Factors Affecting the Adoption of Ict on Project Planning in the Nigerian Food and Beverage Industry
Bibliographic record
Abstract
This study identified the nature of Information and Communication Technology (ICT) adopted on project planning activities and examined the factors affecting the adoption of these ICT on project planning in the Food and Beverage Industry in Nigeria. The study was carried out through the use of questionnaire and interview schedule to a total of forty five (45) respondents across ICT, Production and Project departments of food and beverage firms in southwestern Nigeria. This was used to elicit information on the factors affecting the adoption of ICT in the industry. Data collected were analysed using both descriptive and inferential statistics. The study revealed that the major ICT adopted by food and beverage firms in Nigeria were Enterprise resource planning (4.48), Product lifecycle management (4.29), Customer Relationship Management (4.19), Supply Chain Management (4.34), Management Information Systems (4.38), Portable Data Collection Hand Held (4.65), Virtual Private Networks (4.53), Internet and e-mail (4.77). All of these ICTs had a mean rank of 4.00 and above on a 5 point-likert scales.Three factors were identified to influence ICT adoption. These include Human Resource capacity (52.8%), level of ICT investment (47.2%) and ICT competency (69.4%). Furthermore, regression analysis showed that level of ICT on Investment (r = -.425**: p<0.05) and Employee Competency (r = -.634**: p<0.05) are factors which had significant influence on ICT adoption in the project planning activities of the firms. In conclusion, the study revealed that Level of ICT investment and employee ICT competency are factors that significantly influence the adoption of ICT in the selected firms. These factors were found to be critical hindrances to ICT adoption in Nigerian food and beverage industry.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".