Planning practices, strategy types and firm performance in the Arabian Gulf region
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate planning practices, strategy types, and the performance of indigenous firms in Bahrain and United Arab Emirates (UAE). Design/methodology/approach Data are collected from cheif executive officers (CEOs) and top management of 95 local companies sampled from Chamber of Commerce and Industry databases in Bahrain and UAE using face‐to‐face interviews. Analysis of variance and univariate logistic regression are employed in analyzing the data. Findings Although most of the firms are long‐term planners, many of them do not have a planning process. Majority of the firms are Prospectors and Analyzers. Prospectors perform considerably better than all the other strategy types. Nevertheless, the firms that are included in this paper appear to be cautious and not aggressive in entering new markets or in taking the lead in introducing and marketing new products. Research limitations/implications The paper suffers from selection bias by focusing on indigenous‐owned companies. Also, the data originate from self‐reported responses from business leaders and executives. The results do not establish causality. Finally, only broad demographic links are considered. Other individual and firm variables may influence performance in different ways than indicated here. Practical implications Managers must pay heed to the usefulness of planning and ensure that their companies have a planning process in place. Given the performance of Prospectors, managers must adopt some prospector strategies. Experience and high level of education as essential ingredients to successful planning and performance require management consideration. Originality/value The paper provides empirical support for Miles and Snow typology and corroborates the existing understanding that planning is beneficial to firms from an under‐researched part of the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".