Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "12252"
  #_source: array:26 [
    "id" => "12252"
    "slug" => "long-run-effects-in-dynamic-systems-new-tools-for-cross-lagged-panel-models"
    "yearMonth" => "2022-07"
    "year" => "2022"
    "title" => "Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models"
    "description" => "SHAMSOLLAHI, A., ZYPHUR, M.J. et OZKOK, O. (2022). Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models. <i>Organizational Research Methods</i>, 25(3), pp. 435-458."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "SHAMSOLLAHI Ali"
        "bid" => "B00767865"
        "slug" => "shamsollahi-ali"
      ]
      1 => array:1 [
        "name" => "ZYPHUR Michael J."
      ]
      2 => array:1 [
        "name" => "OZKOK Ozlem"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:7 [
      0 => "longitudinal theory"
      1 => "longitudinal modeling"
      2 => "causal inference"
      3 => "long-run effects"
      4 => "cross-lagged panel model"
      5 => " human resources management"
      6 => "organizational performance"
    ]
    "updatedAt" => "2023-09-22 16:44:01"
    "publicationUrl" => "https://journals.sagepub.com/doi/10.1177/1094428121993228"
    "publicationInfo" => array:3 [
      "pages" => "435-458"
      "volume" => "25"
      "number" => "3"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods."
      "en" => "Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods."
    ]
    "authors_fields" => array:2 [
      "fr" => "Marketing"
      "en" => "Marketing"
    ]
    "indexedAt" => "2024-11-21T13:21:44.000Z"
    "docTitle" => "Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/shamsollahi-ali">SHAMSOLLAHI Ali</a>, ZYPHUR Michael J., OZKOK Ozlem"
    "docDescription" => "<span class="document-property-authors">SHAMSOLLAHI Ali, ZYPHUR Michael J., OZKOK Ozlem</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">2022</span>"
    "keywordList" => "<a href="#">longitudinal theory</a>, <a href="#">longitudinal modeling</a>, <a href="#">causal inference</a>, <a href="#">long-run effects</a>, <a href="#">cross-lagged panel model</a>, <a href="#"> human resources management</a>, <a href="#">organizational performance</a>"
    "docPreview" => "<b>Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models</b><br><span>2022-07 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://journals.sagepub.com/doi/10.1177/1094428121993228" target="_blank">Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.769787
  +"parent": null
}