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Record W1985163539 · doi:10.1287/isre.1090.0229

<b>Research Note</b>—Does Technological Progress Alter the Nature of Information Technology as a Production Input? New Evidence and New Results

2009· article· en· W1985163539 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

VenueInformation Systems Research · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElasticity of substitutionEconomicsRentingSubstitution (logic)Production (economics)MicroeconomicsIndustrial organizationInformation technologyThe InternetCapital (architecture)MarketingEconometricsBusinessComputer science

Abstract

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Prior research at the firm level finds information technology (IT) to be a net substitute for both labor and non-IT capital inputs. However, it is unclear whether these results hold, given recent IT innovations and continued price declines. In this study we extend prior research to examine whether these input relationships have evolved over time. First, we introduce new price indexes to account for varying technological progress across different types of IT hardware. Second, we use the rental price methodology to measure capital in terms of the flow of services provided. Finally, we use hedonic methods to extend our IT measures to 1998, enabling analysis spanning the emergence of the Internet. Analyzing approximately 9,800 observations from over 800 Fortune 1,000 firms for the years 1987–1998, we find firm demand for IT to be elastic for decentralized IT and inelastic for centralized IT. Moreover, Allen Elasticity of Substitution estimates confirm that through labor substitution, the increasing factor share of IT comes at the expense of labor. Last, we identify a complementary relationship between IT and ordinary capital, suggesting an evolution in this relationship as firms have shifted to more decentralized organizational forms. We discuss these results in terms of prior research, suggest areas of future research, and discuss managerial implications. *This paper is dedicated to the memory of Paul Chwelos, respected colleague and dear friend.

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)0.010
metaresearch head score (Gemma)0.007
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 score0.635
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.087
GPT teacher head0.351
Teacher spread0.263 · 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