI regarding the use of FRT to monitor their children, further suggesting that HP is overly concerned with the world of their child&#x2019;s behavior.</p>
<p>Taken together, these results indicate that there is an association between ICI, EV, HP, and BI using face recognition technology. Based on these data, this study can infer that parenting style and EV of using FRT were significantly influenced by ICI. Meanwhile, BI to use face recognition technology was highly correlated with EV and HP. The results of this study further reveal how parents&#x2019; experiential values and parenting styles influence their attitudes toward new technologies in the public environment of the campus.</p>
</sec>
</sec>
<sec id="s6">
<title>6 Conclusion</title>
<p>Face recognition technology is increasingly being used in a wide range of categories, including information confirmation, security, law enforcement, payments, and education. However, unlike other biometric features, FRT does not necessarily require active human cooperation in some public settings [<xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B49">49</xref>, <xref ref-type="bibr" rid="B50">50</xref>]. Its operation and analysis in public settings often does not require active human cooperation, and the use of FRT has the potential for misuse due to low information awareness and legal irregularities. Although some studies have been conducted on the acceptance of face recognition technology, there is still little scientific inquiry into parental BI of RFT use on campus in the specific context of education. The study systematically predicts the use of face recognition technology on campus, and has implications for future adoption in an educational context.</p>
<p>Specifically, the results of this study suggest that the more innovative preschool parents were, the greater the likelihood that they would embrace the use of facial recognition technology on campus. Also, the two influential variables this study discusses have noteworthy contributions. First, the findings suggest that when individuals are more satisfied with a product or technology that they have previously experienced, they are more likely to be interested in bringing related technologies to other areas of their lives [<xref ref-type="bibr" rid="B51">51</xref>]. Although this study explored BI regarding face recognition technology in education, the findings are expected to offer some lessons for other public settings or specific areas that require consumer behavioral intention surveys. Thus, for operators and users of face recognition technology, this study provides implications to improve the products and services to keep users&#x2019; information secure. Secondly, in educational scenarios, preschool parents showed BI about new technologies not only because of the value of the experience but also because of the parenting style they adhered to. To date, while many studies have examined the relationship between HP and parental supervision and child stress, this study revealed the relationship between HP and parental supervision through a specific form of their desire to enhance the supervision of their children&#x2019;s school lives outside of the home through face recognition technology.</p>
</sec>
</body>
<back>
<sec id="s7">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s8">
<title>Author Contributions</title>
<p>All authors contributed equally to the conception of the idea, implementing and analyzing the experimental results, writing the manuscript and reading and approving the final version of the manuscript.</p>
</sec>
<sec id="s9">
<title>Funding</title>
<p>This study was supported by the Ministry of Science and Technology, Taiwan, under grant MOST 109-2511-H-019-004-MY2.</p>
</sec>
<sec sec-type="COI-statement" id="s10">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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