MétaCan
Menu
Back to cohort
Record W4315754134 · doi:10.11124/jbies-22-00433

How “gutsy” does an organization have to be to absorb new information?

2023· editorial· en· W4315754134 on OpenAlexaffabout
Christina Godfrey, Andrea C. Tricco, Rosemary Wilson, Kim Sears

Bibliographic record

VenueJBI Evidence Synthesis · 2023
Typeeditorial
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsSt. Michael's HospitalQueen's University
Fundersnot available
KeywordsAbsorptive capacityKnowledge managementProcess (computing)Body of knowledgeBusinessComputer science

Abstract

fetched live from OpenAlex

Absorptive capacity was first defined in 1990 by Cohen and Levinthal as “an organization’s ability to identify, assimilate, and integrate new knowledge.”1(p.128) In 2002, Zahra and George2 expanded on this definition to emphasize the active process of transforming and exploiting that new knowledge.3 Their extended definition provided the basis for the 4 components of absorptive capacity: i) identify the new knowledge, ii) assimilate the knowledge, iii) integrate the knowledge into the existing knowledge base, and iv) use the new knowledge to change the organization in some way.3 The concept of absorptive capacity has its origins in biology4 and refers to the capacity of the gastrointestinal tract to absorb digested nutrients into the body. Somehow, this term leaked out of the gut and has been adopted by the business world where it is used to help understand and monitor an organization’s capacity to absorb new or innovative knowledge.1 In due course, the health care system also adopted this concept, hence the reason behind our review published in this issue of JBI Evidence Synthesis.3 In preparation for our scoping review, we engaged with all stakeholders and explored our understanding of the concept of absorptive capacity. Then, we examined the literature, seeking to establish how absorptive capacity is conceptualized and measured in the adoption of innovations in health care organizations. A scoping review methodology was deemed the most appropriate methodology to pursue this investigation, as it facilitated the breadth of inquiry required to inform both of these related issues. The review was conducted through the Strategy for Patient Oriented Research (SPOR) Evidence Alliance5 and was initiated by the Knowledge Translation Unit, Strategic Policy Branch of Health Canada. Colleagues from Health Canada were involved at multiple key points throughout the review.6 Most importantly, the entire team, including the Health Canada library scientist, Health Canada director, the Queen’s Collaboration for Health Care Quality research team, SPOR Evidence Alliance staff, and several graduate students, collaborated over multiple meetings to generate and refine the research question. Knowledge-user experts in the field of organizational change were also invited and included in the meeting discussions. Beyond refining the question, this integrated knowledge translation approach7 involved title and abstract screening, full-text review, and editorial contributions to the final report.6 None of the articles included in this review generated a new definition of absorptive capacity; rather, they investigated the influence of local health care contexts on this process. Each context required a different focus on the 4 components of absorptive capacity, and organizations drew attention to the factors that they found valuable to their particular process. It appears that organizations need to be more than just “gutsy” to absorb new information; expertise in each of the components of absorptive capacity is important to the success of this process, and understanding the contribution of context is essential. Measurement of the progress is crucial, and using the domains of absorptive capacity as a framework is beneficial in guiding this process of planning for and assessing the adoption of knowledge within the organization and any change that ensues as a result.

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)0.003
metaresearch head score (Gemma)0.796
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.796
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.053
GPT teacher head0.404
Teacher spread0.350 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJBI Evidence SynthesisSame topicHealth and Medical Research ImpactsFrench-language works237,207