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  <journal-id journal-id-type="publisher-id">46</journal-id>
  <journal-id journal-id-type="short-title">gssr</journal-id>
  <journal-id journal-id-type="doi">10.31703/gssr</journal-id>
  <journal-title-group>
    <journal-title>Global Social Sciences Review</journal-title>
    <abbrev-journal-title abbrev-type="publisher">gssr</abbrev-journal-title>
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  <issn publication-format="print">2520-0348</issn>
  <issn publication-format="electronic">2616-793X</issn>
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    <publisher-name>Humanity Publications</publisher-name>
    <publisher-loc>Pakistan</publisher-loc>
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<article-meta>
  <article-id pub-id-type="publisher-id">390701</article-id>
  <article-id pub-id-type="doi">10.31703/gssr.2019(IV-II).28</article-id>
  <article-id pub-id-type="other" specific-use="submission-id">1170</article-id>
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      <subject>article</subject>
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  <title-group>
    <article-title xml:lang="en">The Determinants Influencing the Influx of Counterfeit Luxury Goods in Pakistan</article-title>
  </title-group>
<contrib-group>
  <contrib contrib-type="author" seq="1" corresp="yes">
    <name>
      <surname>Saeed</surname>
      <given-names>Abid</given-names>
    </name>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing – original draft</role>
    <xref ref-type="aff" rid="aff1"/>
    <xref ref-type="corresp" rid="cor1"/>
  </contrib>
  <contrib contrib-type="author" seq="2">
    <name>
      <surname>Paracha</surname>
      <given-names>Osman Sadiq</given-names>
    </name>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
    <xref ref-type="aff" rid="aff2"/>
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  <aff id="aff1">
    <label>1</label>
    <institution-wrap>
      <institution>Department of Management Sciences, COMSATS University</institution>
    </institution-wrap>
    <named-content content-type="author-role">PhD Scholar</named-content>
    <addr-line>Islamabad</addr-line>
    <country>Pakistan</country>
  </aff>
  <aff id="aff2">
    <label>2</label>
    <institution-wrap>
      <institution>Department of Management Sciences, COMSATS University</institution>
    </institution-wrap>
    <named-content content-type="author-role">Assistant Professor</named-content>
    <addr-line>Islamabad</addr-line>
    <country>Pakistan</country>
  </aff>
</contrib-group>
<author-notes>
  <corresp id="cor1">Corresponding Author: Abid Saeed, PhD Scholar, Department of Management Sciences,COMSATS University, Islamabad, Pakistan.. Contact: 0</corresp>
<fn fn-type="COI-statement" id="fn-coi">
  <p>The authors declare that they have no conflicts of interest.</p>
</fn>
<fn fn-type="ethics-statement" id="fn-ethics">
  <p>This study did not require formal ethics approval.</p>
</fn>
<fn fn-type="data-availability-statement" id="fn-data">
  <p>Data sharing is not applicable to this article.</p>
</fn>
</author-notes>
<pub-date pub-type="epub" date-type="pub" publication-format="electronic">
  <day>30</day>
  <month>06</month>
  <year>2019</year>
</pub-date>
<pub-date pub-type="collection">
  <month>06</month>
  <year>2019</year>
</pub-date>
<pub-date date-type="pub" publication-format="print">
  <day>16</day>
  <month>02</month>
  <year>2022</year>
</pub-date>
  <volume>4</volume>
  <issue>2</issue>
  <season>Spring</season>
  <fpage>211</fpage>
  <lpage>221</lpage>
  <history>
    <date date-type="accepted">
      <day>16</day>
      <month>02</month>
      <year>2022</year>
    </date>
  </history>
<funding-group>
  <funding-statement>
<p>The authors received no specific funding for this work.</p>
  </funding-statement>
</funding-group>
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  <copyright-year>2019</copyright-year>
  <copyright-holder>Humanity Publications</copyright-holder>
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</permissions>
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</supplementary-material>
  <abstract>
    <p>The demand for counterfeit goods has exponentially grown and counterfeit goods trade has consequently emerged as a global problem. The present study investigates the determinants that encourage consumers to acquire counterfeit luxury goods. This research further analyzes purchase intention as mediator and gender role as a moderator between the contextual factors and consumer behavior. A quantitative approach was applied through a questionnaire to gather data from 380 Pakistani respondents. The measurement and structural model assessed through Smart PLS. The results confirmed that purchase intention acts as a mediator between hedonic motives, materialism and consumer behavior. However, purchase intention has no mediating effect on economic benefits. Similarly, gender role as moderator was insignificant.</p>
  </abstract>
<kwd-group kwd-group-type="author-keywords">
  <kwd>Consumer Behavior</kwd>
  <kwd>Counterfeit Luxury Goods</kwd>
  <kwd>Gender</kwd>
  <kwd>Pakistan</kwd>
  <kwd>Purchase Intention.</kwd>
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</front>
<body>
<sec id="sec-1">
  <title>Introduction and Background</title>
<p>Counterfeiting is a centuries-old phenomenon and one of the oldest examples of counterfeiting is the fake coins which were common in the Roman era. “The total value of global counterfeiting has reached 1.2 trillion USD in 2017 and is bound to reach 1.82 trillion USD by the year 2020” (Research and Markets, 2018). Pakistan comes in the top ten countries of the world wherefrom most fake goods originate (OECD/EUIPO, 2016) and counterfeit goods are easily available and sold openly (Ahmad, Yousif, Shabeer, &amp; Imran, 2014).</p><p>Despite the extensive research on counterfeiting in developed countries, no study can be found to determine the causes of counterfeit purchases in Pakistan. This study played an important role in filling this research gap. Firstly, we developed a theoretical model based on contemporary literature to explore the determinants that encourage individuals to buy counterfeit luxury goods. Secondly, we extended the scope of the literature by applying and validating the TRA and TPB on counterfeit luxury goods. Thirdly, we focused on purchase intention as a possible mediator between economic benefits, hedonic motives, materialism and consumer behavior. Furthermore, we used demographic variable gender as a moderator to understand the gender base purchase differences. Lastly, remedies were suggested to anti-counterfeit organizations to overcome this unlawful business practice. The findings will be helpful for the governments, marketing professionals and policymakers who are trying to overcome counterfeiting.</p>
</sec>
<sec id="sec-2">
  <title>Literature Review and Hypotheses Development</title>
<p>Counterfeiting</p><p>The Pakistan Penal Code Section 28 defines counterfeiting as, &quot;A person is said to counterfeit who causes one thing to resemble another thing, intending by means of that resemblance to practice deception, or knowing it to be likely that deception will thereby be practised&quot; (Pakistan Penal Code Act, 1860). The contemporary literature identified three categories of counterfeiting (i) deceptive (ii) non-deceptive and (iii) blur counterfeiting. In deceptive form of counterfeiting, individuals are unaware that the product purchased by them is counterfeit (Phau &amp; Teah, 2009). However, “one-third of consumers would intentionally purchase counterfeit luxury goods and this type of counterfeit purchase is known as non-deceptive counterfeiting” (Amaral &amp; Loken, 2016;  Bian et al., 2016). Non-Deceptive counterfeiting mostly exists in luxury goods (Nia &amp; Zaichkowsky, 2000).</p><p>In blur type of counterfeiting, consumers are confused about whether the goods they are buying are counterfeit or original (Bian, 2006).</p><p>The Effect of Economic Benefits on Purchase Intention and Consumer Behavior</p><p>Consumers value the economic benefits of counterfeit because the price considerably influences the consumer’s decision when opting for a counterfeit product (Gentry, Putrevu, &amp; Shultz, 2006). Consumers always prefer to pay a lower price with certain acceptable standards of product quality Ang et al., (2001) and would prefer a counterfeit product instead of a genuine brand if the price difference is significant (Bian &amp; Moutinho, 2009; Bloch). Due to these factors, we presented the hypotheses:</p><p>Hypothesis 1: Economic benefits effect significantly and positively the counterfeit luxury goods purchase intention.</p><p>Hypothesis 2: Economic benefits effect significantly and positively the counterfeit luxury goods purchase behavior.</p><p>The Effect of Hedonic Motives on Purchase Intention and Consumer Behavior</p><p>Hedonic motives are another important factor that encourages consumers to purchase products (Kang &amp; Park-Poaps, 2010). Consumers consider luxury goods as a novel, status symbol and try to match them with their personality and therefore consumers get attracted and purchase counterfeit goods. (Penz &amp; Sto¨ttinger, 2008). Therefore, we postulated the following hypotheses:</p><p>Hypothesis 3: Hedonic motives effect significantly and positively the counterfeit luxury goods purchase 		          intention.</p><p>Hypothesis 4: Hedonic motives effect significantly and positively the counterfeit luxury goods purchase behavior.</p><p><break/></p><p>The Influence of Materialism on Purchase Intention and Consumer Behavior</p><p>Consumers with materialistic traits give relatively high importance to owning possessions (Belk, 1985). They are highly concerned about exhibiting their possessions to other people or groups to flaunt their high status in society (Fitzmaurice &amp; Comegys, 2006). They consider material goods, as valuable to acquire centrality, success and happiness in life (Richins, 1994). Therefore, we presented the hypotheses:</p><p>Hypothesis 5: Materialism effect significantly and positively the counterfeit luxury goods purchase intention.</p><p>Hypothesis 6: Materialism effect significantly and positively the counterfeit luxury goods purchase behavior.</p><p><break/></p><p>Mediating Role of Purchase Intention</p><p>This research seeks to determine whether purchase intention acts as a mediator between the independent variables (economic benefits, hedonic motives &amp; materialism) and the dependent variable (consumer behavior). Past studies have provided the theoretical background for the mediation effect of purchase intention on consumer behavior (De Matos, Ituassu, &amp; Rossi, 2007). Therefore, we proposed that purchase intention plays a mediating role. The proposed hypothesis is:</p><p>Hypothesis 7: Purchase intention mediates the relationship between contextual variables and consumer behavior.</p><p><break/></p><p>Moderating Role of Gender</p><p>Recent studies confirmed gender differences exist and researchers like Singhapakdi, (2004), highlighted that comparatively female respondent was more ethical than male.  Similarly, Kwong, Yau, Lee, Sin, &amp; Tse, (2003) found males were comfortable in purchasing counterfeit CDs as compared to females. If gender difference patterns exist, it is important for marketers to investigate them to develop a more appropriate gender-based strategy to fight against counterfeit activities. Therefore, the proposed hypothesis is:</p><p>Hypothesis 8a: Gender positively moderates the relationship between contextual variables and intention.</p><p>Hypothesis 8b: Gender positively moderates the relationship between contextual variables and consumer                          behavior.</p>
</sec>
<sec id="sec-3">
  <title>Theoretical Framework</title>
<p>This study draws theoretical support to analyzed counterfeit luxury goods purchasing behavior from reasoned action and planned behavior theories. The research model posits a total of five variables, purchase intention presented as a mediating variable between independent and dependent variables. Furthermore, gender studied as a moderator variable between contextual variables and purchase intention along with consumer behavior. The proposed model presented in Figure 1 (on next page)</p>
</sec>
<sec id="sec-4">
  <title>Figure 1</title>
<p>The Proposed Model</p>
</sec>
<fig id="fig-1"><alt-text>Figure 1</alt-text><caption><title>Figure 1</title></caption><graphic xlink:href="https://gssrjournal.com/kVGeWSvsdN/Figure 1 .jpg"/></fig>
<sec id="sec-5">
  <title>Research Methodology</title>
<p>Research design</p><p>Data was collected from Pakistani consumers based in Pakistan. A sample composed of 380 respondents were gathered. The collected sample was evenly distributed on a gender basis (190 males and 190 females). The respondents were selected through a non-probability snowball sampling technique.</p><p><break/></p><p>Measures</p><p>The research questionnaire consisted of a total of 40 items with five variables plus demographic analysis. The selected constructs were adapted from established and cross-nationally validated scales. The variable economic benefits were measured with six items adapted from Lee and Yoo (2009), hedonic motives with ten items measured by using scale developed by Babin et al., (1994), materialism measured by using nine items of Richins (1994) materialism scale, purchase intention with four items measured by using an adapted scale of De Matos et al., (2007), consumer behavior was measured with five items adapted from Fan, Lan, Huang, and Chang, (2013). These constructs were measured through five-point Likert scale, where 1 “strongly disagree” and 5 “strongly agree”. The demographic characteristics consisted of gender, age, location, qualification, occupation, and monthly income.</p>
</sec>
<sec id="sec-6">
  <title>Data Analysis</title>
<p>The
measurement and structural model assessed through Smart PLS Version 3.2.8. PLS
is a recommended technique to simultaneously measure regression and
confirmatory factor analysis (Garson,
2016). The data were first examined
for missing values and for this purpose a widely accepted list-wise deletion
method was selected.</p><p><bold>Descriptive Statistics</bold></p><p>The demographic characteristics analysis indicated
that the selected sample of the
population primarily consists of a young,
educated, salaried class with quite reasonable income level and equally
distributed on gender basis across Pakistan. Presented below in Table 1.</p><p><bold>Table 1. </bold>Descriptive
Statistics</p> <table-wrap id="table1"><label>Table 1</label><caption><title>Table 1</title></caption><table><tbody><tr><td valign="top"> <p><bold>Demographic
  Variables</bold></p> </td><td valign="top"> <p><bold>Description</bold></p> </td><td> <p><bold>Frequency</bold></p> </td><td> <p><bold>Percent</bold></p> </td></tr><tr><td rowspan="2"> <p>Gender</p> </td><td> <p>Male</p> </td><td> <p>190</p> </td><td> <p>50.0 %</p> </td></tr><tr><td> <p>Female</p> </td><td> <p>190</p> </td><td> <p>50.0 %</p> </td></tr><tr><td rowspan="5"> <p>Age</p> <p>(in years)</p> </td><td> <p>13 to 19</p> </td><td> <p>4</p> </td><td> <p>1.1 %</p> </td></tr><tr><td> <p>20 to 30</p> </td><td> <p>105</p> </td><td> <p>27.6 %</p> </td></tr><tr><td> <p>31 to 40</p> </td><td> <p>205</p> </td><td> <p>53.9 %</p> </td></tr><tr><td> <p>41 to 50</p> </td><td> <p>52</p> </td><td> <p>13.7 %</p> </td></tr><tr><td> <p>51 and above</p> </td><td> <p>14</p> </td><td> <p>3.7 %</p> </td></tr><tr><td rowspan="6"> <p>Location</p> </td><td> <p>Federal Area</p> </td><td> <p>122</p> </td><td> <p>32.1 %</p> </td></tr><tr><td> <p>Punjab</p> </td><td> <p>144</p> </td><td> <p>37.9 %</p> </td></tr><tr><td> <p>Sindh</p> </td><td> <p>17</p> </td><td> <p>4.5 %</p> </td></tr><tr><td> <p>Baluchistan</p> </td><td> <p>11</p> </td><td> <p>2.9 %</p> </td></tr><tr><td> <p>KPK</p> </td><td> <p>61</p> </td><td> <p>16.1 %</p> </td></tr><tr><td> <p>Others</p> </td><td> <p>25</p> </td><td> <p>6.6 %</p> </td></tr><tr><td rowspan="4"> <p>Qualification</p> </td><td> <p>Matric or below</p> </td><td> <p>0</p> </td><td> <p>0 %</p> </td></tr><tr><td> <p>Under Graduate</p> </td><td> <p>11</p> </td><td> <p>2.9 %</p> </td></tr><tr><td> <p>Graduate</p> </td><td> <p>147</p> </td><td> <p>38.7 %</p> </td></tr><tr><td> <p>Post Graduate</p> </td><td> <p>222</p> </td><td> <p>58.4 %</p> </td></tr><tr><td rowspan="4">  <p>Occupation</p> </td><td> <p>Student</p> </td><td> <p>39</p> </td><td> <p>10.3 %</p> </td></tr><tr><td> <p>Salary Person</p> </td><td> <p>315</p> </td><td> <p>82.9 %</p> </td></tr><tr><td> <p>Business People</p> </td><td> <p>3</p> </td><td> <p>0.8 %</p> </td></tr><tr><td> <p>Housewife</p> </td><td> <p>23</p> </td><td> <p>6.1 %</p> </td></tr><tr><td rowspan="5">  <p>Income</p> <p>Per Month</p> <p>(in PKR)</p> </td><td> <p>Below 25,000/-</p> </td><td> <p>63</p> </td><td> <p>16.6 %</p> </td></tr><tr><td> <p>26,000/- to
  50,000/-</p> </td><td> <p>79</p> </td><td> <p>20.8 %</p> </td></tr><tr><td> <p>51,000/- to
  75,000/-</p> </td><td> <p>75</p> </td><td> <p>19.7 %</p> </td></tr><tr><td> <p>76,000/- to
  100,000/-</p> </td><td> <p>62</p> </td><td> <p>16.3 %</p> </td></tr><tr><td> <p>Above 100,000/-</p> </td><td> <p>101</p> </td><td> <p>26.6 %</p> </td></tr></tbody></table></table-wrap> <p><bold>Empirical
Results</bold></p><p>The skewness and kurtosis values are important in
the analysis of behavioral research data. The recommended best fit range of
kurtosis is ± 2 (Gravetter &amp; Wallnau, 2014). The research data confirmed that all constructs
were within the recommended best fit range of kurtosis as presented in Table 2.</p><table-wrap id="table2"><label>Table 2</label><caption><title>Mean, SD, Skewness, Kurtosis, and Items Description</title></caption><table><tbody><tr><td valign="top"> <p><bold>Variables</bold></p> </td><td> <p><bold>Mean</bold></p> </td><td> <p><bold>Std. Deviation</bold></p> </td><td> <p><bold>Skewness</bold></p> </td><td> <p><bold>Kurtosis</bold></p> </td><td> <p><bold>Items</bold></p> </td></tr><tr><td> <p>Economic
  Benefits (EB)</p> </td><td> <p>3.31</p> </td><td> <p>1.02</p> </td><td> <p>-.19</p> </td><td> <p>-1.175</p> </td><td> <p>06</p> </td></tr><tr><td> <p>Hedonic
  Motive (HM)</p> </td><td> <p>3.10</p> </td><td> <p>1.01</p> </td><td> <p>.24</p> </td><td> <p>-1.120</p> </td><td> <p>10</p> </td></tr><tr><td> <p>Materialism
  (M)</p> </td><td> <p>2.96</p> </td><td> <p>1.03</p> </td><td> <p>.51</p> </td><td> <p>-1.046</p> </td><td> <p>09</p> </td></tr><tr><td> <p>Purchase
  Intention (PI)</p> </td><td> <p>3.39</p> </td><td> <p>0.97</p> </td><td> <p>.13</p> </td><td> <p>-1.036</p> </td><td> <p>04</p> </td></tr><tr><td> <p>Consumer
  Behavior (CB)</p> </td><td> <p>3.52</p> </td><td> <p>0.96</p> </td><td> <p>-.44</p> </td><td> <p>-.674</p> </td><td> <p>05</p> </td></tr></tbody></table></table-wrap><p>The diagonal values
shown in table 3 represented the square roots of the AVEs, and all values were
higher than the correlation value of these constructs. Therefore, the results
supported the good discriminant and convergent validity of the data.</p><p><bold>Table 3. </bold>Bi-variate Correlations, Descriptive and Discriminant Validity</p><table-wrap id="table3"><label>Table 3</label><caption><title>Table 3</title></caption><table><tbody><tr><td valign="top"> <p><bold>Variables</bold></p> </td><td> <p><bold>Mean</bold></p> </td><td> <p><bold>S.D</bold></p> </td><td> <p><bold>EB</bold></p> </td><td> <p><bold>HM</bold></p> </td><td> <p><bold>M</bold></p> </td><td> <p><bold>PI</bold></p> </td><td> <p><bold>CB</bold></p> </td></tr><tr><td> <p>EB</p> </td><td> <p>3.31</p> </td><td> <p>1.02</p> </td><td> <p><bold>0.901</bold></p> </td><td>  </td><td>  </td><td>  </td><td>  </td></tr><tr><td> <p>HM</p> </td><td> <p>3.10</p> </td><td> <p>1.01</p> </td><td> <p>.424<sup>**</sup></p> </td><td> <p><bold>0.917</bold></p> </td><td>  </td><td>  </td><td>  </td></tr><tr><td></td><td>  </td><td>  </td><td> <p>.000</p> </td><td>  </td><td>  </td><td>  </td><td>  </td></tr><tr><td> <p>M</p> </td><td> <p>2.96</p> </td><td> <p>1.03</p> </td><td> <p>.283<sup>**</sup></p> </td><td> <p>.034</p> </td><td> <p><bold>0.875</bold></p> </td><td>  </td><td>  </td></tr><tr><td></td><td>  </td><td>  </td><td> <p>.000</p> </td><td> <p>.695</p> </td><td>  </td><td>  </td><td>  </td></tr><tr><td> <p>PI</p> </td><td> <p>3.39</p> </td><td> <p>0.97</p> </td><td> <p>.306<sup>**</sup></p> </td><td> <p>.399<sup>**</sup></p> </td><td> <p>.224<sup>**</sup></p> </td><td> <p><bold>0.965</bold></p> </td><td>  </td></tr><tr><td></td><td>  </td><td>  </td><td> <p>.000</p> </td><td> <p>.000</p> </td><td> <p>.000</p> </td><td>  </td><td>  </td></tr><tr><td> <p>CB</p> </td><td> <p>3.52</p> </td><td> <p>0.96</p> </td><td> <p>.408<sup>**</sup></p> </td><td> <p>.433<sup>**</sup></p> </td><td> <p>.316<sup>**</sup></p> </td><td> <p>.548<sup>**</sup></p> </td><td> <p><bold>0.934</bold></p> </td></tr><tr><td></td><td>  </td><td>  </td><td> <p>.000</p> </td><td> <p>.000</p> </td><td> <p>.000</p> </td><td> <p>.000</p> </td><td>  </td></tr><tr><td colspan="8" valign="top"> <p>**Correlation significant at
  0.05 level (2-tailed).</p> <p>Diagonal values represent square root of AVE.</p> <p>N = 380</p> <p>EB=Economic Benefits,
  HM=Hedonic Motives, M=Materialism, PI= Purchase Intention, CB = Consumer
  Behavior.</p> </td></tr></tbody></table></table-wrap>
</sec>
<sec id="sec-7">
  <title>The Measurement Model</title>
<p>The proposed measurement model consisted of a total of five variables. In the reflective measurement model, none of the latent variables has unidirectional paths. Each latent variable was connected to others and the covariance of the variables was estimated, as shown in Figure 2.</p>
</sec>
<sec id="sec-8">
  <title>Figure 2</title>
<p>The Measurement Model</p>
</sec>
<sec id="sec-9">
  <title>Figure 2</title>
<p>The Measurement Model</p>
</sec>
<sec id="sec-10">
<p>The
composite reliability (CR) examine the overall reliability of selected
heterogeneous but similar items. A CR value above 0.70 is enough to demonstrate
internal consistency and reliability (Hair,
Hult, Ringle, &amp; Sarstedt, 2016). As mentioned in Table 4. Convergent
validity was measured through the value of the average variance extracted
(AVE). The acceptable range for AVE value is above 0.50 Hair et
al., (2016). Results confirmed
that AVE values were above the acceptable range. As shown in Table 4.</p><table-wrap id="table4"><label>Table 4</label><caption><title>Results of the Measurement Model</title></caption><table><tbody><tr><td> <p><bold>Latent Variable</bold></p>  </td><td> <p><bold>Indicator’s</bold></p>  </td><td> <p><bold>Factor</bold></p> <p><bold>Loadings</bold></p>  </td><td> <p><bold>Cronbach&apos;s Alpha</bold></p> <p><bold>(</bold><bold>?</bold><bold>)</bold></p> </td><td> <p><bold>Composite</bold></p> <p><bold>Reliability</bold></p> <p><bold>(CR)</bold></p> </td><td> <p><bold>Average</bold></p> <p><bold>Variance</bold></p> <p><bold>Extracted (AVE)</bold></p> </td></tr><tr><td rowspan="6"> <p>Economic Benefits (EB)</p>  </td><td> <p>EB1</p> </td><td> <p>0.927</p> </td><td rowspan="6"> <p>0.954</p> </td><td rowspan="6"> <p>0.963</p> </td><td rowspan="6"> <p>0.813</p> </td></tr><tr><td> <p>EB2</p> </td><td> <p>0.925</p> </td></tr><tr><td> <p>EB3</p> </td><td> <p>0.934</p> </td></tr><tr><td> <p>EB4</p> </td><td> <p>0.920</p> </td></tr><tr><td> <p>EB5</p> </td><td> <p>0.882</p> </td></tr><tr><td> <p>EB6</p> </td><td> <p>0.815</p> </td></tr><tr><td rowspan="10"> <p>Hedonic
  Motives (HM)</p> </td><td> <p>HM1</p> </td><td> <p>0.912</p> </td><td rowspan="10"> <p>0.979</p> </td><td rowspan="10"> <p>0.982</p> </td><td rowspan="10"> <p>0.841</p> </td></tr><tr><td> <p>HM2</p> </td><td> <p>0.929</p> </td></tr><tr><td> <p>HM3</p> </td><td> <p>0.911</p> </td></tr><tr><td> <p>HM4</p> </td><td> <p>0.910</p> </td></tr><tr><td> <p>HM5</p> </td><td> <p>0.918</p> </td></tr><tr><td> <p>HM6</p> </td><td> <p>0.899</p> </td></tr><tr><td> <p>HM7</p> </td><td> <p>0.932</p> </td></tr><tr><td> <p>HM8</p> </td><td> <p>0.931</p> </td></tr><tr><td> <p>HM9</p> </td><td> <p>0.923</p> </td></tr><tr><td> <p>HM10</p> </td><td> <p>0.907</p> </td></tr><tr><td rowspan="9"> <p>Materialism (M)</p>  </td><td> <p>M1</p> </td><td> <p>0.865</p> </td><td rowspan="9"> <p>0.962</p> </td><td rowspan="9"> <p>0.967</p> </td><td rowspan="9"> <p>0.765</p> </td></tr><tr><td> <p>M2</p> </td><td> <p>0.903</p> </td></tr><tr><td> <p>M3</p> </td><td> <p>0.874</p> </td></tr><tr><td> <p>M4</p> </td><td> <p>0.911</p> </td></tr><tr><td> <p>M5</p> </td><td> <p>0.869</p> </td></tr><tr><td> <p>M6</p> </td><td> <p>0.867</p> </td></tr><tr><td> <p>M7</p> </td><td> <p>0.846</p> </td></tr><tr><td> <p>M8</p> </td><td> <p>0.886</p> </td></tr><tr><td> <p>M9</p> </td><td> <p>0.848</p> </td></tr><tr><td rowspan="4"> <p>Purchase Intention (PI)</p>  </td><td> <p>P1</p> </td><td> <p>0.966</p> </td><td rowspan="4"> <p>0.975</p> </td><td rowspan="4"> <p>0.982</p> </td><td rowspan="4"> <p>0.931</p> </td></tr><tr><td> <p>P2</p> </td><td> <p>0.962</p> </td></tr><tr><td> <p>P3</p> </td><td> <p>0.959</p> </td></tr><tr><td> <p>P4</p> </td><td> <p>0.973</p> </td></tr><tr><td rowspan="5"> <p>Consumer Behavior (CB)</p>  </td><td> <p>CB1</p> </td><td> <p>0.924</p> </td><td rowspan="5"> <p>0.963</p> </td><td rowspan="5"> <p>0.971</p> </td><td rowspan="5"> <p>0.872</p> </td></tr><tr><td> <p>CB2</p> </td><td> <p>0.949</p> </td></tr><tr><td> <p>CB3</p> </td><td> <p>0.936</p> </td></tr><tr><td> <p>CB4</p> </td><td> <p>0.923</p> </td></tr><tr><td> <p>CB5</p> </td><td> <p>0.937</p> </td></tr></tbody></table></table-wrap> <p><bold>The
Structural Model</bold></p><p>The
R² value estimated for purchase intention (mediating variable) and consumer behavior (dependent variable) were 0.212 and
0.418 respectively, these values suggested 21.2% and 41.8% of the variance.
These results provided support for a satisfactory and substantial model. The
proposed hypotheses were tested through the non-parametric bootstrapping
process. A re-sample of 5,000 was processed to obtain the standard error. The
path coefficient and t-values are shown in Figure 3 and Table 5.</p>
</sec>
<sec id="sec-11">
  <title>Figure 3</title>
<p>Main Effect Model</p>
</sec>
<fig id="fig-2"><alt-text>Figure 3</alt-text><caption><title>Figure 3</title></caption><graphic xlink:href="https://gssrjournal.com/kVGeWSvsdN/Figure 3.jpg"/></fig>
<sec id="sec-12">
<p>The
analysis of path coefficient’s results confirmed that economic benefits (EB) has
no direct and significant impact on purchase intention (PI) (? = 0.108; t = 1.732; p = 0.083). The
results indicated to reject Hypothesis 1. The path coefficient’s results of economic benefits (EB) has
a direct and significant positive impact on consumer behavior (CB) (? = 0.150; t = 2.503; p &lt; 0.05). The
results supported the proposed hypothesis and based on that Hypothesis 2
accepted. The path coefficient’s results of hedonic motives
(HB) has a direct and significant impact on purchase intention (PI) (? = 0.346; t = 6.032; p &lt; 0.05). The
results confirmed the proposed hypothesis and based on that Hypothesis 3
accepted. The path coefficient’s results of hedonic motives
(HB) has a direct and significant positive impact on consumer behavior (CB) (? = 0.214; t = 3.612; p &lt; 0.05).
Based on these results Hypothesis 4 accepted. The path coefficient’s results of materialism (M) has a
direct and significant positive impact on purchase intention (PI) (? = 0.184; t = 3.662; p &lt; 0.05). The
results supported the proposed hypothesis and based on that Hypothesis
5accepted. The path coefficient’s results of materialism (M)
has a direct and significant positive impact on consumer behavior (CB) (? = 0.184; t = 4.048; p &lt; 0.05).
Based on these results Hypothesis 6 accepted. The path coefficient’s results of purchase intention (PI)
has a direct and significant positive impact on consumer behavior (CB) (? = 0.376; t = 6.286; p &lt; 0.05). The
supported the proposed hypothesis and based on these results Hypothesis 7
accepted.</p><table-wrap id="table5"><label>Table
5</label><caption><title>Regression Weights</title></caption><table><tbody><tr><td> <p><bold>Models</bold></p> </td><td> <p><bold>Original
  Sample</bold></p> </td><td> <p><bold>Mean
  (M)</bold></p> </td><td> <p><bold>Standard
  Deviation (STDEV)</bold></p> </td><td> <p><bold>T
  Statistics (|O/STDEV|)</bold></p> </td><td> <p><bold>P
  Values</bold></p> </td><td> <p><bold>Status</bold></p> </td></tr><tr><td> <p>EB
  -&gt;PI</p> </td><td> <p>0.108</p> </td><td> <p>0.108</p> </td><td> <p>0.062</p> </td><td> <p>1.732</p> </td><td> <p>0.083</p> </td><td> <p>Rejected</p> </td></tr><tr><td> <p>EB -&gt;CB</p> </td><td> <p>0.150</p> </td><td> <p>0.150</p> </td><td> <p>0.060</p> </td><td> <p>2.503</p> </td><td> <p>0.012</p> </td><td> <p>Accepted</p> </td></tr><tr><td> <p>HM
  -&gt;PI</p> </td><td> <p>0.346</p> </td><td> <p>0.346</p> </td><td> <p>0.058</p> </td><td> <p>6.032</p> </td><td> <p>0.000</p> </td><td> <p>Accepted</p> </td></tr><tr><td> <p>HM
  -&gt;CB</p> </td><td> <p>0.214</p> </td><td> <p>0.214</p> </td><td> <p>0.059</p> </td><td> <p>3.612</p> </td><td> <p>0.000</p> </td><td> <p>Accepted</p> </td></tr><tr><td> <p>M
  -&gt;PI</p> </td><td> <p>0.184</p> </td><td> <p>0.184</p> </td><td> <p>0.050</p> </td><td> <p>3.662</p> </td><td> <p>0.000</p> </td><td> <p>Accepted</p> </td></tr><tr><td> <p>M
  -&gt;CB</p> </td><td> <p>0.184</p> </td><td> <p>0.184</p> </td><td> <p>0.045</p> </td><td> <p>4.048</p> </td><td> <p>0.000</p> </td><td> <p>Accepted</p> </td></tr><tr><td> <p>PI
  -&gt;CB</p> </td><td> <p>0.376</p> </td><td> <p>0.376</p> </td><td> <p>0.060</p> </td><td> <p>6.286</p> </td><td> <p>0.000</p> </td><td> <p>Accepted</p> </td></tr></tbody></table></table-wrap><p><bold>EB</bold>=Economic Benefits, <bold>HM</bold>=Hedonic Motives, <bold>M</bold>=Materialism, <bold>PI</bold>= Purchase Intention, <bold>CB</bold>
= Consumer Behavior.</p><p><bold>The Mediating Analysis of Purchase
Intention</bold></p><p>The
mediating analysis confirmed that purchase intention (PI) has no mediating
effect between economic benefits (EB) and consumer behavior (CB) (? = 0.041; t = 1.6661; p &lt; 0.05).
However, purchase intention (PI) mediates the relationship between hedonic
motivation (HM) and consumer behavior (CB) (? = 0.130; t = 4.208; p &lt; 0.05).
Similarly, purchase intention (PI) mediates the relationship between
materialism (M) and consumer behavior (CB) (? = 0.069; t = 2.905; p &lt; 0.05).
Results confirmed that purchase intention has a mediating
effect between hedonic motives, materialism and consumer behavior. While purchase intention has no mediating effect with economic benefits and
consumer behavior as presented in table
6.</p><table-wrap id="table6"><label>Table 6</label><caption><title>Mediating Analysis</title></caption><table><tbody><tr><td valign="top"> <p><bold>Models</bold></p> </td><td> <p><bold>Path
  (A)</bold></p> </td><td> <p><bold>Path
  (B)</bold></p> </td><td> <p><bold>A*B</bold></p> </td><td> <p><bold>t-Value</bold></p> </td><td> <p><bold>Status</bold></p> </td></tr><tr><td> <p>EB -&gt; PI
  -&gt; CB</p> </td><td> <p>0.108</p> </td><td> <p>0.376</p> </td><td> <p>0.041</p> </td><td> <p>1.666</p> </td><td> <p>Insignificant</p> </td></tr><tr><td> <p>HM -&gt; PI
  -&gt; CB</p> </td><td> <p>0.346</p> </td><td> <p>0.376</p> </td><td> <p>0.130</p> </td><td> <p>4.208</p> </td><td> <p>Significant</p> </td></tr><tr><td> <p>M -&gt; PI
  -&gt; CB</p> </td><td> <p>0.184</p> </td><td> <p>0.376</p> </td><td> <p>0.069</p> </td><td> <p>2.905</p> </td><td> <p>Significant</p> </td></tr></tbody></table></table-wrap><p><bold>EB</bold>=Economic Benefits, <bold>HM</bold>=Hedonic Motives, <bold>M</bold>=Materialism, <bold>PI</bold>= Purchase Intention, <bold>CB</bold>
= Consumer Behavior.</p>
</sec>
<sec id="sec-13">
  <title>The Moderating Analysis of Gender</title>
<p>The
results for moderating variable gender demonstrated insignificant in either
regression equation or none of the value falls
within the acceptable range of P-Value i.e. (p &lt; 0.05) as mentioned in Table
7. The results support previous studies where Schiffman
&amp; Kanuk, (2004) mentioned: “sex roles have blurred, and gender is no longer an
accurate way to distinguish consumers in some product categories”. Similarly, Butler,
(2011) argue that gender has no place
in consumer research and should be abandoned. Based on these results both
hypothesis 8a and hypothesis 8b were rejected.</p><table-wrap id="table7"><label>Table 7</label><caption><title>Moderating Analysis</title></caption><table><tbody><tr><td> <p><bold>Models</bold></p> </td><td> <p><bold>Path
  Coefficients</bold></p> <p><bold>(Female)</bold></p> </td><td> <p><bold>Path
  Coefficients (Male)</bold></p> </td><td> <p><bold>Path
  Coefficients Difference</bold></p> <p><bold>(Male-Female)</bold></p> </td><td> <p><bold>P</bold></p> <p><bold>Values</bold></p> </td><td> <p><bold>Status</bold></p> </td></tr><tr><td> <p>EB -&gt; PI</p> </td><td> <p>0.130</p> </td><td> <p>0.085</p> </td><td> <p>0.045</p> </td><td> <p>0.642</p> </td><td> <p>Rejected</p> </td></tr><tr><td> <p>EB -&gt; CB</p> </td><td> <p>0.073</p> </td><td> <p>0.227</p> </td><td> <p>0.154</p> </td><td> <p>0.093</p> </td><td> <p>Rejected</p> </td></tr><tr><td> <p>HM -&gt; PI</p> </td><td> <p>0.362</p> </td><td> <p>0.330</p> </td><td> <p>0.032</p> </td><td> <p>0.612</p> </td><td> <p>Rejected</p> </td></tr><tr><td> <p>HM-&gt; CB</p> </td><td> <p>0.176</p> </td><td> <p>0.248</p> </td><td> <p>0.072</p> </td><td> <p>0.272</p> </td><td> <p>Rejected</p> </td></tr><tr><td> <p>M -&gt; PI</p> </td><td> <p>0.116</p> </td><td> <p>0.253</p> </td><td> <p>0.137</p> </td><td> <p>0.076</p> </td><td> <p>Rejected</p> </td></tr><tr><td> <p>M -&gt; CB</p> </td><td> <p>0.203</p> </td><td> <p>0.172</p> </td><td> <p>0.031</p> </td><td> <p>0.638</p> </td><td> <p>Rejected</p> </td></tr><tr><td> <p>PI -&gt; CB</p> </td><td> <p>0.479</p> </td><td> <p>0.281</p> </td><td> <p>0.198</p> </td><td> <p>0.947</p> </td><td> <p>Rejected</p> </td></tr></tbody></table></table-wrap><p><bold>EB</bold>=Economic Benefits, <bold>HM</bold>=Hedonic Motives, <bold>M</bold>=Materialism, <bold>PI</bold>=Purchase Intention, <bold>CB</bold>=Consumer
Behavior</p>
</sec>
<sec id="sec-14">
  <title>Discussion</title>
<p>The results indicated that hedonic motives and materialism have a positive impact on counterfeit luxury goods purchase intention and consumer behavior. These results matched with past studies (Erg?n, 2010; Kaufmann et al., 2016; Li, Lam, &amp; Liu, 2018; Lianto, 2015). While economic benefits are insignificant to purchase intention, they are positively related to consumer behavior. The results are again consistent with the previous work done by Bian &amp; Moutinho, (2009); Kwong et al., (2003); Stravinskiene, Dovaliene, &amp; Ambrazeviciute, (2013) which reported that consumers’ counterfeit purchase intention was not purely dependent on income. Similarly, results were unable to find any moderating effects of gender. The result of the non-significant role of gender support the previous research conducted by Hegarty &amp; Sims, (1978); Schiffman &amp; Kanuk, (2004); Singhapakdi &amp; Vitell, (1990), mentioned the limited role of gender to differentiate consumers in some product categories.</p>
</sec>
<sec id="sec-15">
  <title>Theoretical Contribution</title>
<p>Our research makes several theoretical contributions. First and foremost, this research work successfully applied behavioral theories TRA and TPB to study purchase intention leading to consumer purchase behavior. The study also develops a theoretical model by testing and validating the determinants responsible for purchase behavior towards counterfeit luxury goods. Past studies on this issue primarily covered the markets of advanced countries and very few studies explored developing economies like Pakistan. Perhaps this is the only study in the domain of counterfeit luxury goods that have successfully incorporated purchase intention and gender as mediator moderator in a single model. Thus, the proposed and empirically tested model will help the stakeholders understand why consumers have positive purchase behavior towards counterfeiting in Pakistan.</p><p><break/></p><p>Practical Implication</p><p>Ant counterfeit policymakers may wish to determine what can be done to restrain the ever-growing trend of buying counterfeit goods. Our research confirmed that materialism and hedonic motives effect positively to counterfeit purchase intention. Both these variables were related to the display of wealth and a sense of excitement. Therefore, marketers should develop policies to counter the counterfeit goods purchased only for the purpose of status seeking, fun and excitement (Bhardwaj, 2010). Brand owners should develop awareness about ethical purchasing behavior by discouraging consumers regarding the harmful impacts of counterfeit goods. The original brand owners should start educating consumers about the benefits associated with original luxury brands through marketing activities, such as seminars, workshops and special events.</p>
</sec>
<sec id="sec-16">
  <title>Limitations and Directions for Future Research</title>
<p>The selected sample cannot be claimed that it is a perfect representation of all Pakistani consumers. A major limitation of cross-sectional research analysis is that it represents views in relation to a specific time period. Similarly, the selected scale used in the current research may not produce the same results with other counterfeit goods. The theoretical model and data may or may not produce the same results in other countries. Another limitation is that it covered only counterfeit luxury goods and if the same parameter applied on other types of counterfeit products like food, medicines, and auto parts, it may result in more unfavorable consumer behavior towards counterfeits. Finally, this study is time and money constraint.</p><p>Some of the recommendations for future researchers are to study the post-purchase behavior that will help in understanding consumer’s feelings after using these goods. Future researchers can examine those consumers who exclusively buy counterfeit goods online and can even make a comparison of online purchasing with traditional purchasing of counterfeit goods. They can also use the same model in other countries where cultural differences exist. Another suggestion for future researchers is to observe the actual behaviors and emotions of the consumers through an experiment with real customers and retailers.</p>
</sec>
</body>
<back>
<fn-group content-type="conflict-of-interest">
  <title>Conflict of Interest</title>
  <fn fn-type="conflict">
<p>The authors declare that they have no conflicts of interest.</p>
  </fn>
</fn-group>
<fn-group content-type="ethics-statement">
  <title>Ethics Statement</title>
  <fn fn-type="ethics">
<p>This study did not require formal ethics approval.</p>
  </fn>
</fn-group>
<fn-group content-type="data-availability">
  <title>Data Availability</title>
  <fn fn-type="data-availability-statement">
<p>Data sharing is not applicable to this article.</p>
  </fn>
</fn-group>
<app-group>
  <app id="app-suppl">
    <title>Supplementary Materials</title>
<supplementary-material id="suppl-pdf" content-type="pdf" xlink:href="https://gssrjournal.com/pdf/gssr/kVGeWSvsdN.pdf">
  <label>PDF</label>
  <caption>
    <title>Full Text PDF</title>
  </caption>
</supplementary-material>
  </app>
</app-group>
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</ref-list>
</back>
</article>