Exploring IT Governance Effectiveness: Identifying Sources of Divergence through the Adoption of a Behavioural-Based Organizational Routines Perspective
Notice bibliographique
Résumé
This research seeks to broaden and strengthen the holistic understanding of IT governance effectiveness by specifically examining why IT governance systems often fail to produce appropriate or desired IT and organizational behaviours.To address this objective, we investigate and develop a theoretical framework for understanding and explaining the varied sources of divergence that occur during the enactment of IT governance mechanisms.Defined as the difference between desired behaviours and actual behaviours, we argue that the acceptance and consideration of all sources of divergence within the enactment of IT governance mechanisms, is not only necessary, but critical to the appropriate design and maintenance of an effective IT governance system.Traditional IT governance perspectives, heavily rooted in the structural and normative aspects of oversight and control (i.e.structures), have limited our ability to adequately and fully understand how IT governance performs in practice.Framing IT governance mechanisms as routines, we draw on institutional theory and organizational routines theory as an alternative lens for understanding why organizational behaviours are not always aligned to those expected by IT governance owners.Based on Pentland and Feldman`s (2008) generative model of organizational routines, we establish a novel conceptualization for IT governance divergence that posits and delineates three primary sources of IT governance divergence: Representational Divergence, Translational Divergence and Performative Divergence.Through the in-depth examination of the IT investment planning, prioritization and selection routines within two exploratory case studies, we inductively propose a model for explaining IT governance divergence.We apply a narrative networks approach to frame and analyse qualitative data captured through semi-structured interviews, archival and document review and direct observation.Patternmatching and emergent themes analysis is performed to identify and define first-order and secondorder constructs, along with 15 relational propositions.Given the dearth of theoretically-grounded research in this domain, the central contribution of this study rests in the establishment of a robust theoretical framework of IT governance divergence upon which further cumulative empirical study can be undertaken.For the practitioner community, the recognition of divergence is necessary for designing IT governance systems that reduce and control negative actor divergence while simultaneously embracing and reacting to instances of positive divergence.In most organizations, significant investment is being made into the implementation of formal IT governance structures and processes despite little empirical evidence as to their effectiveness.By adopting and highlighting a behavioural-based perspective of IT governance effectiveness, we hope to encourage practitioners to move away from the strict normative view of IT governance towards an alternative conceptualization that accepts and accounts for the complex social and individual environments in which IT governance systems are enacted.From this perspective, we argue that IT governance effectiveness can be improved and IT investment failures can be reduced.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».