The Positive Impact of Social Responsibility (SR) Strategy on the Performance of SMSEs and Entrepreneurs in the North East Region of Nigeria
Bibliographic record
Abstract
This study, empirical assessment of the effects of social responsibility (SR) strategy on the performance of SMSE’s and possible improvement on entrepreneurs in the north east region of Nigeria was undertaken and found that social responsibility has made an impact on the operation, economic growth and survival of SMSE’s sub-sector in Yobe State of Nigeria. The study was built in SMSE’s sector of the region, the “Thermo Fisher’s 4i Values” (Integrity, Intensity, Innovation and Involvement) it made up the sector in questions a great interaction with customers, suppliers and partners, communities and each other. These four values are foundational to our SR approach (Thermo Fisher scientific, 2020). Incognizance of the importance of SMSE, this study has expanded the body of knowledge in respect to social responsibility of small and micro enterprises. Specifically, the study worked out means in which SMSE sector be encouraged profitably. Only few of SMSE’s do participate and recognized the activities of SR. The study also recognized that financial institutions like banks, individual financer and government are attracted to few SMSE that participated in SR (Basariya, Al Kake, 2019). The study realized that “Social Responsibility (SR) has become a fundamental way of defining the role of business in society” (Itziar & Josep, 2011). Other prominent writers in this context believe that, the needs of current and future generations cannot be met unless there is respect for natural systems and international standards protecting core social and environmental values. It is increasingly recognized the critical role of business sector. As a part of society, it is in business’ interest to contribute to addressing common problems. Strategically speaking, the study has captured that business can only flourish when the communities and ecosystems in which they operate are healthy (Marylyn, Caroline & Sheena, 2011., Judy & Victoria, 2011., Cameron, 2011., Tobias & Frank, 2011., Min-Dong, 2011, &Babafemi, 2015).
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".