Factors Influencing Electronic Business Technologies Adoptionand Use by Small and Medium Scale Enterprises (SMES) in aNigerian Municipality
Notice bibliographique
Résumé
This study examined the adoption of e-business technologies by SMEs in Ibadan a metropolitan city in South West Nigeria. It aimed at finding out the factors that promote and inhibit the adoption of e-business technologies, the kinds of e-business technologies adopted and used and their extent of use. It also identified the challenges faced by SMEs with regard to e-business technologies use. Descriptive survey research design was adopted. Data were collected with structured questionnaires administered among sixty SMEs (30 adopters and 30 non-adopters). Four hypotheses were tested at 0.05 level of significance. Data were analyzed using frequency and percentage distributions, t-test and multiple regression. Results showed that majority of the firms were smaller firms with 0-9 employees and not less than 1-5 years of establishment. The respondents cited perceived benefits as the major factor for adopting e-business technologies in their firms while 83.4% of non-adopters agreed that low capital base was the major reason inhibiting them from adoption. Hundred percent of the firms each have adopted internet technology and electronic mail which are daily used by all the firms. The major service provided with the use of these technologies was e-mail to communicate with customers and suppliers. On the benefit and challenges of e-business, all the organizations affirmed that e-business have benefited them in the sharing and exchange of information and improving market share. About 96.7% of them affirmed inadequate technical manpower as the major challenge. Further results revealed that the age of the SMEs had significant relationship on the adoption of e-business while size had no significant relationship. Independent variables jointly correlated significantly with the adoption of electronic business (R=0.162) and they contributed (22%) to the variance of the dependent variables. Their significant contributions were as follows: perceived benefit (β=0.568, p<0.05), nature of organization’s business (β=0.533, p<0.05); owner’s awareness of the technology (β=-0.577, p<0.05); and (β=0.725, p<0.05) while other variables were not significant. The results clearly indicate the necessity to provide support to SMEs to enable them to successfully adopt and use e-business technologies. The results have implications not only for managers of SMEs but also for government bodies in developing countries such as Nigeria.
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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