Headquarters of the future: The impact of digitalization on headquarters structures and value added
Bibliographic record
Abstract
In this report, we investigate how the digital transformation impacts headquarters (HQs). Drawing on 85 survey responses from corporate and divisional HQ managers in Austria, we find that the digital transformation is expected to fundamentally change the HQ of the future in terms of how the HQ will derive decisions, how it will interact with its subunits, and how the HQ will add value to the firm. The majority of firms see the digital transformation primarily as an opportunity to increase the value-added that the HQ generates for its subunits as opposed to increased cost efficiency. Additionally, the responding HQ managers foresee a much more influential role of HQs vis-à-vis its subunits, based on better and more timely information, as well as more room for strategic thinking. Finally, our results suggest that many firms need to put more effort into digitalizing their HQ. Only 26% of the responding firms seem to have developed a clear idea what the digital transformation means for their HQ. These advanced firms see the highest potential in digitalizing the HQ. To this end, the biggest barrier to stepping up the digital transformation of the HQ seems to be the lack of digital talent.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".