MétaCan
Menu
Back to cohort
Record W4406022550 · doi:10.1504/ijtm.2025.143583

(How) does digital transformation promote boundary-spanning strategies Evidence from Chinese firms' unrelated diversification

2025· article· en· W4406022550 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

VenueInternational Journal of Technology Management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBoundary spanningDiversification (marketing strategy)BusinessIndustrial organizationTransformation (genetics)MarketingKnowledge managementComputer science

Abstract

fetched live from OpenAlex

The emergence of new generations of digital technologies has presented firms with important strategic opportunities at the corporate level. This study investigates the digital transformation - unrelated diversification link and theorises the role of industry shakeout and the performance expectation gap in said relationship. Our analysis based on the data of China's A-share listed manufacturing firms from 2015 to 2020 shows that: 1) the degree of firms' digital transformation is positively correlated to the degree of their unrelated diversification; 2) industry shakeout positively moderates the above relationship, i.e., in industries with a higher degree of shakeout, the positive digital transformation-unrelated diversification link is more pronounced; 3) the performance expectation gap negatively moderates the digital transformation-unrelated diversification link, i.e., the greater the performance expectation gap, the weaker the positive correlation between firms' digital transformation and their unrelated diversification.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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