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  <doc>
    <id>2103</id>
    <completedYear>2025</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>38</pageFirst>
    <pageLast>54</pageLast>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>28</volume>
    <type>article</type>
    <publisherName>Comenius University Bratislava</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Investigating the mediating effect of quiet quitting on turnover intention across generations X, Y and Z</title>
    <abstract language="eng">Purpose – This study sets out to examine the mediating role of quiet quitting in the relationship between various workplace antecedents and turnover intention, with a specific focus on generational differences across GenX, GenY, and GenZ.&#13;
Aims(s) – The primary aim is to identify the antecedents that show significant indirect generation-specific effects on turnover intention via quiet quitting. Design/methodology/approach – Utilizing a sample of 2,193 urban and suburban employees from Berlin, Germany, this study tested a mediation model featuring five independent variables linked to quiet quitting, with turnover intention as the outcome variable. The analysis employed Cronbach’s alpha, confirmatory factor analysis, multicollinearity checks, and multiple-mediation regression techniques.&#13;
Findings – The results reveal that quiet quitting partially mediated the relationship between dissatisfaction and turnover intention for GenY, as well as the relationship between negative extra-role behavior and cynicism/depersonalization for GenZ. Additionally, negative work-life balance showed partial mediation across all three generations—GenX, GenY, and GenZ. Full mediation effects were observed specifically in GenZ for both negative extra-role behavior and cynicism/depersonalization. No significant mediating effect of quiet quitting was found for disengagement. Limitations of the study – The sample is limited to Berlin and its suburbs, with no comparative data from other regions. The generational groups are represented by moderately sized subsamples. Additionally, after conducting reliability analysis and confirmatory factor analysis (CFA), most scales were reduced to just two or three items.&#13;
Originality/value – This study positions quiet quitting as a mediating factor within a network of related variables and is among the first to examine how these mediation effects differ across GenX, GenY, and GenZ.</abstract>
    <parentTitle language="eng">Journal of Human Resource Management – HR Advances and Developments</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-21039</identifier>
    <enrichment key="opus.import.date">2026-01-07T08:43:19+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.46287/CDRX6882</enrichment>
    <enrichment key="SourceTitle">Roedenbeck, M., Poljsak-Rosinski, P., Herold, M. (2025). Investigating the mediating effect of quiet quitting on turnover intention across generations X, Y and Z. Journal of Human Resource Management – HR Advances and Developments, 28(2), 38-54. https://doi.org/10.46287/CDRX6882</enrichment>
    <enrichment key="CopyrightInfo">Author(s) acknowledge that JHRM is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. Author holds the copy right without restrictions and keeps the publishing rights. https://www.jhrm.eu/publishing-agreement-copyright/</enrichment>
    <licence>Das Dokument ist urheberrechtlich geschützt.</licence>
    <author>Marc Roedenbeck</author>
    <author>Petra Poljsak-Rosinski</author>
    <author>Marcel Herold</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GenX</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GenY</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GenZ</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mediation analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quiet quitting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>turnover intention</value>
    </subject>
    <collection role="ddc" number="158">Angewandte Psychologie</collection>
    <collection role="ddc" number="658">Allgemeines Management</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft, Informatik, Recht</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="3">Diamond Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/2103/03Article-310-final-edited.pdf</file>
  </doc>
  <doc>
    <id>1837</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>519</pageFirst>
    <pageLast>537</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName>Emerald</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial neural network in soft HR performance management: new insights from a large organizational dataset</title>
    <abstract language="eng">Purpose&#13;
This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R2/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered.&#13;
&#13;
Design/methodology/approach&#13;
The study builds on a dataset (n &gt; 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006).&#13;
&#13;
Findings&#13;
The analysis reveals a substantial artificial neural network model performance (R2 &gt; 0.75) with an excellent fit statistic (nRMSE &lt;0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one.&#13;
&#13;
Originality/value&#13;
Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance.</abstract>
    <parentTitle language="eng">Evidence-based HRM</parentTitle>
    <identifier type="issn">2049-3991</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-18377</identifier>
    <enrichment key="opus.import.date">2023-12-08T07:45:04+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.1108/EBHRM-07-2022-0171</enrichment>
    <enrichment key="SourceTitle">Roedenbeck, M. and Poljsak-Rosinski, P. (2023), "Artificial neural network in soft HR performance management: new insights from a large organizational dataset", Evidence-based HRM, Vol. 11 No. 3, pp. 519-537. https://doi.org/10.1108/EBHRM-07-2022-0171</enrichment>
    <licence>Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International</licence>
    <author>Marc Roedenbeck</author>
    <author>Petra Poljsak-Rosinski</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>soft HRM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>performance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>drivers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>non-linearity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>prediction</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="658">Allgemeines Management</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft, Informatik, Recht</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="2">Green Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1837/1837.pdf</file>
  </doc>
</export-example>
