<?xml version="1.0" encoding="UTF-8"?>
<article article-type="Research Article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">collnet-journal-of-scientometrics-and-information-management</journal-id>
      <journal-title-group>
        <journal-title>COLLNET Journal of Scientometrics and Information Management</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">2168-930X</issn>
      <issn publication-format="print">0973-7766</issn>
      <publisher>
        <publisher-name>Taru Publications</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.47974/CJSIM-2025-01003</article-id>
      <title-group>
        <article-title>80/20 law in Information Production Processes (IPPs)</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Lathabai</surname>
            <given-names>Hiran Haridas</given-names>
          </name>
          <aff>Amrita Center for Research in Analytics, Education and Training, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, Kerala, 690525, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Gupta</surname>
            <given-names>Solanki</given-names>
          </name>
          <aff>School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, 201310, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Mohan</surname>
            <given-names>Rakhi</given-names>
          </name>
          <aff>Department of Statistics, Attingal, Government College, Thiruvananthapuram, Kerala, 695101, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Singh</surname>
            <given-names>Vivek Kumar</given-names>
          </name>
          <aff>Department of Computer Science, University of Delhi, Delhi, 110007, India</aff>
          <aff>NITI AYOG, Government of India</aff>
        </contrib>
      </contrib-group>
      <volume>19</volume>
      <issue>2</issue>
      <fpage>197</fpage>
      <lpage>214</lpage>
      <pub-date date-type="pub">
        <day>17</day>
        <month>12</month>
        <year>2025</year>
      </pub-date>
      <abstract>
        <p>Information Production Processes (IPPs) like source-item systems possess several properties. Skewness is one property that makes indicators such as h-type indicators relevant for determining key sources that produced the majority of items. Due to this skewness, the 80/20 rule (in which the top 80 percent of items may be produced by the top 20 percent of the sources) also is at play in IPPs. In this work, we investigate whether any of the major existing h-type indicators are capable of reflecting the 80/20 rule effectively. Also, can considering 20% of T (total publications) or 0.2T as an indicator, provide a better alternative? Or is there a room for new indicator or perhaps a new law? These questions are answered in this work. The implications and potential uses of the most suitable base indicator for scientometric applications, especially for science, technology and innovation (STI) management and for applications outside the realm of scientometrics are discussed.</p>
      </abstract>
      <kwd-group>
        <kwd>80/20 law</kwd>
        <kwd>Information Production Processes (IPPs)</kwd>
        <kwd>Skewness</kwd>
        <kwd>h-type indicators</kwd>
        <kwd>Ideal indicator</kwd>
        <kwd>y-index</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>access</meta-name>
          <meta-value>open</meta-value>
        </custom-meta>
        <custom-meta>
          <meta-name>retracted</meta-name>
          <meta-value>no</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
  </front>
</article>
