Exploring Digital Strategy as a Business Management and Transformation Tool in Developing Countries: The Nigerian Experience
Bibliographic record
Abstract
Despite valuable business opportunities and solutions that digital technologies offer, studies show that many business leaders have yet to explore the potential of digital solutions to manage and transform their businesses to capture more value. Through a semi-structured interview with business leaders and managers in Lagos, Nigeria, and content analysis, this paper explored how business leaders may leverage the potency of digital strategies to manage and transform business operations in developing nations like Nigeria to obtain more value. The result provided a distinctive insight into the dynamics around digital strategy adoption in business and the inclusive effects, evidencing that managing and transforming business operations in developing nations is achievable by adopting soft and hardware digital solutions at all levels of the business process. This paper advances the discussion on digital strategy adoption in business and recommends a paradigm shift of businesses in developing countries to a digitally evolved business construct to streamline processes, unlock new opportunities, and benefit from the potency of digital solutions. The paper underscores the government and other stakeholders' roles in driving digital strategy adoption in developing nations' businesses. The recommendation in this paper could be rewarding to influencing change in the process and management of business operations in developing nations, creating an opportunity for theoretical expansion and implementation of digital strategies for business management and transformation to achieve operational efficiency and sustainability objectives.
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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 | 0.003 | 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 | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".