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Record W438468815

The experience of being a knowledge manager in a multinational : a practice perspective

2009· preprint· en· W438468815 on OpenAlexaboutno aff
Linh Chi Vo

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2009
Typepreprint
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationPsychologyKnowledge managementEmpirical researchTransactional analysisThematic analysisSociologyEpistemologyQualitative researchSocial psychologyComputer sciencePolitical scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

Inspired by the lack of research on the practices of knowledge managers in the literature and the existing dominance of the building mode in strategy-as-practice research, this thesis has two attempts. One is to examine how the knowledge managers, as strategizing practitioners, do their job via intentional doing and practical coping in their particular context. Another is to understand how knowledge managers, as strategizing practitioners, live in their position. To develop an investigation framework, this thesis relies on the pragmatic theory of John Dewey, especially his perspective on the transactional relationship between man and the environment. The empirical investigation is carried out based on the six inter-related procedures put forward by van Manen (1990), which are in line with the spirit of hermeneutic phenomenology. Seven knowledge managers of a multinational, who work in different countries, including France, Austria, China, and Canada, were interviewed during two sessions of two hours. The interviews are semi-structured. The interpretation process relies on the model proposed by Benner (1994), which performs cross-case thematic analysis to show the common threads between the stories of the knowledge managers, and studies the paradigm cases using withincase analysis to understand the individuality of each story. Exemplars are used to convey the different aspects of the themes and paradigm cases presented. Validity is enhanced by internal verification and testing of interpretive description, craftsmanship quality (Kvale, 1996), and a validation of findings by the participant. An “audit trail” (Koch, 1994) is kept to help the reader follow the rationale of the researcher’s interpretations. The analysis reveals that the main particularity of knowledge management is that it is a new function. The knowledge managers have to build a place for the knowledge management function through a strategizing process to gain acceptance. The knowledge managers can be compared with the rafters on an angry river, with the river indicating the organizational context, and the rafting representing the strategizing process. The knowledge managers, as the rafters, are carried on as well as endangered by the current. They fight to protect their inflatable boat from sinking by trying to avoid the obstacles and taking advantage of what is useful for them on the river. From the cross-case thematic analysis, the strategizing actions, undertaken by the knowledge managers, are grouped into three categories: intentional, emergent, and constraint-responding. They are labelled marketing, island-finding, and force-building, respectively. From the paradigm-case analysis, the knowledge managers are divided into three groups based on their capability to survive the turbulent organizational context and build a territory for the knowledge management function. Among the seven knowledge manager, one has decided to give up the position, four are still struggling with the obstacles and the powerful current to obtain a place within the organizational life, and two have achieved certain success. They are named the defeated, the struggling, and the contented, respectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0180.024
Scholarly communication0.0120.011
Open science0.0030.010
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.370
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2009
Admission routes1
Has abstractyes

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