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Record W4390731671 · doi:10.1016/j.jik.2024.100462

Do corporate social responsibility and technological innovation get along? A systematic review and future research agenda

2024· review· en· W4390731671 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

VenueJournal of Innovation & Knowledge · 2024
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsCorporate social responsibilitySet (abstract data type)Work (physics)Public relationsPolitical scienceEngineering ethicsBusinessSociologyEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Corporate social responsibility (CSR) and technological innovation (TI) are two fundamental driving forces for sustainable development. The importance of the CSR-TI relationship has grown in prominence over recent years. This study is motivated by two research questions: (1) What makes the CSR-TI relationship conclusions different? (2) Which emerging themes in the literature will likely set the stage for future work? Using VOSviewer for co-occurrence analysis, this research examines 67 scholarly works in related research fields from 1996 to 2023 in 36 leading journals to answer these questions. This paper is a systematic literature review of the CSR-TI relationship from different perspectives. Addressing these two research questions helps us clarify the internal logic of the existing research in the CSR-TI relationship, deepens the understanding of the relationship between CSR and TI, and provides a glimpse of the future.

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)0.038
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0040.020
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.219
GPT teacher head0.429
Teacher spread0.210 · 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