Digital “x”—Charting a Path for Digital-Themed Research
Bibliographic record
Abstract
We live in a time when digital technologies reshape most aspects of business and social life. This challenges received assumptions about modes of operation in organizations. As a result, scholars and practitioners increasingly use the label “digital” to signify that something has changed to the extent that a plethora of long-established management concepts are expressed in a new formulaic form of “digital x,” and x can stand for innovation, strategy, transformation, infrastructure, etc. In the information systems discipline and beyond, “digital” has emerged as an oft-used conceptual label to characterize age-long phenomena hitherto described by the IT (or x) label. There is a sense among academic and practitioner communities that digital and IT are not mere synonyms, but beyond the hype, something fundamentally different is being signaled when the “digital” label is invoked. This paper traces the intellectual roots and foundations of the growing use of “digital” as a conceptual label, identifies when the label use is warranted as well as outlines implications that the moniker holds for future scholarship, policy, and practice. In particular, the paper offers actionable guidance that enables more reflective use of the term “digital” as we move forward.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.076 |
| Scholarly communication | 0.030 | 0.062 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".