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Record W2522589710 · doi:10.1111/poms.12644

Emergent Themes in the Interface Between Economics of Information Systems and Management of Technology

2016· article· en· W2522589710 on OpenAlexafffund
Sulin Ba, Barrie R. Nault

Bibliographic record

VenueProduction and Operations Management · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsProductivityKnowledge managementPerspective (graphical)Information technologySupply chain managementInformation systemManagement information systemsInformation managementProduct (mathematics)Productivity paradoxComputer scienceBusinessSupply chainMarketingEconomicsEngineering

Abstract

fetched live from OpenAlex

In this article, we look at research published over a five‐year time span in the economics of information systems (IS) area in four premier journals, including Management Science, Information Systems Research, MIS Quarterly, and Production and Operations Management, to identify research themes that have implications for future research in the area of Management of Technology (MOT). Through our examination of the literature, we identify three emergent themes that can be used to form foundations for future MOT research from an economics of IS perspective: productivity, vertical relations, and platforms. Within each of these themes, we classify previous research into subthemes, summarize the major findings, and explore future research opportunities within the MOT domain that are relevant to these subthemes. Specifically, we examine how information technology has impacted firm productivity, their product design and development process, innovation capabilities, knowledge management capabilities, and supply chain integration.

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.

How this classification was reachedexpand

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 score0.815
Threshold uncertainty score0.187

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 communication0.0000.002
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 head0.014
GPT teacher head0.213
Teacher spread0.198 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2016
Admission routes2
Has abstractyes

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