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Record W4403971897 · doi:10.1093/cje/beae035

Technology rhetoric and institutional ownership

2024· article· en· W4403971897 on OpenAlex
Panayiotis C. Andreou, Kyriakos Drivas, Dennis Philip, Geoffrey Wood

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

VenueCambridge Journal of Economics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsWestern University
Fundersnot available
KeywordsRhetoricEconomicsPositive economicsNeoclassical economicsLaw and economicsPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Abstract This article compares actual R&D spend with the managerial rhetoric around technology and innovation contained within corporate disclosures of US-listed firms. We find that, whilst actual R&D spend and patents do not entice institutional investors to increase their stock holdings, firms that espouse technology and innovation in their corporate disclosures are quite successful in drawing in short-term investors. We frame this investor behaviour within the economics of expectation literature. While managers are incentivised to draw in capital, short-horizon investors are less likely to exert due diligence and are rather persuaded by a technology narrative—that is, a ‘gold rush’ effect. This explains our finding that when there is a sudden downturn with stock price crashes, short-term investors rush to withdraw their money from firms that ‘talk tech’. Our findings have implications for managerial rewards systems, especially when these encourage managerial hype.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.289

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 communication0.0000.001
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 head0.023
GPT teacher head0.219
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