MétaCan
Menu
Retour à la cohorte
Enregistrement W3090030095 · doi:10.17722/ijme.v14i1.1127

Corporate Strategy for Medium Scale Manufacturing Enterprises in Kenya

2019· article· en· W3090030095 sur OpenAlexvenueno aff
Evans Mwasiaji

Notice bibliographique

RevueInternational Journal of Management Excellence · 2019
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueInnovation and Socioeconomic Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLikert scaleBusinessUnit (ring theory)Strategic business unitSmall and medium-sized enterprisesScale (ratio)Strategic managementIndustrial organizationCompetitive advantageManufacturing sectorSustainable developmentMarketingEconomicsFinance

Résumé

récupéré en direct d'OpenAlex

Sustainable Development Goals and Africa Agenda 2063 acknowledges Small and Medium Enterprises as critical in promoting sustainable global economic development. However, most studies on corporate strategy in Kenya have mainly examined micro, small and large enterprises creating a missing middle with inadequate empirical data on medium scale enterprises, including those in the manufacturing sector. Moreover, Kenya’s big four agenda proposes support to the manufacturing sector so as to raise its GDP share to 15 percent by 2022 in support of the realization of Vision 2030. Unfortunately, growth in the manufacturing sector has stagnated at about USD 5 billion for over a decade and continues to lose market share and competitiveness internationally. This study therefore investigated corporate strategy and competitiveness of medium scale manufacturing enterprises in Kenya. Data was collected from 56 senior management staff. Mean responses received in a Likert scale of 1 – 5 for each of the tested item was calculated by summing up all the codes and getting the average of the 56 respondents. This study established MSMEs which are within the SME sector are on average performing below par on issues to do with business strategy. The results show that in 56.1% of the MSMEs, there is a clearly written business unit mission statement (mean response of 4.3). In 54.5% of the firms, the business unit strategy is not adequate in light of competitive pressure (mean response 2.5) and the business unit strategy is not appropriate for exploiting opportunities in the future. In 48.5% of the firms, the business unit strategy is not formulated carefully by all levels of management (mean response 2.7) and there is no clearly developed long term business unit strategy (mean response 2.9). In 39.4% of these firms, the business unit strategy does not adequately reflect the strengths of the business unit (mean response 2.8). The study concluded that lack of an effective business strategy to direct the efforts of human resources in the desired direction would result in inability to realize the set organizational objectives. This means these MSMEs are struggling to operate, manage and improve their businesses efficiency and effectiveness in order to deliver quality products and services consistently and on time. This has a negative effect on MSMEs performance as it implies internal inefficiencies, ineffectiveness and negative bottom line, reduced job opportunities and low contribution to the gross domestic product (GDP) in Kenya. The study recommended that the MSMEs should organise strategic focus workshops and use a combination of Porter’s five force model components to plan, organise and formulate their business strategy mechanism after a comprehensive SWOT analysis. The MSMEs should periodically review their strategy in line with the prevailing competitive pressures using the following criteria to identify crucial strategic issues: (a) The impact they could have on their enterprises, (b) the likelihood that the identified issues would materialize, and (c) the time frame over which they could develop. The number of these issues needs to be limited to a manageable number (three to nine) to enhance the chances of securing the commitment and resources necessary to effectively act on them. The expected study output would be enhanced competitiveness of MSME and realization of Kenya’s vision 2030.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,375
Score d'incertitude au seuil0,584

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,245
Écart entre enseignants0,223 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueInternational Journal of Management ExcellenceMême sujetInnovation and Socioeconomic DevelopmentTravaux en français237 207