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Headquarters of the future: The impact of digitalization on headquarters structures and value added

2019· article· en· W2950132006 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWU Research · 2019
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsBerger (Canada)
Fundersnot available
KeywordsValue (mathematics)BusinessIndustrial organizationPolitical scienceOperations managementEconomicsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.138

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.298
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it