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Record W4241507076 · doi:10.1080/09585192.2012.671507

Knowledge appropriation and HRM: the MNC experience in Tanzania

2012· article· en· W4241507076 on OpenAlexaff
Ken Kamoche, Aloysius Newenham‐Kahindi

Bibliographic record

VenueThe International Journal of Human Resource Management · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMultinational corporationAppropriationBusinessMicrofoundationsHuman resource managementTanzaniaOrganizational cultureEthnocentrismHuman resourcesPublic relationsKnowledge managementSociologyManagementPolitical scienceEconomicsFinance

Abstract

fetched live from OpenAlex

Abstract This paper takes a critical look at the processes of knowledge appropriation through the management of human resources (HRM) and culture by multinational companies (MNCs) in a developing nation, Tanzania. We compare the approaches of two global MNC banks, Citibank, an American bank, and Standard Bank, a South African bank, and examine how each aligns its HR practices and policies with its conception of corporate culture with a view to strengthening its ability to secure, i.e. appropriate the contribution from its human resources. Both banks claim to follow a transnational model of ‘best practices’, by relying on proven global capabilities to manage people. However, we found evidence of a complex set of approaches in the way each bank developed its organizational and HRM practices and legitimized them through ethnocentric values. Using Foucault's social theory, we examine the way MNCs' knowledge appropriation regimes emerge and become rationalized at the organizational level. The notion of normalization, in particular, enables us to examine the application of organizational controls and the attendant implication about the protections available to employees in the Tanzanian banking sector. Keywords: HRMknowledge appropriationMNC banksnormalizationTanzania

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)0.001
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 score0.855
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.282
Teacher spread0.248 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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