Assessing digital capabilities for digital transformation—The <scp>MIND</scp> framework
Bibliographic record
Abstract
Abstract With the rise in the advances and adoption of digital technologies and evolving business dynamics, we live in an era where many organisations are embarking on digital transformation. To stay relevant, however, organisations struggle to comprehensively outline the digital capabilities they have or need in relation to the digital transformation objectives they aim for. This struggle stems from the paucity of knowledge and practical guidance on how to assess the digital capabilities of organisations relative to their desired digital transformation goals. This paper presents a framework (MIND Framework) for assessing digital capabilities in four critical areas – Management (M), Infrastructure (I), Networking/Sourcing (N), and Development (D) – abstracted from prior literature. The framework assesses digital capability status in each area in relation to the organisation's stated digital transformation goals. MIND, which is an outcome of a multi‐year design science research project, helps organisations assess their current capability status and create a pathway for navigating from their current status to the desired transformation state. In this article, we describe an in‐depth application of the MIND framework in assessing the digital capabilities of an incumbent company in the digital transformation process. Based on this, we illustrate how the framework can provide valuable insights and attitudinal shifts in an organisation's digital transformation efforts. We further abstract from the case to demonstrate how the assessment of an organisation's digital capabilities can provide valuable insights and critical input for any organisation embarking on a digital transformation journey. We conclude with a detailed guideline on how organisations can apply the MIND framework in their transformation journey.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".