Factors Influencing Electronic Business Technologies Adoptionand Use by Small and Medium Scale Enterprises (SMES) in aNigerian Municipality
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".