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<article xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.3" article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">AJMA</journal-id>
      <journal-title-group>
        <journal-title>Asian Journal of Management and Accounting</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2963-4547</issn>
      <publisher>
        <publisher-name>Formosa Publisher</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.55927/ajma.v4i3.14762</article-id>
      <title-group>
        <article-title>The Effect of the Use of Paylater Services on Consumption Patterns Mediated by Personal Money Management in Generation Z with Socioeconomic Factors as Control Variables</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Novarianty</surname>
            <given-names>Terisha Andra</given-names>
          </name>
          <aff>Telkom University</aff>
          <email>terisaandra9@gmail.com</email>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Hidayat</surname>
            <given-names>Agus Maolana</given-names>
          </name>
          <aff>Telkom University</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Kartawinata</surname>
            <given-names>Budi Rustandi</given-names>
          </name>
          <aff>Telkom University</aff>
        </contrib>
      </contrib-group>
      <pub-date pub-type="epub">
        <day>17</day>
        <month>07</month>
        <year>2025</year>
      </pub-date>
      <history>
        <date date-type="received">
          <day>28</day>
          <month>05</month>
          <year>2025</year>
        </date>
        <date date-type="rev-recd">
          <day>12</day>
          <month>06</month>
          <year>2025</year>
        </date>
        <date date-type="accepted">
          <day>16</day>
          <month>07</month>
          <year>2025</year>
        </date>
      </history>
      <volume>4</volume>
      <issue>3</issue>
      <fpage>819</fpage>
      <lpage>836</lpage>
      <abstract>
        <p>This study aims to analyze the influence of the use of paylater services on the consumption patterns of Generation Z with personal money management as a mediating variable and socioeconomic factors as a control variable. Using a quantitative approach, data was collected through questionnaires from 275 respondents, with 200 test-worthy data analyzed using the Structural Equation Modeling (SEM) method through SmartPLS. The results of the study show that the use of paylater services has a positive effect on consumption patterns, and this influence is significantly mediated by personal money management. Socioeconomic factors also affect consumption behavior. These findings support the advancement of financial literacy and risk management among Generation Z in the context of the digital age.</p>
      </abstract>
      <kwd-group>
        <kwd>Paylater Services</kwd>
        <kwd>Consumption Patterns</kwd>
        <kwd>Generation Z</kwd>
        <kwd>Socio-Economic</kwd>
        <kwd>Structural Equation Model (SEM)</kwd>
        <kwd>SmartPLS</kwd>
      </kwd-group>
      <permissions>
        <license>
          <ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">http://creativecommons.org/licenses/by/4.0/</ali:license_ref>
          <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License.</license-p>
        </license>
      </permissions>
    </article-meta>
  </front>
  <body>

<sec>
  <title>INTRODUCTION</title>
  <disp-quote>
    <p>The rapid development of technology and the internet has changed
    people's lifestyles by utilizing access to digitalization and
    integrating the internet into daily life. The phenomenon of
    digitalization is increasingly penetrating various sectors,
    including the economy, which has prompted the birth of a new
    paradigm known as the digital economy (Komang et al., 2024). The
    concept of the digital economy refers to products and services that
    are traded electronically, including in the context of alternative
    payment method services that use technology to reduce dependence on
    cash. This approach, known as cashless payments, digital
    transactions, or electronic payments, has gained widespread
    acceptance around the world. The impact can be seen from a
    significant increase in the academic literature reflecting the
    growing interest in the adoption of cashless payments in the
    community, as well as a strong push to continue developing
    innovative &quot;digital payments&quot; concepts (Niankara &amp;
    Traoret, 2023). For example, paylater services are one type of
    digital payment that is popular today.</p>
    <p>Paylater has transformed how consumers engage with products and
    services. The development of the use of paylater services in
    Indonesia has shown a very significant growth trend in the last
    three years (Kredivo, 2024). The phenomenon of using paylater
    services in Indonesia is increasingly in demand, especially among
    Generation Z who are known as the digital native generation (Sitepu
    &amp; Fadila, 2024). At the end of 2022, the number of active
    paylater users was recorded at around 13.25 million people, with the
    dominance of young age groups and users increasingly expanding to
    various transaction categories, both online and offline (Bisnis.com,
    2024). Entering 2023, the number of active users increased to 13.4
    million people, with an annual growth of 1.12% (Bisnis.com, 2024).
    Paylater users are dominated by the age group of 26–35 years,
    followed by the age group of 36–45 years, and the proportion of male
    users is 56.5% and women are 43.5% (Kredivo, 2024). In addition,
    there has been a significant increase in usage categories such as
    food, health and beauty, and stationery (Kredivo, 2024). The regions
    with the highest number of users are West Java, DKI Jakarta, East
    Java, and Central Java (Bisnis.com, 2024). According to a survey
    conducted by Katadata Insight Center and Zigi, it is known that
    millennials and generation Z choose paylater payments as a
    transaction method. Generation Z is a generation born between 1997
    and 2012 (DJKN, 2021).</p>
    <p>At present, widely recognized paylater services include
    Spaylater, Kredivo, Gopaylater, Akulaku, Traveloka Paylater,
    Indodana, and Home Credit (Waluyo et al., 2022). Based on a study
    conducted by (Sitepu &amp; Fadila, 2024) on digital financial
    services, it is known that Spaylater or Shopee paylater has the most
    users at 78.4 percent, while the next order is Gopaylater, Kredivo,
    Akulaku, Traveloka Paylater and others. The number of paylater
    services in various types of e-commerce can help the community in
    financing, so that various needs of the community can be met without
    being constrained by financial availability. The development of
    financial technology not only improves the efficiency of financial
    management, but also has an impact on</p>
    <p>individual consumption patterns. In this context, financial
    technology (Fintech) has been shown to increase the tendency of
    individuals to choose long-term investments, although the drive for
    instant consumption remains. In addition, financial literacy and
    lifestyle are important mediators that determine the success of
    Fintech in improving financial well-being, especially among the
    younger generation (Munna et al., 2025).</p>
    <p>The trend of increasing paylater service users in Indonesia from
    13.25 million in 2022 to 14.37 million in 2024. an increase of 9.35%
    from the previous year (GoodStats, 2024). The total paylater loan
    portfolio also increased to IDR</p>
    <p><inline-graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_e6725605f9984b0e894893281b97825e/media/image3.jpeg" />30.14
    trillion, with outstanding financing receivables reaching IDR 6.13
    trillion as of March 2024, up 23.9% year-on-year (OJK, 2024). In
    addition, table 1.1 illustrates the total value of the loan
    portfolio and outstanding receivables which reached a significant
    figure in 2024, reflecting the massive expansion in paylater
    transaction volume as well as the increase in financial activity
    that occurred in this sector.</p>
  </disp-quote>
  <sec id="figure-1.-number-of-paylater-contracts-ojk-2024">
    <title>Figure 1. Number of Paylater Contracts (OJK 2024)</title>
    <disp-quote>
      <p>Figure 1. describes the paylater services that are widely used
      in Indonesia, the first Shopee Paylater, GoPaylater, Kredivo,
      Akulaku Paylater, etc. The development of e-commerce in Indonesia
      provides an easy way for paylater to develop. However, the ease of
      access to paylater services also poses the risk of debt dependence
      and impulse buying behavior that can negatively impact the
      financial condition of users (Mappadang et al., 2025). Despite
      providing convenience, OJK data (2024) shows that 23% of paylater
      users experience late payments that have the potential to damage
      credit scores. A study by Sakina Sihombing (2021) found that 34%
      of Generation Z experienced impulsive buying due to paylater with
      an average debt of IDR 1.2 million per person. Previous research
      confirms that materialistic traits have a strong relationship with
      impulsive and compulsive purchasing behaviors, especially when
      combined with digital payment methods such as credit cards or
      paylater services. The use of this payment method reinforces
      unplanned consumptive</p>
      <p>tendencies, which can negatively impact financial stability
      (Halim &amp; Christian, 2024).</p>
      <p>Given this background, conducting this research is highly
      relevant as it provides both scientific and practical
      contributions to understanding the dynamics surrounding the use of
      paylater services, personal financial management, and consumption
      patterns among Generation Z. In line with this, the study is
      entitled: &quot;The Use of Paylater Services on Consumption
      Patterns Mediated by Personal Money Management in Gen Z with
      Socioeconomic Factors as Control Variables.&quot;</p>
    </disp-quote>
  </sec>
</sec>












<sec>
  <title>LITERATURE REVIEW</title>
  <sec id="definition-of-paylater">
    <title>Definition of Paylater</title>
    <disp-quote>
      <p>Paylater is a financing method that enables consumers to
      purchase goods or services upfront and defer payment to a later
      date. This method is similar to a credit system, where service
      providers fund payments for goods or services acquired by users.
      After making a transaction, users will get a credit limit that
      needs to be repaid within a certain period, either in one payment
      or through installments (Waluyo et al., 2022).</p>
    </disp-quote>
  </sec>
  <sec id="use-of-paylater">
    <title>Use of Paylater</title>
    <disp-quote>
      <p>Paylater is widely used by e-commerce, marketplaces, digital
      companies, and others so that consumers can easily make
      transactions and meet their living needs (Ningsih Wijaya et al.,
      2024). Paylater services are a practical solution and can realize
      purchase desires that have not been achieved. (Waluyo et al.,
      2022) However, even though paylater provides convenience, if used
      inappropriately, it can cause financial risks, such as falling
      into consumptive debt. (Scott, et al., 2024)</p>
    </disp-quote>
  </sec>
  <sec id="socio-economic">
    <title>Socio-Economic</title>
    <disp-quote>
      <p><italic>Definition of Socio-Economic</italic></p>
      <p>Socioeconomics is a concept that refers to the social and
      economic conditions of individuals or groups in society, which
      includes aspects of income, education, employment, and other
      social status that affect consumption behavior and financial
      management (Fook &amp; McNeill, 2020).</p>
      <p>Max Weber's Theory of Stratification and Social Structure
      (1922) states that social class, economic status, and cultural
      background influence individual economic behavior and consumption
      patterns. In this context, socioeconomic variables function as
      moderation variables that affect perceptions and behaviors towards
      digital financial services, including financial management and
      consumption patterns.</p>
    </disp-quote>
  </sec>
  <sec id="personal-money-management">
    <title>Personal Money Management</title>
    <disp-quote>
      <p><italic>Definition of Financial Planning</italic></p>
      <p>Personal money management is the ability of individuals to plan
      and manage the use of funds effectively so that their living needs
      are met without causing financial problems in the future
      (Mappadang et al., 2025). The theory of financial planning
      emphasizes the importance of financial planning which</p>
      <p>includes cash flow management, expenditure control, investment,
      and risk management to achieve financial well-being (Yeo et al.,
      2024). The Self-Control Theory from Gottfredson &amp; Hirschi
      (1990) in A General Theory of Crime states that the level of
      self-control and discipline in financial management is important
      for maintaining healthy consumptive behavior. This concept was
      further developed by Bandura (1986) through Social Cognitive
      Theory, which highlights the importance of self-efficacy and
      social learning in shaping individuals' capacity to manage their
      finances independently. The Financial Decision Model from Mayer
      and Solomon (1999) states that personal money management is
      influenced by the perception of control, financial knowledge, and
      user experience, which directly influences consumption patterns
      and financial behavior.</p>
      <p>This research is unique compared to previous studies, such as
      Novarianty and Hidayat (2024) which only focused on the effect of
      the use of paylater on student financial management in Bandung in
      a descriptive manner. In contrast to that, this study covers the
      territory of Indonesia nationally and uses a quantitative approach
      with the SEM method, so that it is able to analyze the direct and
      indirect relationship between paylater use, personal money
      management, and consumption patterns more comprehensively. The
      addition of control variables in the form of socioeconomic factors
      also provides added value in understanding the consumption
      dynamics of Generation Z that have not been explained in previous
      research.</p>
    </disp-quote>
  </sec>
  <sec id="frame-of-mind">
    <title>Frame of Mind</title>
    <disp-quote>
      <p>Based on the theoretical study that has been described, a
      research framework can be made that can be described as
      follows:</p>
    </disp-quote>
    <graphic mimetype="image" mime-subtype="png" xlink:href="vertopal_e6725605f9984b0e894893281b97825e/media/image4.png" />
    <disp-quote>
      <p><bold>Figure 2. Frame of Mind</bold></p>
    </disp-quote>
  </sec>
</sec>














<sec>
  <title>METHODOLOGY</title>
  <disp-quote>
    <p>The research method employed in this thesis is quantitative with
    an associative approach, aiming to examine the relationships between
    two or more variables and to analyze both the direct and indirect
    effects among them.</p>
    <p>The data analysis technique applied in this study is Structural
    Equation Modeling (SEM) using the Partial Least Squares (PLS)
    approach, facilitated by SmartPLS software. The analysis is
    conducted in two primary stages:</p>
    <p>Outer Model Testing, to assess the validity and reliability of
    indicators in representing latent constructs. Inner Model Testing,
    to test the relationships between latent variables and test research
    hypotheses. Sampling was carried out using accidental sampling
    techniques, with the study population being Generation Z users of
    paylater services throughout Indonesia.</p>
  </disp-quote>
</sec>













<sec>
  <title>RESEARCH RESULT</title>
  <sec id="data-collection">
    <title>Data Collection</title>
    <disp-quote>
      <p>The respondents in this study were a sample of Generation Z as
      many as 410 respondents using questionnaires. Meanwhile, secondary
      data is obtained from literature sources, previous research, and
      journals. Of the total 410 questionnaires that were returned and
      could be processed, which could then be tested and analyzed. The
      rate of return of respondents can be seen in table 4.1</p>
      <p>Table 1. Questionnaire Return Rate</p>
    </disp-quote>
    <table-wrap>
      <label>Table 1. Questionnaire Return Rate</label>
      <table>
        <thead>
          <tr>
            <th align="center">Criterion</th>
            <th align="center">Sum</th>
            <th align="center">Percentage</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>Questionnaire distributed</td>
            <td align="center">410</td>
            <td align="center">100%</td>
          </tr>
          <tr>
            <td>Number of questionnaires that did not return</td>
            <td align="center">0</td>
            <td align="center">0%</td>
          </tr>
          <tr>
            <td>Incomplete number of questionnaires</td>
            <td align="center">35</td>
            <td align="center">9%</td>
          </tr>
          <tr>
            <td>Eligible questionnaire</td>
            <td align="center">375</td>
            <td align="center">91%</td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
  </sec>
  <sec id="respondent-characteristics">
    <title>Respondent Characteristics</title>
    <disp-quote>
      <p>This study examines the impact of paylater service usage on
      consumption patterns, with personal money management acting as a
      mediating variable, and socioeconomic factors serving as control
      variables among Generation Z. The research sample consists of 375
      individuals from Generation Z.</p>
    </disp-quote>
  </sec>
  <sec id="sem-pls-analysis">
    <title>SEM-PLS Analysis</title>
    <disp-quote>
      <p>The study employed the PLS-SEM method using SmartPLS software,
      chosen for its ability to analyze complex relationships between
      variables, including both direct and indirect effects (J. Hair et
      al., 2019). The choice of smartpls as an analytical tool is also
      because smartpls is able to process data with an unlimited number
      of samples, does not require normal data distribution, and can
      handle models with reflective and formative variables (Garson,
      2016).</p>
    </disp-quote>
  </sec>
  <sec id="measurement-modelouter-model">
    <title>Measurement Model/Outer model</title>
    <disp-quote>
      <p>Evaluation is carried out through convergent validity testing
      by looking at the Outer loading value&gt; 0.70 and the Average
      Variance Extracted value&gt; 0.50, discriminant validity through
      the Fornell-Larcker method, HTMT and cross loading, and construct
      reliability testing using Cronbach's Alpha and</p>
      <p>Composite Reliability values&gt; 0.70 (Hair et al., 2017). If
      all indicators meet these criteria, the measurement model is
      declared feasible and the analysis can proceed to the inner model
      stage.</p>
    </disp-quote>
    <graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_e6725605f9984b0e894893281b97825e/media/image5.jpeg" />
  </sec>
  <sec id="figure-3.-outer-model-testing">
    <title>Figure 3. Outer Model Testing</title>
    <disp-quote>
      <p><italic>Source: SmartPLS output, (2025)</italic></p>
    </disp-quote>
    <sec id="validity-test">
      <title>Validity Test</title>
      <disp-quote>
        <p>Convergent validity is demonstrated by an outer loading value
        ideally greater than 0.70 and an Average Variance Extracted
        (AVE) value exceeding</p>
        <p>0.50. Discriminant validity, on the other hand, is assessed
        using the Fornell- Larcker criterion, HTMT ratio, and
        cross-loading analysis to ensure that each construct is
        distinctly different from the others (Rosli et al., 2024). When
        indicators satisfy both convergent and discriminant validity
        criteria, it can be concluded that the constructs in the model
        meet the standards for acceptable validity.</p>
      </disp-quote>
    </sec>
    <sec id="convergent-validity">
      <title>Convergent Validity</title>
      <disp-quote>
        <p>In the analysis using SmartPLS, convergent validity is
        evaluated through two main indicators, namely the Outer loading
        value and Average Variance Extracted (AVE) (J. Hair et al.,
        2019). Indicators are said to be convergently valid if they have
        an Outer loading value above 0.70, and an AVE value exceeding
        0.50 (Garson, 2016).</p>
      </disp-quote>
    </sec>
    <sec id="outer-loading">
      <title>Outer loading</title>
      <disp-quote>
        <p>Table 2. Loading Factor Value of Each Indicator</p>
      </disp-quote>
      <table-wrap>
        <label>Table 2. Loading Factor Value of Each Indicator</label>
        <table>
          <thead>
            <tr>
              <th align="center">Yes</th>
              <th align="center">Indicator</th>
              <th align="center">Loading Factor</th>
              <th align="center">Information</th>
              <th align="center">Yes</th>
              <th align="center">Indicator</th>
              <th align="center">Loading Factor</th>
              <th align="center">Information</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="center">1</td>
              <td align="center">PK1</td>
              <td align="center">0,813</td>
              <td align="center">Valid</td>
              <td align="center">16</td>
              <td align="center">PP8</td>
              <td align="center">0,775</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">2</td>
              <td align="center">PK2</td>
              <td align="center">0,756</td>
              <td align="center">Valid</td>
              <td align="center">17</td>
              <td align="center">PL1</td>
              <td align="center">0,814</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">3</td>
              <td align="center">PK3</td>
              <td align="center">0,767</td>
              <td align="center">Valid</td>
              <td align="center">18</td>
              <td align="center">PL2</td>
              <td align="center">0,771</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">4</td>
              <td align="center">PK4</td>
              <td align="center">0,776</td>
              <td align="center">Valid</td>
              <td align="center">19</td>
              <td align="center">PL3</td>
              <td align="center">0,796</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">5</td>
              <td align="center">PK5</td>
              <td align="center">0,793</td>
              <td align="center">Valid</td>
              <td align="center">20</td>
              <td align="center">PL4</td>
              <td align="center">0,782</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">6</td>
              <td align="center">PK6</td>
              <td align="center">0,766</td>
              <td align="center">Valid</td>
              <td align="center">21</td>
              <td align="center">PL5</td>
              <td align="center">0,773</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">7</td>
              <td align="center">PK7</td>
              <td align="center">0,819</td>
              <td align="center">Valid</td>
              <td align="center">22</td>
              <td align="center">PL6</td>
              <td align="center">0,725</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">8</td>
              <td align="center">PK8</td>
              <td align="center">0,805</td>
              <td align="center">Valid</td>
              <td align="center">23</td>
              <td align="center">PL7</td>
              <td align="center">0,776</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">9</td>
              <td align="center">PP1</td>
              <td align="center">0,743</td>
              <td align="center">Valid</td>
              <td align="center">24</td>
              <td align="center">PL8</td>
              <td align="center">0,742</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">10</td>
              <td align="center">PP2</td>
              <td align="center">0,787</td>
              <td align="center">Valid</td>
              <td align="center">25</td>
              <td align="center">SE1</td>
              <td align="center">0,820</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">11</td>
              <td align="center">PP3</td>
              <td align="center">0,830</td>
              <td align="center">Valid</td>
              <td align="center">26</td>
              <td align="center">SE2</td>
              <td align="center">0,769</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">12</td>
              <td align="center">PP4</td>
              <td align="center">0,793</td>
              <td align="center">Valid</td>
              <td align="center">27</td>
              <td align="center">SE3</td>
              <td align="center">0,792</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">13</td>
              <td align="center">PP5</td>
              <td align="center">0,766</td>
              <td align="center">Valid</td>
              <td align="center">28</td>
              <td align="center">SE4</td>
              <td align="center">0,788</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">14</td>
              <td align="center">PP6</td>
              <td align="center">0,789</td>
              <td align="center">Valid</td>
              <td align="center">29</td>
              <td align="center">SE5</td>
              <td align="center">0,764</td>
              <td align="center">Valid</td>
            </tr>
            <tr>
              <td align="center">15</td>
              <td align="center">PP7</td>
              <td align="center">0,782</td>
              <td align="center">Valid</td>
              <td align="center">30</td>
              <td align="center">SE6</td>
              <td align="center">0,786</td>
              <td align="center">Valid</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPls Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>The analysis results revealed that the outer loading values
        for all indicators exceeded 0.70, indicating a strong and
        significant association between each indicator and its
        corresponding construct. This high loading value suggests that
        the indicators accurately represent their respective constructs,
        demonstrating a solid level of measurement validity.
        Consequently, it can be concluded that all indicators in the
        model meet the established criteria, exhibit high reliability,
        and are appropriate for inclusion in the research without the
        need for elimination.</p>
      </disp-quote>
    </sec>
    <sec id="average-variance-extracted-ave">
      <title>Average Variance Extracted (AVE)</title>
      <disp-quote>
        <p>Table 3. Average Variance Extracted Test Results</p>
      </disp-quote>
      <table-wrap>
        <label>Table 3. Average Variance Extracted Test Results</label>
        <table>
          <thead>
            <tr>
              <th align="left">Variabel</th>
              <th align="center">Average Variance Extracted (AVE)</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Consumption Patterns</td>
              <td align="center">0,619</td>
            </tr>
            <tr>
              <td>Personal Money Management</td>
              <td align="center">0,614</td>
            </tr>
            <tr>
              <td>Paylater</td>
              <td align="center">0,597</td>
            </tr>
            <tr>
              <td>Socio-Economic</td>
              <td align="center">0,619</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source : SmartPls Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>The analysis results indicated that the Average Variance
        Extracted (AVE) value for each variable exceeded 0.50,
        signifying that each construct in the study model was capable of
        explaining more than 50% of the variance in its associated
        indicators. This affirms that all variables in the measurement
        model demonstrate strong convergent validity, meaning the
        indicators effectively represent their respective constructs and
        possess sufficient measurement quality. Therefore, all AVE
        values meet the established standards, further supporting the
        validity of the constructs within the research model.</p>
      </disp-quote>
    </sec>
    <sec id="discriminant-validity">
      <title>Discriminant Validity</title>
      <disp-quote>
        <p>Discriminant validity refers to the degree to which a
        construct in a measurement model is truly distinct from other
        constructs, ensuring that each one captures phenomena not
        represented by others. In SmartPLS, discriminant validity is
        assessed using several methods, including the Fornell-Larcker
        Criterion, the Heterotrait-Monotrait Ratio (HTMT), and cross
        loading analysis. Fornell-Larcker Criterion.</p>
        <p>Tabel 4. Fornell-Larcker Criterion Test</p>
      </disp-quote>
      <table-wrap>
        <label>Tabel 4. Fornell-Larcker Criterion Test</label>
        <table>
          <thead>
            <tr>
              <th align="left" rowspan="2">Variabel</th>
              <th align="center" colspan="4"></th>
            </tr>
            <tr>
              <th align="center">Paylater</th>
              <th align="center">Personal Money Management</th>
              <th align="center">Consumption Patterns</th>
              <th align="center">Socio-Economic</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Paylater</td>
              <td align="center">0,773</td>
              <td></td>
              <td></td>
              <td></td>
            </tr>
            <tr>
              <td>Personal Money Management</td>
              <td align="center">0,449</td>
              <td align="center">0,784</td>
              <td></td>
              <td></td>
            </tr>
            <tr>
              <td>Consumption Patterns</td>
              <td align="center">0,570</td>
              <td align="center">0,753</td>
              <td align="center">0,787</td>
              <td></td>
            </tr>
            <tr>
              <td>Socio-Economic</td>
              <td align="center">0,726</td>
              <td align="center">0,526</td>
              <td align="center">0,604</td>
              <td align="center">0,787</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPls Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>Based on the analysis results, the square root of the Average
        Variance Extracted (AVE) for each construct was greater than its
        correlations with other constructs. This indicates that all
        variables in the model fulfill the Fornell- Larcker Criterion,
        demonstrating strong discriminant validity. Thus, it can be
        concluded that the constructs in this model can be clearly
        distinguished from each other.</p>
      </disp-quote>
    </sec>
    <sec id="heterotrait-monotrait-ratio-htmt">
      <title>Heterotrait-Monotrait Ratio (HTMT)</title>
      <disp-quote>
        <p>Table 5. Heterotrait-Monotrait (HTMT)</p>
      </disp-quote>
      <table-wrap>
        <label>Table 5. Heterotrait-Monotrait (HTMT)</label>
        <table>
          <thead>
            <tr>
              <th align="left" rowspan="2">Variabel</th>
              <th align="center">Paylater</th>
              <th align="center">Personal Money Management</th>
              <th align="center">Consumption Patterns</th>
              <th align="center">Socio-Economic</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Paylater</td>
              <td></td>
              <td></td>
              <td></td>
              <td></td>
            </tr>
            <tr>
              <td>Personal Money Management</td>
              <td align="center">0,495</td>
              <td></td>
              <td></td>
              <td></td>
            </tr>
            <tr>
              <td>Consumption Patterns</td>
              <td align="center">0,626</td>
              <td align="center">0,823</td>
              <td></td>
              <td></td>
            </tr>
            <tr>
              <td>Socio-Economic</td>
              <td align="center">0,814</td>
              <td align="center">0,587</td>
              <td align="center">0,674</td>
              <td></td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPls Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>Based on the table above, the HTMT value for each variable is
        less than 0.9, so each variable meets the HTMT prerequisites and
        meets the Discriminant Validity.</p>
      </disp-quote>
    </sec>
    <sec id="cross-loading">
      <title>Cross Loading</title>
      <disp-quote>
        <p>Table 6. Cross Loading Value of Each Indicator</p>
      </disp-quote>
      <table-wrap>
        <label>Table 6. Cross Loading Value of Each Indicator</label>
        <table>
          <thead>
            <tr>
              <th align="left">Indicator</th>
              <th align="center">Paylater</th>
              <th align="center">Personal Money Management</th>
              <th align="center">Consumption Patterns</th>
              <th align="center">Socio-Economic</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>PK1</td>
              <td align="center">0,479</td>
              <td align="center">0,585</td>
              <td align="center"><b>0,813</b></td>
              <td align="center">0,518</td>
            </tr>
            <tr>
              <td>PK2</td>
              <td align="center">0,466</td>
              <td align="center">0,546</td>
              <td align="center"><b>0,756</b></td>
              <td align="center">0,489</td>
            </tr>
            <tr>
              <td>PK3</td>
              <td align="center">0,507</td>
              <td align="center">0,540</td>
              <td align="center"><b>0,767</b></td>
              <td align="center">0,485</td>
            </tr>
            <tr>
              <td>PK4</td>
              <td align="center">0,398</td>
              <td align="center">0,599</td>
              <td align="center"><b>0,776</b></td>
              <td align="center">0,455</td>
            </tr>
            <tr>
              <td>PK5</td>
              <td align="center">0,439</td>
              <td align="center">0,649</td>
              <td align="center"><b>0,793</b></td>
              <td align="center">0,473</td>
            </tr>
            <tr>
              <td>PK6</td>
              <td align="center">0,446</td>
              <td align="center">0,618</td>
              <td align="center"><b>0,766</b></td>
              <td align="center">0,449</td>
            </tr>
            <tr>
              <td>PK7</td>
              <td align="center">0,456</td>
              <td align="center">0,583</td>
              <td align="center"><b>0,819</b></td>
              <td align="center">0,473</td>
            </tr>
            <tr>
              <td>PK8</td>
              <td align="center">0,400</td>
              <td align="center">0,609</td>
              <td align="center"><b>0,805</b></td>
              <td align="center">0,460</td>
            </tr>
            <tr>
              <td>PL1</td>
              <td align="center"><b>0,814</b></td>
              <td align="center">0,366</td>
              <td align="center">0,501</td>
              <td align="center">0,594</td>
            </tr>
            <tr>
              <td>PL2</td>
              <td align="center"><b>0,771</b></td>
              <td align="center">0,337</td>
              <td align="center">0,442</td>
              <td align="center">0,544</td>
            </tr>
            <tr>
              <td>PL3</td>
              <td align="center"><b>0,796</b></td>
              <td align="center">0,358</td>
              <td align="center">0,482</td>
              <td align="center">0,602</td>
            </tr>
            <tr>
              <td>PL4</td>
              <td align="center"><b>0,782</b></td>
              <td align="center">0,341</td>
              <td align="center">0,439</td>
              <td align="center">0,561</td>
            </tr>
            <tr>
              <td>PL5</td>
              <td align="center"><b>0,773</b></td>
              <td align="center">0,343</td>
              <td align="center">0,415</td>
              <td align="center">0,554</td>
            </tr>
            <tr>
              <td>PL6</td>
              <td align="center"><b>0,725</b></td>
              <td align="center">0,342</td>
              <td align="center">0,358</td>
              <td align="center">0,497</td>
            </tr>
            <tr>
              <td>PL7</td>
              <td align="center"><b>0,776</b></td>
              <td align="center">0,314</td>
              <td align="center">0,441</td>
              <td align="center">0,553</td>
            </tr>
            <tr>
              <td>PL8</td>
              <td align="center"><b>0,742</b></td>
              <td align="center">0,375</td>
              <td align="center">0,430</td>
              <td align="center">0,577</td>
            </tr>
            <tr>
              <td>PP1</td>
              <td align="center">0,376</td>
              <td align="center"><b>0,743</b></td>
              <td align="center">0,492</td>
              <td align="center">0,496</td>
            </tr>
            <tr>
              <td>PP2</td>
              <td align="center">0,402</td>
              <td align="center"><b>0,787</b></td>
              <td align="center">0,586</td>
              <td align="center">0,458</td>
            </tr>
            <tr>
              <td>PP3</td>
              <td align="center">0,347</td>
              <td align="center"><b>0,830</b></td>
              <td align="center">0,662</td>
              <td align="center">0,434</td>
            </tr>
            <tr>
              <td>PP4</td>
              <td align="center">0,352</td>
              <td align="center"><b>0,793</b></td>
              <td align="center">0,613</td>
              <td align="center">0,372</td>
            </tr>
            <tr>
              <td>PP5</td>
              <td align="center">0,335</td>
              <td align="center"><b>0,766</b></td>
              <td align="center">0,626</td>
              <td align="center">0,359</td>
            </tr>
            <tr>
              <td>PP6</td>
              <td align="center">0,334</td>
              <td align="center"><b>0,789</b></td>
              <td align="center">0,589</td>
              <td align="center">0,405</td>
            </tr>
            <tr>
              <td>PP7</td>
              <td align="center">0,315</td>
              <td align="center"><b>0,782</b></td>
              <td align="center">0,547</td>
              <td align="center">0,371</td>
            </tr>
            <tr>
              <td>PP8</td>
              <td align="center">0,354</td>
              <td align="center"><b>0,775</b></td>
              <td align="center">0,589</td>
              <td align="center">0,399</td>
            </tr>
            <tr>
              <td>SE1</td>
              <td align="center">0,605</td>
              <td align="center">0,402</td>
              <td align="center">0,521</td>
              <td align="center"><b>0,820</b></td>
            </tr>
            <tr>
              <td>SE2</td>
              <td align="center">0,544</td>
              <td align="center">0,372</td>
              <td align="center">0,410</td>
              <td align="center"><b>0,769</b></td>
            </tr>
            <tr>
              <td>SE3</td>
              <td align="center">0,580</td>
              <td align="center">0,434</td>
              <td align="center">0,492</td>
              <td align="center"><b>0,792</b></td>
            </tr>
            <tr>
              <td>SE4</td>
              <td align="center">0,579</td>
              <td align="center">0,415</td>
              <td align="center">0,465</td>
              <td align="center"><b>0,788</b></td>
            </tr>
            <tr>
              <td>SE5</td>
              <td align="center">0,590</td>
              <td align="center">0,407</td>
              <td align="center">0,480</td>
              <td align="center"><b>0,764</b></td>
            </tr>
            <tr>
              <td>SE6</td>
              <td align="center">0,527</td>
              <td align="center">0,446</td>
              <td align="center">0,475</td>
              <td align="center"><b>0,786</b></td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPLS Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>Based on the cross loading values of each indicator in this
        study, all indicators showed values above 0.700 and had the
        strongest correlation with their respective latent variables.
        Therefore, all indicators are considered valid and there is no
        need to remove any from the measurement model.</p>
      </disp-quote>
    </sec>
    <sec id="reliability-test">
      <title>Reliability Test</title>
      <disp-quote>
        <p>One commonly used way to test reliability in a measurement
        model is to calculate Cronbach's Alpha and Composite Reliability
        values.</p>
      </disp-quote>
    </sec>
    <sec id="cronbachs-alpha">
      <title>Cronbach’s Alpha</title>
      <disp-quote>
        <p>The Cronbach’s Alpha values for all variables are presented
        in the table below.</p>
        <p>Table 7. Cronbach's Alpha Value of Each Variable</p>
      </disp-quote>
      <table-wrap>
        <label>Table 7. Cronbach's Alpha Value of Each Variable</label>
        <table>
          <thead>
            <tr>
              <th align="left">Variabel</th>
              <th align="center">Cronbach's Alpha</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Consumption Patterns</td>
              <td align="center">0,912</td>
            </tr>
            <tr>
              <td>Personal Money Management</td>
              <td align="center">0,910</td>
            </tr>
            <tr>
              <td>Paylater</td>
              <td align="center">0,903</td>
            </tr>
            <tr>
              <td>Socio-Economic</td>
              <td align="center">0,877</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPls Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>The table above indicates that all variables have Cronbach’s
      Alpha</p>
      <p>values exceeding 0.7, demonstrating a strong level of internal
      consistency across</p>
      <disp-quote>
        <p>measurements. Therefore, all indicators are deemed reliable
        and suitable for inclusion in the study, with no need for
        elimination.</p>
      </disp-quote>
    </sec>
    <sec id="composite-reliability">
      <title>Composite Reliability</title>
      <disp-quote>
        <p>The Composite Reliability values for each variable are shown
        in the table below.</p>
        <p>Table 8. Composite Reliability Results of Each Variable</p>
      </disp-quote>
      <table-wrap>
        <label>Table 8. Composite Reliability Results of Each Variable</label>
        <table>
          <thead>
            <tr>
              <th align="left">Variabel</th>
              <th align="center">Composite Reliability</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Pola Konsumsi</td>
              <td align="center">0,929</td>
            </tr>
            <tr>
              <td>Pengelolaan Uang Pribadi</td>
              <td align="center">0,927</td>
            </tr>
            <tr>
              <td>Paylater</td>
              <td align="center">0,922</td>
            </tr>
            <tr>
              <td>Sosial Ekonomi</td>
              <td align="center">0,907</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPls Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>Based on the Composite Reliability values, all variables
        scored above 0.700, indicating that each construct meets the
        required reliability standard. Therefore, all indicators are
        considered reliable and can be retained in the study without the
        need for removal.</p>
        <p><italic><bold>Structural Model /Inner
        Model</bold></italic></p>
        <graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_e6725605f9984b0e894893281b97825e/media/image6.jpeg" />
        <p><italic><bold>Figure 4. Inner Test Model</bold></italic></p>
        <p><italic>Source: SmartPLS Output, (2025)</italic></p>
      </disp-quote>
    </sec>
  </sec>
  <sec id="R Square">
    <title>R Square</title>
    <disp-quote>
      <p>Table 9. R Square Test Results</p>
    </disp-quote>
    <table-wrap>
      <label>Table 9. R Square Test Results</label>
      <table>
        <thead>
          <tr>
            <th align="left">Variabel</th>
            <th align="center">R Square</th>
            <th align="center">R Square Adjusted</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>Personal Money Management</td>
            <td align="center">0,286</td>
            <td align="center">0,282</td>
          </tr>
          <tr>
            <td>Consumption Patterns</td>
            <td align="center">0,644</td>
            <td align="center">0,641</td>
          </tr>
        </tbody>
      </table>
      <table-wrap-foot>
        <fn>
          <p><italic>Source: SmartPLS Output, (2025)</italic></p>
        </fn>
      </table-wrap-foot>
    </table-wrap>
    <disp-quote>
      <p>Based on the R Square test results, the Consumption Pattern
      variable is influenced by its independent variables by 0.644, or
      64.4%, indicating a substantial explanatory power. The remaining
      35.6% is attributed to other factors outside the scope of this
      study. Additionally, the Personal Money Management variable is
      explained by its independent variable by 0.286, or 28.6%, while
      the remaining 71.4% is influenced by other variables not examined
      in this research.</p>
    </disp-quote>
    <sec id="q-square">
      <title>Q Square</title>
      <disp-quote>
        <p>Table 10. Q Square Results</p>
      </disp-quote>
      <table-wrap>
        <label>Table 10. Q Square Results</label>
        <table>
          <thead>
            <tr>
              <th align="left">Variabel</th>
              <th align="center">Q Square</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Personal Money Management</td>
              <td align="center">0,171</td>
            </tr>
            <tr>
              <td>Consumption Patterns</td>
              <td align="center">0,392</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPLS Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>From the table above, it is evident that the Q Square value
        for the Consumption Pattern variable is 0.392, while for the
        Personal Money Management variable it is 0.171. Since both
        values are greater than 0, it can be concluded that the
        independent variables in the model have a good predictive
        ability in explaining the Consumption Pattern and Personal Money
        Management variables. Therefore, the structural model used in
        this study demonstrates adequate predictive relevance.</p>
      </disp-quote>
    </sec>
    <sec id="model-fit">
      <title>Model Fit</title>
      <disp-quote>
        <p>The accepted SRMR value is below 0.100, which indicates that
        the model fits the existing data. If the SRMR value is higher
        than the threshold, then the model is considered unfit and needs
        to be adjusted further (Henseler et al., 2016). Here are the
        test results of Model fit:</p>
        <p>Table 11. Output Model Fit</p>
      </disp-quote>
      <table-wrap>
        <label>Table 11. Output Model Fit</label>
        <table>
          <thead>
            <tr>
              <th align="left">Indicator</th>
              <th align="center">Saturated Model</th>
              <th align="center">Estimated Model</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>SRMR</td>
              <td align="center">0,056</td>
              <td align="center">0,056</td>
            </tr>
            <tr>
              <td>d_ULS</td>
              <td align="center">1,457</td>
              <td align="center">1,457</td>
            </tr>
            <tr>
              <td>d_G</td>
              <td align="center">1,044</td>
              <td align="center">1,044</td>
            </tr>
            <tr>
              <td>Chi-Square</td>
              <td align="center">1937,800</td>
              <td align="center">1937,800</td>
            </tr>
            <tr>
              <td>NFI</td>
              <td align="center">0,763</td>
              <td align="center">0,763</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPLS Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>From the table above, it can be seen that the SRMR value in
        the saturated model is 0.056 &lt; 0.100 and the estimated model
        is 0.056 &lt; 0.100, so the model formed is declared to meet the
        feasibility of the model.</p>
      </disp-quote>
    </sec>
    <sec id="hypothesis-test">
      <title>Hypothesis Test</title>
      <disp-quote>
        <p>This hypothesis test is important to ensure that the
        developed model can describe the relationships between variables
        in a valid and generalizable manner. Here are the results of the
        bootstrapping path coefficients test:</p>
        <p>Table 12. Hypothesis Test Results</p>
      </disp-quote>
      <table-wrap>
        <label>Table 12. Hypothesis Test Results</label>
        <table>
          <thead>
            <tr>
              <th align="left">Construct</th>
              <th align="center">Original Sample (O)</th>
              <th align="center">T Statistics (|O/STDEV|)</th>
              <th align="center">P Values</th>
              <th align="center">Hipotesis</th>
              <th align="center">Information</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Paylater -&gt; Consumption Patterns</td>
              <td align="center">0,195</td>
              <td align="center">3,310</td>
              <td align="center">0,001</td>
              <td align="center">H1</td>
              <td align="center">Accepted</td>
            </tr>
            <tr>
              <td>Socio-Economics -&gt; Consumption Patterns</td>
              <td align="center">0,156</td>
              <td align="center">2,304</td>
              <td align="center">0,021</td>
              <td align="center">H2</td>
              <td align="center">Accepted</td>
            </tr>
            <tr>
              <td>Paylater -&gt; Personal Money Management</td>
              <td align="center">0,143</td>
              <td align="center">2,321</td>
              <td align="center">0,020</td>
              <td align="center">H3</td>
              <td align="center">Accepted</td>
            </tr>
            <tr>
              <td>Socio-Economics -&gt; Personal Money Management</td>
              <td align="center">0,422</td>
              <td align="center">6,361</td>
              <td align="center">0,000</td>
              <td align="center">H4</td>
              <td align="center">Accepted</td>
            </tr>
            <tr>
              <td>Personal Money Management -&gt; Consumption Patterns</td>
              <td align="center">0,583</td>
              <td align="center">7,282</td>
              <td align="center">0,000</td>
              <td align="center">H5</td>
              <td align="center">Accepted</td>
            </tr>
            <tr>
              <td>Paylater -&gt; Personal Money Management -&gt; Consumption Patterns</td>
              <td align="center">0,084</td>
              <td align="center">2,029</td>
              <td align="center">0,042</td>
              <td align="center">H6</td>
              <td align="center">Accepted</td>
            </tr>
            <tr>
              <td>Socio-Economics -&gt; Personal Money Management -&gt; Consumption Patterns</td>
              <td align="center">0,246</td>
              <td align="center">4,229</td>
              <td align="center">0,000</td>
              <td align="center">H7</td>
              <td align="center">Accepted</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><italic>Source: SmartPLS Output, (2025)</italic></p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <disp-quote>
        <p>Based on the table above, the following conclusions can be
        drawn:</p>
      </disp-quote>
      <list list-type="bullet">
        <list-item>
          <p>Paylater to Consumption Pattern has an Original Sample
          value of 0.195, a Statistical T value of 3.310 &gt; 1.96, and
          a P Value of 0.001 &lt; 0.05. Thus, it can be concluded that
          Paylater has a significant positive effect on Consumption
          Patterns, so the H1 Hypothesis is accepted.</p>
        </list-item>
        <list-item>
          <p>Socio-Economic on Consumption Patterns has an Original
          Sample value of 0.156, a Statistical T value of 2.304 &gt;
          1.96, and a P Value of 0.021 &lt; 0.05. Thus, it can be
          concluded that Socio-Economics has a significant positive
          effect on Consumption Patterns, so the H2 Hypothesis is
          accepted.</p>
        </list-item>
        <list-item>
          <p>Paylater for Personal Money Management has an Original
          Sample value of 0.143, a Statistical T value of 2.321 &gt;
          1.96, and a P Value of 0.020 &lt; 0.05. Thus, it can be
          concluded that Paylater has a significant positive effect on
          Personal Money Management, so the H3 Hypothesis is
          accepted.</p>
        </list-item>
        <list-item>
          <p>Socio-Economics on Personal Money Management has an
          Original Sample value of 0.422, a Statistical T value of 6.361
          &gt; 1.96, and a P value of 0.000 &lt;</p>
        </list-item>
      </list>
      <disp-quote>
        <p>0.05. Thus, it can be concluded that Socio-Economics has a
        significant positive effect on Personal Money Management, so the
        H4 Hypothesis is accepted.</p>
      </disp-quote>
      <list list-type="bullet">
        <list-item>
          <p>Personal Money Management on Consumption Patterns has an
          Original Sample value of 0.583, a Statistical T value of 7.282
          &gt; 1.96, and a P Value of</p>
        </list-item>
      </list>
      <disp-quote>
        <p>0.000 &lt; 0.05. Thus, it can be concluded that Personal
        Money Management</p>
        <p>has a significant positive effect on Consumption Patterns, so
        the H5 Hypothesis is accepted.</p>
      </disp-quote>
      <list list-type="bullet">
        <list-item>
          <p>Paylater on Consumption Patterns through Personal Money
          Management has an Original Sample value of 0.084, a
          Statistical T value of 2.029 &gt; 1.96, and a P Value of 0.042
          &lt; 0.05. Thus, it can be concluded that there is a
          significant positive influence of Paylater on Consumption
          Patterns through Personal Money Management, so the H6
          Hypothesis is accepted.</p>
        </list-item>
        <list-item>
          <p>Socio-Economics on Consumption Patterns through Personal
          Money Management has an Original Sample value of 0.246, a
          Statistical T value of</p>
        </list-item>
      </list>
      <disp-quote>
        <p>4.229 &gt; 1.96, and a P Value of 0.000 &lt; 0.05. Thus, it
        can be concluded that there is a significant positive influence
        of Socio-Economic on Consumption Patterns through Personal Money
        Management, so the H7 Hypothesis is accepted.</p>
      </disp-quote>
    </sec>
  </sec>
</sec>










<sec>
  <title>CONCLUSIONS AND RECOMMENDATIONS</title>
  <sec id="conclusion">
    <title>Conclusion</title>
    <disp-quote>
      <p>This study finds that the use of paylater services
      significantly influences the consumption patterns of Generation Z.
      This indicates a meaningful relationship, as paylater allows
      individuals to purchase desired goods without the need for
      immediate cash, making their consumption behavior more closely
      linked to the availability and use of such services. Additionally,
      paylater usage shows a significant positive effect on personal
      money management—suggesting that responsible use of paylater
      services is associated with improved financial management among
      Generation Z. Furthermore, socio-economic factors are shown to
      have a causal impact on personal money management; the better an
      individual's socio-economic status, the more prudent and effective
      their money management tends to be.</p>
      <p>Personal Money Management has a significant positive influence
      on Consumption Patterns. Effective financial management enables
      individuals to closely monitor their spending, recognize patterns,
      and identify which expenses can be reduced or eliminated. This
      study also confirms that good personal money management plays a
      key mediating role in the relationship between paylater usage and
      consumption patterns. With adequate financial knowledge and
      discipline, Generation Z can utilize paylater services responsibly
      while minimizing potential negative impacts on their consumption
      behavior.</p>
    </disp-quote>
  </sec>
  <sec id="recommendation">
    <title>Recommendation</title>
    <disp-quote>
      <p>To improve financial management and consumption patterns of
      Generation Z, especially in the context of using paylater
      services. First, there is a need to increase financial literacy
      that is comprehensive and sustainable through educational programs
      involving various parties, such as educational institutions,
      fintech platforms, and the government, so that the younger
      generation is able to understand the risks and benefits of using
      digital financial services wisely. Second, fintech platforms and
      paylater service providers are expected to develop educational
      features and spending restrictions aimed at minimizing excessive
      consumptive behavior and uncontrolled debt.</p>
      <p>Furthermore, it is important to pay attention to socio-economic
      aspects in designing educational strategies so that the approach
      provided is more relevant and effective according to the
      conditions of each individual. Finally, this study also recommends
      the need to develop stricter policies and regulations to protect
      consumers, especially the younger generation, from potential
      financial risks resulting from the use of paylater services, so
      that they can manage their finances responsibly in the digital
      era.</p>
    </disp-quote>
  </sec>
</sec>










<sec>
  <title>ADVANCED RESEARCH</title>
  <disp-quote>
    <p>Future research is recommended to adopt a longitudinal approach
    to observe long-term changes in paylater usage behavior and personal
    financial management. Additionally, upcoming studies could
    incorporate psychographic variables such as impulsivity, financial
    anxiety, and attitudes toward digital financial services to gain
    deeper insights. Comparative research across generations, such as
    between Generation Z and Millennials, may also reveal behavioral
    differences influenced by age and experience. The use of mixed
    methods will enrich the findings by combining quantitative and
    qualitative data. Furthermore, expanding the research scope to
    regional or cross-country contexts by considering regulatory factors
    and the maturity of the fintech ecosystem can provide broader policy
    implications. Finally, developing financial literacy-based
    intervention models linked to the mediating role of personal money
    management should be tested experimentally to evaluate their
    effectiveness in promoting more responsible consumption
    behavior.</p>
  </disp-quote>
</sec>












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