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Record W4378620102 · doi:10.52403/ijrr.20230550

Influence of Grand Strategies on Performance of Manufacturing Firms in Nairobi County, Kenya

2023· article· en· W4378620102 on OpenAlexaboutno aff
Gerald Okoth, Julius Miroga

Bibliographic record

VenueInternational Journal of Research and Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDescriptive statisticsBusinessMarketingQuarter (Canadian coin)Sampling frameCredenceDescriptive researchManufacturingProduct (mathematics)Operations managementEconomicsPopulationGeographyStatistics

Abstract

fetched live from OpenAlex

Relationship between grand strategies and performance of manufacturing firms has gained credence globally. Manufacturing firms in Japan have recorded greater success in manufacturing sector globally due to their ability to adopt various grand strategies that have aided them to achieve sustained competitive advantage. However, manufacturing industry in Kenya has experienced decline over the last five years. Manufacturing sector GDP contribution in Kenya has reduced in 2022 first quarter to KES 118,134 from KES 113,460 million 2022 second quarter. Therefore, this study sought to examine the influence of grand strategies on performance of manufacturing firms in Nairobi County, Kenya. The specific objectives were to examine the influence of product development strategy on performance of manufacturing firms and to establish the influence of turnaround strategy on performance of manufacturing firms in Nairobi County, Kenya. The study was guided by Igor Ansoff’s theory, and Stage theory of successful turnaround. Descriptive research design was used in this study. One hundred (100) respondents from 20 large manufacturing firms in Nairobi County were targeted. The sampling frame comprised of marketing/sales managers, finance managers, human resource managers, operational managers, Strategy & Business Development Managers. The study sampled 100 using census sampling technique. Primary data was collected using a well-designed questionnaire. Quantitative data was analyzed using descriptive and inferential statistics. Descriptive analysis was summarized data in form of central tendency as well as dispersion and inferential analysis was used to test hypothesis at a significance level of 0.05. Descriptive analysis included; frequencies, Mean, Standard deviation and percentage while inferential analysis involved correlation analysis and multiple linear regression analysis. Prior to conducting multiple linear regressions, the study ensured that the assumptions of linear regression are met. The data was presented in form of tables and models. The results indicated that product development strategy had positive and significant effect on organizational performance. Turnaround strategy had a positive and significant effect on performance. On the other hand, the regression analysis revealed that the grand strategies explained up to 64.5% change in organizational performance of manufacturing firms in Nairobi County. The study concluded that grand strategies significantly influence organizational performance of manufacturing firms in Nairobi County. This study recommends that management of manufacturing firms pursuing product development strategies so as to come up with products that meet the changing needs of their customers. It is recommended that the companies should open new branches in new geographical areas to reach new customers not only beyond Nairobi County, but also beyond East Africa. The companies can expand their product lines by developing new products that may or may not be related to the current products to target current customers. The study recommends that management of manufacturing firms should create an organizational culture that is in line with their turnaround strategy. Keywords: [Organizational Performance, Product Development Strategy, Turnaround Strategy]

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.184

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 communication0.0000.001
Open science0.0000.000
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 head0.049
GPT teacher head0.340
Teacher spread0.291 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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