MétaCan
Menu
Back to cohort
Record W4407638277 · doi:10.1109/tem.2025.3543143

Organizational Strategy and IT Workforce During Times of Environmental Turbulence

2025· article· en· W4407638277 on OpenAlex
Alexander Serenko, Jaideep Ghosh, Prashant Palvia, Tim Jacks

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

VenueIEEE Transactions on Engineering Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTurbulenceWorkforceBusinessEnvironmental sciencePhysicsMechanicsEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate how organizations may improve their agility in order to respond to environmental turbulence by developing an appropriate organizational strategy and adapting their information technology (IT) function. It analyzes a dataset containing responses from 10 386 IT professionals located in 37 countries, which was collected as part of the World IT Project. The findings show that to become agile and prosper in a turbulent environment, organizations should both innovate and differentiate themselves from their competition: in other words, they should become Prospectors and employ a differentiation strategy. They also need to invest heavily in their IT human capital by way of increasing their IT personnel and establishing effective collaboration between non-IT and IT workers. Such steps would ensure the maximization of IT resources and facilitate efficient business-IT alignment. However, on the flip side, navigating a turbulent business environment can take its toll on employees who experience work exhaustion and become less satisfied with their jobs.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.621

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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