Essec\Faculty\Model\Contribution {#2220
  #_index: "academ_contributions"
  #_id: "15472"
  #_source: array:26 [
    "id" => 15472
    "slug" => "15472-quantifying-relationships-between-ordinal-categorical-variables-application-to-metrics-tracked-by-satisfaction-barometers"
    "yearMonth" => "2025-02"
    "year" => 2025
    "title" => "Quantifying relationships between ordinal categorical variables: Application to metrics tracked by satisfaction barometers"
    "description" => "BULTEZ, A., LAURENT, G. et LEMAY, L. (2025). Quantifying relationships between ordinal categorical variables: Application to metrics tracked by satisfaction barometers. <i>Recherche et Applications en Marketing</i>, 40(2), pp. 122-161."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "LAURENT Gilles"
        "bid" => "B00770447"
        "slug" => "laurent-gilles"
      ]
      1 => array:1 [
        "name" => "Bultez Alain"
      ]
      2 => array:1 [
        "name" => "Lemay Laurent"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Ordinal categorical variables"
      1 => "Cumulative and monotone multinomial regression"
      2 => "Random resampling (bootstrapping)"
      3 => "Satisfaction barometer"
      4 => "Net Promoter Score (NPS)"
    ]
    "updatedAt" => "2026-07-02 14:20:50"
    "publicationUrl" => "https://doi.org/10.1177/07673701241305467"
    "publicationInfo" => array:3 [
      "pages" => "122-161"
      "volume" => "40"
      "number" => "2"
    ]
    "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" => """
        Perceptions, attitudes, intentions, . . ., sont, à des fins de segmentation des marchés, de suivi ou de prévision des réactions des consommateurs, régulièrement étudiées. Ces composantes cognitives, affectives, ou comportementales, sont le plus souvent mesurées par les réponses à des questions à choix multiples : modalités prenant la forme d’un nombre limité de classes ordonnées, avec points d’ancrage, mais pas toujours toutes sémantiquement différenciées. L’analyse statistique de telles données, qualitatives, dites catégorielles ordinales, conduit généralement à les encoder numériquement et à les considérer quantitatives. Ce faisant, on leur attribue alors des propriétés dont elles ne sont pas dotées, avec pour conséquence des risques de biais. Pour les traiter correctement nous proposons une paramétrisation économétrique des relations entre de telles variables, respectant strictement leur contenu informatif.\n
        Alliant techniques de régression multinomiale cumulative et monotone, ainsi que de rééchantillonnage aléatoire, notre méthodologie permet de tester rigoureusement si des métriques de feedback de nature catégorielle ordinale pourraient être assimilées à des mesures d’intervalles. Nous l’expérimentons de manière concluante sur deux grands échantillons résultant des baromètres de satisfaction des clients de deux opérateurs de télécommunications, concurrents. Ainsi, nous montrons combien la disposition de leurs clients à les recommander s’explique par les satisfactions procurées par leurs produits-services. Subsidiairement, cette double étude de cas invalide le très populaire Net Promoter Score (NPS) censé synthétiser des distributions de degrés de vraisemblance d’une participation des consommateurs à la promotion de leurs marques favorites par bouche-à-oreille.
        """
      "en" => "Perceptions, attitudes, intentions, etc., are routinely studied for market segmentation, tracking, and predicting consumer reactions. These cognitive, affective, or behavioral components are most often measured by responses to multiple-choice questions: modalities that take the form of a limited number of ordered classes with anchor points, but not always all semantically differentiated. Statistical analysis of such qualitative, ordinal categorical data usually leads us to code them numerically and consider them quantitative. In doing so, we endow them with properties that they do not possess, which entails a risk of bias. To handle them correctly, we propose an econometric parametrization of their relationships, strictly respecting their information content. Combining cumulative and monotonic multinomial regression with random resampling techniques, our methodology allows us to rigorously test whether ordinal categorical feedback metrics can be assimilated to interval measures. We implement this approach conclusively on two large samples derived from the marketing barometers of two competing telecom operators. Thereby, we show to what extent their customers’ willingness to recommend them is explained by their satisfaction with the products and services these two players offer. As a byproduct, this double case study invalidates the very popular Net Promoter Score (NPS), which is supposed to synthesize distributions of degrees to which consumers are likely to participate in the promotion of their favorite brands by word-of-mouth."
    ]
    "authors_fields" => array:2 [
      "fr" => "Marketing"
      "en" => "Marketing"
    ]
    "indexedAt" => "2026-07-12T15:23:24.000Z"
    "docTitle" => "Quantifying relationships between ordinal categorical variables: Application to metrics tracked by satisfaction barometers"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/laurent-gilles">LAURENT Gilles</a>, Bultez Alain, Lemay Laurent"
    "docDescription" => "<span class="document-property-authors">LAURENT Gilles, Bultez Alain, Lemay Laurent</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">2025</span>"
    "keywordList" => "<a href="#">Ordinal categorical variables</a>, <a href="#">Cumulative and monotone multinomial regression</a>, <a href="#">Random resampling (bootstrapping)</a>, <a href="#">Satisfaction barometer</a>, <a href="#">Net Promoter Score (NPS)</a>"
    "docPreview" => "<b>Quantifying relationships between ordinal categorical variables: Application to metrics tracked by satisfaction barometers</b><br><span>2025-02 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1177/07673701241305467" target="_blank">Quantifying relationships between ordinal categorical variables: Application to metrics tracked by satisfaction barometers</a>"
  ]
  +lang: "en"
  +"_score": 9.021115
  +"_ignored": array:2 [
    0 => "abstract.en.keyword"
    1 => "abstract.fr.keyword"
  ]
  +"parent": null
}