Exploring IT Governance Effectiveness: Identifying Sources of Divergence through the Adoption of a Behavioural-Based Organizational Routines Perspective
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".