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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">IJAR</journal-id>
      <journal-title-group>
        <journal-title>Indonesian Journal of Advanced Research</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2986-0768</issn>
      <publisher>
        <publisher-name>Formosa Publisher</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.55927/ijar.v4i7.15037</article-id>
      <title-group>
        <article-title>Flexible Time to Enhance Shopping Need and Customer Visit on the Shopee Website</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Sari</surname>
            <given-names>Dian Ratna</given-names>
          </name>
          <aff>Mulawarman University</aff>
          <email>drs0598@gmail.com</email>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Indriastuti</surname>
            <given-names>Herning</given-names>
          </name>
          <aff>Mulawarman University</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Rahmawati</surname>
            <given-names>Heni Rahayu</given-names>
          </name>
          <aff>Mulawarman University</aff>
        </contrib>
      </contrib-group>
      <pub-date pub-type="epub">
        <day>30</day>
        <month>07</month>
        <year>2025</year>
      </pub-date>
      <history>
        <date date-type="received">
          <day>14</day>
          <month>05</month>
          <year>2025</year>
        </date>
        <date date-type="rev-recd">
          <day>28</day>
          <month>06</month>
          <year>2025</year>
        </date>
        <date date-type="accepted">
          <day>30</day>
          <month>07</month>
          <year>2025</year>
        </date>
      </history>
      <volume>4</volume>
      <issue>7</issue>
      <fpage>1653</fpage>
      <lpage>1672</lpage>
      <abstract>
        <p>This study aims to analyze the effect of flexible time on shopping need and customer visit on the Shopee platform. Using a quantitative approach with SEM-PLS, the findings reveal that flexible time directly and indirectly influences customer visits through shopping need. The results support the Stimulus-Organism-Response (SOR) model in explaining digital consumer behavior. Flexible time enhances psychological comfort and internal motivation to shop and engage with the platform. The study implies that time-based marketing strategies in e-commerce are essential to boost user engagement and loyalty.</p>
      </abstract>
      <kwd-group>
        <kwd>Flexible Time</kwd>
        <kwd>Shopping Need</kwd>
        <kwd>Customer Visit</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 advancement of digital technology has brought about a
    significant transformation in consumer behavior, particularly within
    the realm of electronic commerce (e-commerce). Platforms such as
    Shopee have become an integral part of everyday shopping activities,
    driven by high internet penetration and the widespread adoption of
    mobile devices (dataSpring, 2021). In Indonesia, Shopee has emerged
    as the leading e-commerce site in terms of user visits, recording
    over 134 million visits in September 2024, far surpassing its main
    competitors, including Tokopedia, Lazada, and Blibli (Semrush,
    2024). This phenomenon not only reflects market dominance but also
    illustrates the evolving dynamics of consumer behavior within an
    increasingly competitive digital environment.</p>
    <p>Amid this rapid growth, the factors influencing customer visits
    have become a primary focus in digital marketing research. Customer
    visits represent both exploratory and transactional consumer
    intentions, influenced by elements such as user experience,
    promotional stimuli, ease of access, and importantly, time
    availability (Srinivasan et al., 2016; Wang et al., 2019). One
    dimension that has remained underexplored in existing literature,
    yet holds substantial relevance in today’s digital economy, is
    flexible time the availability of discretionary time outside routine
    work or primary daily activities, allowing consumers to engage more
    meaningfully with e-commerce platforms.</p>
    <p>Recent trends suggest that access to digital platforms tends to
    increase during evening hours and weekends, when consumers have
    greater flexibility to browse and make transactions. Previous
    studies have acknowledged the influence of time on
    information-seeking behavior and purchasing decisions, as well as
    the relationship between navigation experience and access time
    (Basalla et al., 2021; Necula, 2023). However, there is a notable
    lack of comprehensive investigations that simultaneously link
    flexible time to shopping need,the internal motivation to shop and
    customer visit behavior. This highlights a significant conceptual
    and empirical gap in the literature on online consumer behavior.</p>
    <p>Although variables such as pricing, promotions, and user
    interface have been widely examined as determinants of purchase
    decisions and site visits, time as an independent construct has
    received insufficient scholarly attention (Indriastuti et al., 2022;
    Wijaya et al., 2021). In the context of modern consumers navigating
    time constraints and demanding schedules, time flexibility emerges
    as a critical determinant in digital engagement particularly in
    e-commerce. As such, understanding how flexible time contributes to
    shopping needs and customer visits not only enriches theoretical
    discourse but also offers strategic implications for digital
    marketing management.</p>
    <p>The primary contribution of this study lies in its theoretical
    enrichment of digital consumer behavior by introducing time as a
    variable with both direct and indirect effects on purchase intention
    and visit intensity. The study also provides a unique empirical
    context by examining a sample of active Shopee users in Indonesia, a
    market characterized by dynamic demographics and rapid digital
    adoption in Southeast Asia. Furthermore, the findings are expected
    to</p>
    <p>offer empirical novelty by integrating flexible time as a
    determinant of both shopping behavior and consumer visitation
    patterns, a relationship that has not been simultaneously addressed
    in prior literature.</p>
    <p>From a practical standpoint, insights into the effects of
    flexible time can assist digital marketing managers in designing
    more temporally efficient strategies such as time-based promotions,
    optimized push notification schedules, and behavioral mapping
    aligned with peak access periods. Through a time-sensitive approach,
    e-commerce companies can enhance user engagement and foster greater
    customer loyalty. Accordingly, this study explicitly aims to examine
    the interrelationships among flexible time, shopping need, and
    customer visit, while contributing significantly to the advancement
    of both theoretical understanding and managerial practices in
    platform based digital economies.</p>
  </disp-quote>
</sec>





<sec>
  <title>LITERATURE REVIEW</title>
  <sec id="model-stimulus-organism-response-sor">
    <title>Model Stimulus-Organism-Response (SOR)</title>
    <disp-quote>
      <p>The Stimulus-Organism-Response (SOR) model is a
      well-established framework in behavioral psychology that explains
      how external stimuli influence an individual's internal state
      (organism), ultimately leading to behavioral responses (Goi et
      al., 2014; McQuail, 2010). In the context of digital environments
      and e-commerce, the SOR model has been widely applied to examine
      how external elements such as website design, time flexibility, or
      promotional cues trigger emotional and cognitive reactions in
      consumers, which then drive actions such as site visits or
      purchase decisions (Mehrabian &amp; Russell, 1974).</p>
      <p>In this study, the stimulus is conceptualized as flexible time,
      the organism is represented by shopping need, and the resulting
      response is the customer visit. Research by Necula (2023) and
      Basalla et al. (2021) indicates that shopping time flexibility can
      influence consumers’ sense of convenience and control over their
      digital consumption behavior. Accordingly, flexible time serves as
      an internal stimulus that fosters shopping motivation and drives
      the frequency of consumer visits to the Shopee platform.</p>
      <p>H1: Flexible time has a significant effect on customer visit
      behavior on Shopee.</p>
    </disp-quote>
  </sec>
  <sec id="work-life-balance-theory-and-digital-consumer-behavior">
    <title>Work-Life Balance Theory and Digital Consumer
    Behavior</title>
    <disp-quote>
      <p>Work-Life Balance Theory posits that the balance between
      personal life and professional responsibilities is largely
      determined by the degree of control individuals exert over their
      time. Wöhrmann <italic>et al.,</italic> (2021) argue that when
      individuals can manage when and how they perform tasks, their
      overall satisfaction and productivity— including in consumption
      activities—tend to increase.</p>
      <p>From the perspective of Digital Consumer Behavior, time
      autonomy is a key enabler for consumers to engage with digital
      platforms without spatial or temporal restrictions (D.-J. Lee
      &amp; Sirgy, 2019). This is particularly relevant in e- commerce
      environments where consumers often access platforms such as</p>
      <p>Shopee during evenings or weekends, leveraging their
      discretionary time to explore product offerings.</p>
      <p>Previous studies have shown that perceived time savings and
      ease of access significantly enhance consumer preference for
      online shopping platforms (Wei et al., 2018). Furthermore, the
      combination of time flexibility and competitive pricing has been
      found to increase the likelihood of online purchase behaviors
      (Moussaoui et al., 2023).</p>
      <p>H2: Flexible time has a significant effect on consumers'
      shopping need on Shopee.</p>
    </disp-quote>
  </sec>
  <sec id="theory-of-planned-behavior-tpb-and-online-shopping-motivation">
    <title>Theory of Planned Behavior (TPB) and Online Shopping
    Motivation</title>
    <disp-quote>
      <p>The Theory of Planned Behavior (TPB) explains that behavioral
      intentions are influenced by three primary constructs: attitude
      toward the behavior, subjective norms, and perceived behavioral
      control (Ajzen, 1991). In the context of online shopping, shopping
      need can be interpreted as a form of behavioral intention that is
      shaped by emotional impulses, social influences (e.g., trends,
      reviews), and the degree of control consumers perceive in
      navigating online transactions.</p>
      <p>Shopping need is also informed by the Online Shopping
      Motivation framework, which emphasizes both functional and hedonic
      motivations driving consumer purchases. These motivations may
      manifest as urgency due to limited-time promotions, a desire for
      emotional satisfaction, or a preference for convenient and
      time-efficient shopping experiences.</p>
      <p>Emotional gratification and promotional stimuli have been shown
      to influence purchase decisions significantly (Ajizah &amp;
      Nugroho, 2023; Maharani &amp; Darma, 2018). Therefore, shopping
      need is not merely a response to functional requirements but is
      also heavily influenced by emotional drivers that can be amplified
      by flexible time and promotional strategies.</p>
      <p>H3: Shopping need has a significant effect on customer visit
      behavior on Shopee.</p>
    </disp-quote>
  </sec>
  <sec id="web-usage-behavior-theory-and-online-purchase-pathways">
    <title>Web Usage Behavior Theory and Online Purchase
    Pathways</title>
    <disp-quote>
      <p>The Web Usage Behavior Theory focuses on how the frequency,
      duration, and interaction patterns of users with digital platforms
      reflect their behavioral intentions and loyalty. In the e-commerce
      setting, a high volume of customer visits often indicates
      sustained consumer interest in a platform’s products or
      services.</p>
      <p>Indicators such as visit frequency and timing are crucial in
      evaluating consumer engagement with digital platforms (Bera &amp;
      Das, 2012; Moe &amp; Fader, 2004). The duration of visits is also
      closely linked to the depth of product exploration and the
      likelihood of purchase conversion. Furthermore, user comfort and
      positive interaction experiences have been found to contribute to
      increased repeat visits, as demonstrated in the case of Shopee
      (Suhendry, 2023).</p>
      <p>In the present study, the integration of these theoretical
      perspectives underscores the role of shopping need as the
      mediating construct (organism) within the SOR model. It mediates
      the influence of flexible time (stimulus) on</p>
      <p>customer visit (response), thus suggesting an indirect but
      significant pathway that warrants empirical examination.</p>
      <p>H4: Flexible time has a significant indirect effect on customer
      visit via shopping need on Shopee.</p>
    </disp-quote>
    <graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_472f436e27c44a58bf357983b2ce18bb/media/image3.jpeg" />
    <disp-quote>
      <p>Gambar 1. Conceptual Framework</p>
    </disp-quote>
  </sec>
</sec>







<sec>
  <title>METODOLOGI</title>
  <sec id="research-design">
    <title>Research Design</title>
    <disp-quote>
      <p>This study employed a quantitative approach with a combination
      of descriptive and causal research designs (Neuman, 2017). The
      descriptive design was used to portray patterns of consumer
      behavior in online shopping, particularly in relation to time
      flexibility. The causal design aimed to identify cause-and-effect
      relationships between flexible time, shopping need, and customer
      visit on the Shopee platform. The analysis was conducted using
      Partial Least Squares–Structural Equation Modeling (PLS-SEM) to
      assess both the measurement and structural models (Hair et al.,
      2017).</p>
    </disp-quote>
  </sec>
  <sec id="population-and-sample">
    <title>Population and Sample</title>
    <list list-type="order">
      <list-item>
        <p>Research Population: The population of this study consisted
        of active Shopee users in Indonesia, defined as individuals who
        regularly use the Shopee application or website for online
        shopping.</p>
      </list-item>
      <list-item>
        <p>Research Sample: The sample was selected using a purposive
        sampling technique, focusing on users who meet the following
        inclusion criteria:</p>
        <list list-type="bullet">
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Minimum age of 18 years.</p>
              </disp-quote>
            </p>
          </list-item>
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Actively using a Shopee account for at least six
                months.</p>
              </disp-quote>
            </p>
          </list-item>
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Engaged in at least two online purchases per
                month.</p>
              </disp-quote>
            </p>
          </list-item>
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Belonging to categories likely to have flexible time,
                such as university students, freelancers, housewives,
                and employees working under hybrid or remote
                systems.</p>
              </disp-quote>
            </p>
          </list-item>
        </list>
      </list-item>
      <list-item>
        <p>Sample Size: Based on the recommendation by Hair <italic>et
        al.</italic> (2017), the minimum required sample size is
        calculated by multiplying the total number of indicators by 10.
        With 12 indicators in the model, the minimum sample size is 120
        respondents.</p>
      </list-item>
    </list>
  </sec>
  <sec id="data-collection-techniques">
    <title>Data Collection Techniques</title>
    <disp-quote>
      <p>Primary Data collected via an online questionnaire distributed
      through platforms such as Google Forms. The questionnaire items
      measured the three latent constructs Flexible Time, Shopping Need,
      and Customer Visit. Each item was assessed using a 5-point Likert
      scale ranging from “Strongly Disagree (1)” to “Strongly Agree
      (5).” Secondary Data collected from Google Analytics, Semrush, or
      similar platforms. These data provide behavioral insights, such as
      visit frequency, duration, time of visit, and conversion rates,
      which serve to support and validate findings from the primary data
      (Creswell, 2017; Neuman, 2017).</p>
    </disp-quote>
  </sec>
  <sec id="data-analysis-techniques">
    <title>Data Analysis Techniques</title>
    <disp-quote>
      <p>This study utilized Partial Least Squares–Structural Equation
      Modeling (PLS-SEM), a robust multivariate analysis method suited
      for complex models involving multiple latent constructs. The
      analysis was conducted in two primary stages (Abdillah &amp;
      Jogiyanto, 2015; Hair et al., 2017).</p>
    </disp-quote>
    <list list-type="order">
      <list-item>
        <p>Measurement Model Evaluation (Outer Model)</p>
      </list-item>
    </list>
    <disp-quote>
      <p>This stage assesses the reliability and validity of the
      indicators measuring each latent variable.</p>
    </disp-quote>
    <list list-type="bullet">
      <list-item>
        <p>Convergent Validity</p>
        <list list-type="bullet">
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Indicator loadings should be &gt; 0.70</p>
              </disp-quote>
            </p>
          </list-item>
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Average Variance Extracted (AVE) must be &gt;
                0.50</p>
              </disp-quote>
            </p>
          </list-item>
        </list>
      </list-item>
      <list-item>
        <p>Discriminant Validity</p>
        <list list-type="bullet">
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Cross-loading values should exceed 0.60 for their
                respective constructs</p>
              </disp-quote>
            </p>
          </list-item>
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Fornell-Larcker Criterion is satisfied when the
                square root of AVE exceeds the correlation with other
                constructs</p>
              </disp-quote>
            </p>
          </list-item>
        </list>
      </list-item>
      <list-item>
        <p>Construct Reliability:</p>
        <list list-type="bullet">
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Composite Reliability (CR) and Cronbach’s Alpha
                values must</p>
              </disp-quote>
            </p>
          </list-item>
        </list>
      </list-item>
    </list>
    <disp-quote>
      <p>both exceed 0.70</p>
    </disp-quote>
    <list list-type="bullet">
      <list-item>
        <p>Model Fit</p>
        <list list-type="bullet">
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Q-Square Predictive Relevance (Q²): Q² &gt; 0
                demonstrates predictive relevance of the model for
                endogenous constructs</p>
              </disp-quote>
            </p>
          </list-item>
          <list-item>
            <p specific-use="wrapper">
              <disp-quote>
                <p>Goodness of Fit (GoF): Calculated as the geometric
                mean of the</p>
              </disp-quote>
            </p>
          </list-item>
        </list>
      </list-item>
    </list>
    <disp-quote>
      <p>average AVE and R². GoF ≥ 0.36 = strong; 0.25–0.36 =
      moderate;</p>
      <p>&lt; 0.25 = weak.</p>
    </disp-quote>
    <list list-type="order">
      <list-item>
        <label>2.</label>
        <p>Structural Model Evaluation (Inner Model)</p>
      </list-item>
    </list>
    <disp-quote>
      <p>This stage tests the hypothesized relationships between latent
      constructs and their significance.</p>
    </disp-quote>
    <list list-type="bullet">
      <list-item>
        <p specific-use="wrapper">
          <disp-quote>
            <p>Coefficient of Determination (R²): Indicates the
            explanatory power of independent variables over dependent
            variables. R² ≥ 0.67 = substantial; 0.33–0.67 = moderate;
            &lt; 0.33 = weak</p>
          </disp-quote>
        </p>
      </list-item>
      <list-item>
        <p specific-use="wrapper">
          <disp-quote>
            <p>Path Coefficients: Assessed to determine the strength and
            direction of relationships among constructs. Significance
            tested using bootstrapping, where t-statistics ≥ 1.96 (for α
            = 0.05) indicates a significant relationship</p>
          </disp-quote>
        </p>
      </list-item>
      <list-item>
        <p>Effect Size (f²): Measures the impact of each exogenous
        construct. f² ≥</p>
      </list-item>
    </list>
    <disp-quote>
      <p>0.35 = large; 0.15–0.34 = medium; 0.02–0.14 = small</p>
      <p>This integrated approach allows for comprehensive evaluation of
      both the psychometric properties of the constructs and the
      structural relationships hypothesized in the model. The use of
      SEM-PLS is particularly suitable given the sample size and the
      model's predictive nature, providing both theoretical insights and
      managerial implications for e-commerce strategies on platforms
      like Shopee.</p>
    </disp-quote>
  </sec>
</sec>





<sec>
  <title>RESEARCH RESULT</title>
  <sec id="latent-variable-model-of-the-study">
    <title>Latent Variable Model of the Study</title>
    <disp-quote>
      <p><inline-graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_472f436e27c44a58bf357983b2ce18bb/media/image4.jpeg" />The
      latent variable model employed in this study is presented as
      follows:</p>
      <p>Figure 1. Latent Variable Model of the Study</p>
    </disp-quote>
  </sec>
  <sec id="outer-model">
    <title>Outer Model</title>
    <disp-quote>
      <p><italic>Convergent Validity</italic></p>
      <p>In the convergent validity test, outer loadings and Average
      Variance Extracted (AVE) were utilized. The results are presented
      as follows.</p>
    </disp-quote>
    <disp-quote>
      <p><bold>Table 1. Outer Loading</bold></p>
    </disp-quote>
    <table-wrap>
        <label>Table 1. Outer Loading</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top"></th>
                    <th align="center" valign="top">Customer Visit</th>
                    <th align="center" valign="top">Flexible Time</th>
                    <th align="center" valign="top">Shopping Need</th>
                    <th align="center" valign="top">Ket.</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">CV1</td>
                    <td align="center" valign="top">0.780</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">CV2</td>
                    <td align="center" valign="top">0.823</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">CV3</td>
                    <td align="center" valign="top">0.809</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">CV4</td>
                    <td align="center" valign="top">0.729</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">FT1</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.796</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">FT2</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.890</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">FT3</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.781</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">FT4</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.809</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">SN1</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.809</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">SN2</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.840</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">SN3</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.790</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">SN4</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">0.681</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p><bold>Table 2. Average Variance Extracted</bold></p>
    </disp-quote>
    <table-wrap>
        <label>Table 2. Average Variance Extracted</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Variabel</th>
                    <th align="center" valign="top">Average Variance Extracted</th>
                    <th align="center" valign="top">Ket.</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Flexible Time</td>
                    <td align="center" valign="top">0.618</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Shopping Need</td>
                    <td align="center" valign="top">0.672</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Customer Visit</td>
                    <td align="center" valign="top">0.612</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p>Based on the outer loading test results presented in Table 1,
      the majority of indicators meet the minimum threshold of ≥ 0.70,
      indicating strong validity in measuring their respective latent
      constructs. Furthermore, the results of the Average Variance
      Extracted (AVE) test in Table 2 show that all constructs— Customer
      Visit, Flexible Time, and Shopping Need—satisfy the criterion for
      convergent validity, with AVE values equal to or greater than
      0.50.</p>
    </disp-quote>
    <disp-quote>  
      <p><italic>Discriminant Validity</italic></p>
    </disp-quote>
    <disp-quote>
      <p>Discriminant validity in this study was assessed using
      cross-loading analysis and the Fornell-Larcker criterion.</p>
    </disp-quote>
    <disp-quote>
      <p><bold>Table 3. Fornell-Larcker Stage 1</bold></p>
    </disp-quote>
    <table-wrap>
        <label>Table 3. Fornell-Larcker Stage 1</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Variable</th>
                    <th align="center" valign="top">Customer Visit</th>
                    <th align="center" valign="top">Flexible Time</th>
                    <th align="center" valign="top">Shopping Need</th>
                    <th align="center" valign="top">Ket.</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Customer Visit</td>
                    <td align="center" valign="top">0.786</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Unvalid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Flexible Time</td>
                    <td align="center" valign="top">0.839</td>
                    <td align="center" valign="top">0.820</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Unvalid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Shopping Need</td>
                    <td align="center" valign="top">0.857</td>
                    <td align="center" valign="top">0.839</td>
                    <td align="center" valign="top">0.782</td>
                    <td align="center" valign="top">Unvalid</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p>As shown in Table 3, the initial results based on the
      Fornell-Larcker criterion indicate that the model did not fully
      meet the ideal criteria for discriminant validity. Therefore, a
      second-stage analysis was conducted by modifying the model and
      removing overlapping indicators namely FT3, SN1, and CV2.</p>
    </disp-quote>
    <disp-quote>
      <p><bold>Table 4. Fornell-Larcker Stage 2</bold></p>
    </disp-quote>
    <table-wrap >
        <label>Table 4. Fornell-Larcker Stage 2</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Variabel</th>
                    <th align="center" valign="top">Customer Visit</th>
                    <th align="center" valign="top">Flexible Time</th>
                    <th align="center" valign="top">Shopping Need</th>
                    <th align="center" valign="top">Ket.</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Customer Visit</td>
                    <td align="center" valign="top">0.799</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Flexible Time</td>
                    <td align="center" valign="top">0.770</td>
                    <td align="center" valign="top">0.861</td>
                    <td align="center" valign="top"></td>
                    <td align="center" valign="top">Valid</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Shopping Need</td>
                    <td align="center" valign="top">0.757</td>
                    <td align="center" valign="top">0.787</td>
                    <td align="center" valign="top">0.799</td>
                    <td align="center" valign="top">Valid</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p>The results of the second-stage discriminant validity test are
      as follows. As shown in Table 4, the Fornell-Larcker Criterion in
      Stage 2 confirms that the discriminant validity of the model has
      been achieved.</p>
    </disp-quote>
    <disp-quote>
      <p><italic>Construct Validity</italic></p>
    </disp-quote>
    <disp-quote>
      <p>Construct validity in this study was assessed using Composite
      Reliability and Cronbach’s Alpha.</p>
    </disp-quote>
    <disp-quote>
      <p>Table 5. Composite Reliability</p>
    </disp-quote>
    <table-wrap>
        <label>Table 5. Composite Reliability</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Variable</th>
                    <th align="center" valign="top">Composite Reliability</th>
                    <th align="center" valign="top">Keterangan</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Customer Visit</td>
                    <td align="center" valign="top">0.841</td>
                    <td align="center" valign="top">Reliabel</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Flexible Time</td>
                    <td align="center" valign="top">0.895</td>
                    <td align="center" valign="top">Reliabel</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Shopping Need</td>
                    <td align="center" valign="top">0.840</td>
                    <td align="center" valign="top">Reliabel</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p>Table 6. Croncbach Alpha</p>
    </disp-quote>
    <table-wrap>
        <label>Table 6. Croncbach Alpha</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Variable</th>
                    <th align="center" valign="top">Croncbach's Alpha</th>
                    <th align="center" valign="top">Keterangan</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Customer Visit</td>
                    <td align="center" valign="top">0.716</td>
                    <td align="center" valign="top">Reliabel</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Flexible Time</td>
                    <td align="center" valign="top">0.825</td>
                    <td align="center" valign="top">Reliabel</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Shopping Need</td>
                    <td align="center" valign="top">0.712</td>
                    <td align="center" valign="top">Reliabel</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p>The results of the construct reliability test, based on
      Composite Reliability values, indicate that all variables in the
      model demonstrate excellent internal consistency, exceeding the
      recommended minimum threshold of 0.70. Similarly, the reliability
      test results based on Cronbach’s Alpha show that all variables
      exhibit adequate to high levels of internal reliability, with
      values exceeding the minimum recommended threshold of 0.60. These
      findings suggest that the indicators within each construct are
      consistent and stable in measuring the same underlying
      concept.</p>
    </disp-quote>
    <disp-quote>
      <p><italic>Model Fit</italic></p>
    </disp-quote>
    <disp-quote>
      <p>The model fit test in Partial Least Squares–Structural Equation
      Modeling (PLS-SEM) is a procedure used to evaluate the extent to
      which both the measurement model and the structural model are
      consistent with the empirical data. The results of the model fit
      test are presented in Table 7.</p>
    </disp-quote>
    <disp-quote>
      <p>Table 7. Model Fit</p>
    </disp-quote>
    <table-wrap>
      <label>Table 7. Model Fit</label>
      <table>
        <thead>
          <tr>
            <th align="left" valign="top">Parameter</th>
            <th align="left" valign="top">Rule of Thumb</th>
            <th align="center" valign="top">Nilai Parameter</th>
            <th align="center" valign="top">Keterangan</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td align="left" valign="top">Goodness of Fit/GoF</td>
            <td align="left" valign="top">0.1 (GOF small), 0.25 (GOF moderate), 0.36 (GOF strong)</td>
            <td align="center" valign="top">0.428</td>
            <td align="center" valign="top">Fit (Kuat)</td>
          </tr>
          <tr>
            <td align="left" valign="top" rowspan="2">Q<superscript>2</superscript> Predictive Relevance</td>
            <td align="left" valign="top">
              Q<superscript>2</superscript> &gt; 0: Having predictive relevance<br/>
              Q<superscript>2</superscript> &lt; 0: Has limited predictive relevance.
            </td>
            <td align="center" valign="top">Q<superscript>2</superscript> Shopping Need 0.607 &gt; 0</td>
            <td align="center" valign="top"></td>
          </tr>
          <tr>
            <td align="left" valign="top">0.02 (Low), 0.15 (Moderate), 0.35 (Strong)</td>
            <td align="center" valign="top">Q<superscript>2</superscript> Customer Visit 0.572 &gt; 0</td>
            <td align="center" valign="top">Fit</td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
    <disp-quote>
      <p>Based on the data in Table 7, the PLS-SEM model employed in
      this study demonstrates a very good fit, is empirically valid, and
      is therefore suitable for interpreting the relationships among
      constructs in the subsequent structural analysis phase.</p>
    </disp-quote>
  </sec>
  <sec id="inner-model">
    <title>Inner Model</title>
    <disp-quote>
      <p><italic>R Square</italic></p>
      <p>The results of the R Square (R²) analysis in this study are
      presented in Table 8.</p>
    </disp-quote>
    <disp-quote>
      <p>Table 8. R Square</p>
    </disp-quote>
    <table-wrap>
        <label>Table 8. R Square</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Variable Dependen</th>
                    <th align="center" valign="top">R-square</th>
                    <th align="center" valign="top">R-square adjusted</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Customer Visit</td>
                    <td align="center" valign="top">0.653</td>
                    <td align="center" valign="top">0.647</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Shopping Need</td>
                    <td align="center" valign="top">0.620</td>
                    <td align="center" valign="top">0.616</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p>As shown in the table 8, the structural model exhibits strong
      predictive power for the endogenous constructs, particularly the
      Customer Visit variable, which has an R² value of 0.653. This
      indicates that 65.3% of the variance in the Customer Visit
      construct can be explained by the exogenous constructs within the
      model, demonstrating a high level of predictive contribution. The
      SEM-PLS model output for the R Square test is illustrated in
      Figure 2.</p>
      <graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_472f436e27c44a58bf357983b2ce18bb/media/image5.jpeg" />
      <p>Figure 2. Output Model SEM-PLS <italic>R Square</italic></p>
    </disp-quote>
    <disp-quote>
      <p><italic>Path Coefficient</italic></p>
    </disp-quote>
    <disp-quote>
      <p>The results of the bootstrapping analysis for testing both
      direct and indirect effects within the research model are
      presented below. The direct effect test results, conducted using
      SmartPLS 4, are displayed in Table 9.</p>
    </disp-quote>
    <disp-quote>
      <p>Table 9. Direct Effect Bootstrapping</p>
    </disp-quote>
    <table-wrap>
        <label>Table 9. Direct Effect Bootstrapping</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Path Coefficient</th>
                    <th align="center" valign="top">Original sample (O)</th>
                    <th align="center" valign="top">Sample mean (M)</th>
                    <th align="center" valign="top">Standard deviation (STDEV)</th>
                    <th align="center" valign="top">T statistics</th>
                    <th align="center" valign="top">P values</th>
                    <th align="center" valign="top">Ket.</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Flexible Time -> Customer Visit</td>
                    <td align="center" valign="top">0.457</td>
                    <td align="center" valign="top">0.468</td>
                    <td align="center" valign="top">0.092</td>
                    <td align="center" valign="top">4.981</td>
                    <td align="center" valign="top">0.000</td>
                    <td align="center" valign="top">Terbukti</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Flexible Time -> Shopping Need</td>
                    <td align="center" valign="top">0.787</td>
                    <td align="center" valign="top">0.789</td>
                    <td align="center" valign="top">0.051</td>
                    <td align="center" valign="top">15.351</td>
                    <td align="center" valign="top">0.000</td>
                    <td align="center" valign="top">Terbukti</td>
                </tr>
                <tr>
                    <td align="left" valign="top">Shopping Need -> Customer Visit</td>
                    <td align="center" valign="top">0.398</td>
                    <td align="center" valign="top">0.390</td>
                    <td align="center" valign="top">0.099</td>
                    <td align="center" valign="top">4.015</td>
                    <td align="center" valign="top">0.000</td>
                    <td align="center" valign="top">Terbukti</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
    <disp-quote>
      <p>The analysis shows that the direct effect of Flexible Time on
      Customer Visit yields a path coefficient of 0.457, indicating a
      positive directional relationship. The associated t-statistic of
      4.981 far exceeds the critical value of</p>
      <p>1.96 (at α = 0.05 for a two-tailed test), with a p-value of
      0.000, which is well below the 0.05 significance threshold. These
      results confirm that the effect is statistically significant.
      Accordingly, the hypothesis stating that Flexible Time has a
      significant effect on Customer Visit is supported.</p>
      <p>The significance test further reveals that the path coefficient
      from Flexible Time to Shopping Need is 0.787, with a t-statistic
      of 15.351 and a p- value of 0.000. This exceptionally high t-value
      and extremely low p-value provide strong evidence of a
      statistically significant and robust relationship between the two
      variables. Hence, it can be concluded that Flexible Time
      significantly and positively influences consumers' Shopping Need,
      thereby supporting the second hypothesis.</p>
      <p>Moreover, the test results show that the path coefficient
      between Shopping Need and Customer Visit is 0.398, with a
      t-statistic of 4.015 and a p- value of 0.000. Given that the
      t-statistic exceeds the critical value of 1.96 and the p-value is
      well below 0.05, this effect is statistically significant. These
      findings indicate a meaningful contribution of Shopping Need to
      the frequency of consumer visits. Therefore, the hypothesis
      stating that Shopping Need has a significant effect on Customer
      Visit is accepted.</p>
      <graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_472f436e27c44a58bf357983b2ce18bb/media/image6.jpeg" />
      <p>Figure 3. Output Model SEM-PLS <italic>Direct
      Effect</italic></p>
      <p>The results of the indirect effect bootstrapping analysis,
      conducted using SmartPLS 4, are presented in Table 10.</p>
    </disp-quote>
    <disp-quote>
      <p>Table 10. Indirect Effect Bootstrapping</p>
    </disp-quote>
    <table-wrap>
        <label>Table 10. Indirect Effect Bootstrapping</label>
        <table>
            <thead>
                <tr>
                    <th align="left" valign="top">Path Coefficient</th>
                    <th align="center" valign="top">Original sample (O)</th>
                    <th align="center" valign="top">Sample mean (M)</th>
                    <th align="center" valign="top">Standard deviation (STDEV)</th>
                    <th align="center" valign="top">T statistics</th>
                    <th align="center" valign="top">P values</th>
                    <th align="center" valign="top">Ket.</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td align="left" valign="top">Flexible Time -> Shopping Need -> Customer Visit</td>
                    <td align="center" valign="top">0.313</td>
                    <td align="center" valign="top">0.306</td>
                    <td align="center" valign="top">0.078</td>
                    <td align="center" valign="top">4.034</td>
                    <td align="center" valign="top">0.000</td>
                    <td align="center" valign="top">Terbukti</td>
                </tr>
            </tbody>
        </table>
    </table-wrap>
  <disp-quote>
    <p>As shown in the table, the indirect effect test using the
    bootstrapping procedure indicates a statistically significant
    indirect relationship between Flexible Time and Customer Visit,
    mediated by Shopping Need. The path coefficient for this indirect
    effect is 0.313, with a t-statistic of 4.034 and a p-value of 0.000.
    The t-value, which far exceeds the critical threshold of 1.96, and
    the p- value, which falls well below the 0.05 level of significance,
    confirm that this mediation pathway is statistically significant.
    These findings suggest that Shopping Need serves as an effective
    mediator, bridging the relationship between flexible time
    availability and the frequency of consumer visits. Thus, the
    hypothesis stating that Shopping Need mediates the effect of
    Flexible Time on Customer Visit is empirically supported.</p>
    <p><italic>Effect Size</italic></p>
    <p>The effect size test (f²) is employed to determine the magnitude
    of each independent variable’s contribution to the dependent
    variable within the structural model, by examining the change in R²
    when the variable is included or excluded. The results of the effect
    size analysis are presented in Table 11.</p>
  </disp-quote>
  <disp-quote>
    <p>Table 11. Effect Size</p>
  </disp-quote>
  <table-wrap>
      <label>Table 11. Effect Size</label>
      <table>
          <thead>
              <tr>
                  <th align="left" valign="top">Variabel</th>
                  <th align="center" valign="top">Customer Visit</th>
                  <th align="center" valign="top">Flexible Time</th>
                  <th align="center" valign="top">Shopping Need</th>
              </tr>
          </thead>
          <tbody>
              <tr>
                  <td align="left" valign="top">Customer Visit</td>
                  <td align="center" valign="top"></td>
                  <td align="center" valign="top"></td>
                  <td align="center" valign="top"></td>
              </tr>
              <tr>
                  <td align="left" valign="top">Flexible Time</td>
                  <td align="center" valign="top">0.229</td>
                  <td align="center" valign="top"></td>
                  <td align="center" valign="top">1.629</td>
              </tr>
              <tr>
                  <td align="left" valign="top">Shopping Need</td>
                  <td align="center" valign="top">0.174</td>
                  <td align="center" valign="top"></td>
                  <td align="center" valign="top"></td>
              </tr>
          </tbody>
      </table>
  </table-wrap>
  <disp-quote>
    <p>As shown in Table 11, the f² values in the structural model
    indicate that all constructs exhibit substantial effects on their
    respective dependent variables. The f² value of 1.629 for the
    relationship between Flexible Time and Shopping Need reflects a
    large effect size, well above the threshold of 0.35, suggesting that
    Flexible Time contributes significantly to explaining the variance
    in Shopping Need. Similarly, the effect of Flexible Time on Customer
    Visit yields an f² value of 0.229, which also falls within the large
    effect size category. Moreover, the f² value of 0.174 for the
    relationship between Shopping Need and Customer Visit further
    supports previous findings, confirming that Shopping Need plays a
    key role in explaining the variance in Customer Visit. Taken
    together, these results demonstrate that all three major paths in
    the model exhibit large effect sizes, indicating that the exogenous
    constructs in this study have a strong and substantial influence on
    the corresponding endogenous constructs.</p>
  </disp-quote>
</sec>
</sec>






<sec>
  <title>DISCUSSION</title>
  <disp-quote>
    <p>This study highlights the pivotal role of flexible time in
    shaping consumer behavior within the e-commerce ecosystem,
    particularly in the context of the Shopee platform in Indonesia.
    Flexible time, representing consumers’ autonomy in determining their
    most convenient periods for accessing digital platforms, has been
    empirically shown to significantly influence the intensity of user
    visits. This indicates that digital-era consumers place high value
    on the ease and convenience of managing their time for online
    shopping. The more flexibility they have in organizing their
    schedules, the higher their tendency to explore products, engage
    with platform features, and make purchases. These findings
    underscore that time is no longer a constraint in online
    transactions; instead, it functions as a strategic resource that
    enhances consumer loyalty and engagement.</p>
    <p>These results align with the Stimulus-Organism-Response (SOR)
    model, in which flexible time acts as a stimulus that fosters
    positive psychological conditions in users, such as comfort,
    personal control, and efficiency (I. Lee, 2016). These conditions
    shape users’ perceptions and emotional responses (organism), which
    are subsequently expressed through actual behavior in the form of
    customer visits. In this context, the freedom to choose shopping
    times serves as a primary trigger that mitigates common
    psychological barriers in the purchasing process, such as time
    pressure, work fatigue, or limited physical access to stores.
    Shopee, as a digital platform, successfully provides a flexible</p>
    <p>shopping environment accessible anytime and anywhere, which
    ultimately cultivates consumer preferences for frequent and
    intensive visits.</p>
    <p>Beyond providing technical convenience, flexible time fosters a
    heightened sense of control over the shopping experience. This sense
    of control empowers consumers to dictate their own consumption
    rhythms without external pressure (Solomon, 2015). Such autonomy
    becomes a significant intrinsic motivator, transforming shopping
    from a functional necessity into a pleasurable experience. When
    consumers feel empowered to decide when and how they shop, they are
    not only more active in visiting the site but are also more likely
    to interact with features such as reviews, wishlists, and
    personalized promotions.</p>
    <p>In this sense, Shopee functions not merely as a transactional
    platform, but as a digital space that facilitates time-based
    consumption behavior. This flexibility enables deeper engagement,
    allowing consumers to browse products at their own pace, which
    increases both the duration of visits and the depth of product
    exploration. This experience fosters a sense of attachment and
    builds long-term platform loyalty, as users perceive that Shopee
    understands and accommodates their personal time preferences.</p>
    <p>Another key finding reveals that flexible time not only affects
    visit frequency but also triggers the formation of shopping need.
    This indicates that when consumers have ample time to explore the
    platform, their internal motivation to make purchases increases.
    From the SOR perspective, flexible time creates a stimulus that
    cultivates emotional responses such as desire, excitement about
    discounts, or fear of missing out (FOMO) (Huo et al., 2023). These
    emotional reactions intensify shopping need, as consumers feel they
    have time to contemplate purchasing decisions, evaluate options, and
    take action without pressure.</p>
    <p>Shopping needs that develop under flexible time conditions are
    often more intense and personalized. Leisure time allows consumers
    to internalize promotional information and align it with their
    individual preferences. In such circumstances, purchase decisions
    are not solely driven by impulse but are supported by rational
    evaluation conducted in a relaxed and controlled manner. Consumers
    feel that they govern the consumption process rather than simply
    reacting to marketing strategies. Consequently, flexible time not
    only stimulates purchase urgency but also enhances the quality of
    decision-making in online shopping.</p>
    <p>The emotional comfort derived from flexible time significantly
    strengthens the relationship between consumers and the Shopee
    platform. When consumers shop in conditions of their own choosing,
    such as at night, during rest, or in a calm mood, shopping is no
    longer perceived as a chore, but as an enjoyable activity worth
    repeating. In such scenarios, shopping need becomes more than a mere
    reaction to market stimuli; it becomes an expression of digital
    lifestyle, where consumption is designed according to personal tempo
    (Turchyn, 2021).</p>
    <p>Furthermore, the shopping need formed under flexible time
    conditions directly drives customer visits. When consumers
    experience an internal urge to buy, they are more likely to access
    Shopee's app or website frequently. This relationship indicates that
    shopping need is a key driver of digital visit behavior.</p>
    <p>It applies not only to functional shopping but also to
    exploratory behaviors, product discovery, or the desire to stay
    updated with the latest deals. Visits to the platform thus serve as
    tangible expressions of prior intention and motivation.</p>
    <p>As shopping needs intensify, visits become more intentional and
    frequent (Moe &amp; Fader, 2004). Consumers tend to allocate
    specific time to browse Shopee, read reviews, compare prices, and
    even develop purchasing strategies based on available promotions.
    These activities strengthen consumer engagement and establish visits
    as routines rooted in actual or perceived needs. This demonstrates
    how shopping need mediates the relationship between flexible time
    and increased visit activity.</p>
    <p>The mediating role of shopping need between flexible time and
    customer visit underscores the dynamic nature of digital consumer
    behavior, which is shaped through the interplay of external and
    internal factors. The flexibility in consumers’ schedules creates a
    setting in which platform-generated stimulation such as promotions
    and discounts are internalized as needs rather than just
    information. This internalization leads to psychological impulses
    that drive concrete actions, namely, visits to Shopee. Therefore,
    shopping need emerges not only as a mediating variable but also as a
    critical component in understanding digital consumer decision-making
    pathways.</p>
    <p>Positive user experiences associated with flexible time present
    opportunities for platforms to design more personalized, time-based
    marketing strategies. Such strategies may include sending push
    notifications during evenings or weekends, scheduling promotions
    based on user activity patterns, or customizing app interfaces to
    support various time conditions. When consumers feel their time is
    respected and that the platform is present when they are ready to
    shop, brand-consumer relationships become stronger and more
    enduring.</p>
    <p>Conceptually, this study reinforces the SOR framework within the
    context of digital consumer behavior. Flexible time, as a stimulus,
    influences the organism—through emotional needs and consumption
    preferences, which in turn results in a response in the form of
    active platform visits. This process unfolds under conditions where
    users feel comfortable, in control, and satisfied. As such,
    time-based strategies emerge as a central approach in shaping
    sustainable digital behavior.</p>
    <p>The implications of these findings are relevant not only to
    researchers but also to e-commerce practitioners and digital
    marketers. In an increasingly competitive landscape, deep
    understanding of how time influences consumer psychology and
    behavior is a strategic asset. Platforms like Shopee must
    continuously refine their interaction strategies to align with
    users’ needs for time flexibility. This includes integrating
    time-sensitive features, personalizing user experiences based on
    temporal preferences, and adapting promotional campaigns to match
    modern consumers' life rhythms.</p>
    <p>Recognizing that consumers are seeking not just products but
    experiences that resonate with their lifestyles, flexible time must
    become a core element in system design, user interaction, and
    digital communication strategies. This study provides both empirical
    and theoretical evidence that time is not merely an environmental
    variable, but a strategic lever for enhancing engagement,
    shaping</p>
    <p>needs, and building customer loyalty. Shopee and other e-commerce
    platforms can leverage these findings to strengthen their
    positioning in a fast-evolving digital landscape centered on
    comfort, autonomy, and personalized shopping experiences.</p>
  </disp-quote>
</sec>






<sec>
  <title>CONCLUSION</title>
  <disp-quote>
    <p>This study concludes that flexible time plays a vital role in
    shaping digital consumer behavior, particularly on the Shopee
    e-commerce platform in Indonesia. Drawing on the
    Stimulus-Organism-Response (SOR) model, the research finds that
    flexible time defined as the user’s ability to choose when to engage
    with the platform has a direct and indirect effect on customer visit
    behavior, with shopping need acting as a key mediating variable.
    Consumers who experience greater autonomy over their time tend to
    develop stronger internal motivations to shop, which in turn leads
    to increased platform visits. This finding not only advances the
    theoretical framework of digital consumer engagement but also
    introduces temporal flexibility as a novel construct in
    understanding online shopping motivations.</p>
  </disp-quote>
</sec>





<sec>
  <title>RECOMENDATION</title>
  <disp-quote>
    <p>From a practical standpoint, these findings offer significant
    implications for e-commerce strategy and digital marketing design.
    Shopee and similar platforms can leverage time-based personalization
    such as scheduling promotions during peak leisure periods,
    optimizing push notifications for evening hours, and offering
    flexible navigation tools to enhance engagement. Furthermore, by
    reinforcing users’ sense of control over their shopping experience,
    platforms can shift shopping behavior from a necessity to an
    enjoyable routine, thereby increasing customer loyalty. In sum, time
    should be regarded not only as a contextual variable but as a
    strategic lever in the development of user-centric, emotionally
    resonant digital commerce environments.</p>
  </disp-quote>
</sec>





<sec>
  <title>ADVANCED RESEARCH</title>
  <disp-quote>
    <p>For future advanced research, scholars are encouraged to explore
    the longitudinal impact of flexible time on consumer loyalty and
    purchasing behavior over extended periods, particularly across
    diverse e-commerce platforms beyond Shopee. Further studies could
    also integrate psychographic and demographic moderators such as age,
    digital literacy, or work-life balance preferences to examine
    whether the effects of flexible time vary across user segments.
    Moreover, employing mixed-method approaches, such as combining
    PLS-SEM with in-depth interviews or digital ethnography, may offer
    richer insights into the emotional and cognitive mechanisms
    underlying shopping need formation. Investigating the role of
    AI-driven personalization and its interaction with flexible time
    could also contribute to a deeper understanding of how algorithmic
    interventions shape consumer engagement in real-time.</p>
  </disp-quote>
</sec>








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    <volume>75</volume>
    <issue>1</issue>
    <fpage>74</fpage>
    <lpage>85</lpage>
    <pub-id pub-id-type="doi">10.1007/s41449-020-00230-x</pub-id>
  </element-citation>
</ref>

</ref-list>
</sec>
</body>
</article>
