Beyond Digital vs. IT: The Untold Story of Their Relationship from an Organizing Logic Perspective
Bibliographic record
Abstract
Organizations often feature digital and information technology (IT) units, suggesting that managers perceive digital technology as different from IT. However, these units do not coexist in silos; rather, they interact in pursuit of organizational goals. In this study, we investigate the interactions between the digital and IT units of three organizations undergoing digital transformation. We find that these interactions reflect three relationships with varying dynamics. The interplay of these dynamics shape organization’s digital transformation efforts. We outline three considerations for managing these dynamics productively. First, we outline conditions that determine the dynamic (e.g., synergistic or conflicting) more likely to manifest. These conditions depend on the (in)compatibility between the units’ need to interact and the strategic, routine or technology rationales they draw on. We caution against seeking compatibility at all costs. Rather, managers should consider the context of each interaction before deciding how to influence these dynamics. Second, we found that interpersonal relationships, hiring talent with diverse perspectives, or establishing idea exchange forums can help to foster productive collaborations. However, they alone do not determine these dynamics. Third, cross-unit relationships is important for accomplishing organizational goals like digital transformation. Managers should proactively nurture such relationships to foster collaboration among units.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".