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Record W1973816997 · doi:10.4018/jkm.2010070101

Critical Success Factors and Outcomes of Market Knowledge Management

2010· article· en· W1973816997 on OpenAlex
Subramanian Sivaramakrishnan, Marjorie Delbaere, Zhengwen Zhang, Edward R. Bruning

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Knowledge Management · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of SaskatchewanUniversity of Manitoba
Fundersnot available
KeywordsBusinessCompetitor analysisCritical success factorKnowledge managementMarketingMarket orientationOutcome (game theory)Industrial organizationEconomicsComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

In this paper, the authors examine critical success factors and outcomes of market knowledge management, which is the management of knowledge pertaining to a firm’s customers, competitors, and suppliers. Using data collected from 307 managers in 105 businesses across Canada, the authors show that a firm’s extent of information technology adoption, its analytical capabilities, and market orientation are critical success factors for the firm’s market knowledge management. An important outcome of market knowledge management is the organization’s financial performance, mediated by customer satisfaction and customer loyalty. Results of this study indicate that superior business performance depends not only on the effective management of knowledge, but also on what type of knowledge is managed. Finally, implications of results and avenues for future research are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.296
Teacher spread0.282 · 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