Strategic Planning in Small and Medium Enterprises (SMEs): A Case Study of Botswana SMEs
Bibliographic record
Abstract
Although small and medium enterprises contribute immensely to the economy of a country, they are characterised by low performance and high failure rate which is often blamed on lack of resources such as funds, land and skilled labour. Many business management specialists argue that even on the availability of such resources, some SMEs still fail due to lack of strategic planning. Extent literature indicates that formal strategic planning improves business performance as it involves deriving a game plan that enables SMEs to anticipate and respond to the turbulent market by arranging their resources and capabilities accordingly. As such, this research investigates the status of strategic planning by SMEs in Botswana. The study also investigates the perceived value of Strategic Planning by SME owner managers, and the extent of planning as well as identifying the barriers that prevent effective strategic planning. Using semi-structured interviews of 36 Small and Medium firms selected across several sectors, the study finds that strategic planning efforts do exist within SMEs but most of these firms engage in strategic planning activities to a limited extent. The study also finds several barriers, which contribute to lack of strategic planning. For instance, the study finds that most SME owner/managers have limited knowledge in the area of strategic planning. Some indicated that they do not plan because of the size of the business. Whereas some admitted that they still possess the traditional based thinking where most business decisions are based on intuition. The findings of this study have implications for policy decision makers and SME owner managers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".