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Record W2011133088 · doi:10.5430/ijba.v5n6p48

Competition as a Response Strategy to Globalization by Manufacturing Firms in Kenya

2014· article· en· W2011133088 on OpenAlexvenueno aff
Solomon Kinyanjui, Margaret Oloko, Hazel Gachunga, Beatrice G. Gathondu

Bibliographic record

VenueInternational Journal of Business Administration · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsGlobalizationKenyaCompetition (biology)Nonprobability samplingBusinessPopulationManufacturingSample (material)Industrial organizationMarketingEconomicsMarket economyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Globalization affects local and international firms in many ways. Studies have shown that factors in the internal as well as external environments of firms influence the rate to which globalization will affect them. On the local scene however, no known studies have been done on the response of Kenyan manufacturing firms to counter globalization. In addition, since the concept of globalization is multidimensional and its influence is varied in nature, this study aimed at investigating how manufacturing firms in Kenya have responded to probable pressure from the forces of globalization in order to sharpen their competitiveness. Cross sectional survey design was adopted for the study. The population for the study was the 735 manufacturing firms in Kenya. The target population of the study was CEOs/MDs and their deputies from 545 manufacturing companies in Nairobi and Athi River. Stratified sampling technique was used to categorize the targeted manufacturing firms into sectors where purposive sampling technique was used to sample the respondents for the study. A total of 100 firms from the 14 sectors were targeted by the study out of which 80 responded giving a response rate of 80%. Questionnaire was used to collect primary data. Regression and correlation analysis was done to test the relationship between the study variables. On the relationship between competition and globalization in manufacturing firms in Kenya, the study found that 60% of the respondents indicated to a large extent exploring other markets is a competitive strategy of responding to globalization, 55% of the respondents indicated that to a large extent increasing the range of products produced is a competitive strategy of responding to globalization, 40% of the respondents indicated that to a very large extent innovations are competitive strategies of responding to globalization. The findings from the correlation analysis showed that globalization has a positive relation with competition with a Pearson’s Correlation Coefficient of 0.558 and 0.021 level of coefficient. The null hypothesis that there is no significant relationship between globalization and competition was therefore rejected. The study concluded that manufacturing firms in Kenya have adopted competition as response strategies to globalization. The study recommended that manufacturing firms should reduce the direct cost such as energy while improving the market share.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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 designObservational
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

Citations2
Published2014
Admission routes1
Has abstractyes

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