Emerging Technologies, Globalization and Strategic Business Management: Exploring the Trilogy for Developing Countries
Bibliographic record
Abstract
In this paper, we explored the trilogy of emerging technology, globalization, and strategic management with a focus on developing countries. The quantitative study deployed the quasi-experimental research design whereby a cross-sectional survey was conducted to collect data from respondents at a single point in time. The Population consists of individuals who are Nigerians living either in Nigeria or in the Diaspora. With a sample size of 385, the population size was determined using the z-score at 95% confidence level and a choice response of 50%. We adopted the conceptual framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), a variant of The Technology Acceptance Model (TAM), in this study.This study is significant because of the need to understand the emerging trends in technology adoption and the role of globalization against a backdrop of a given economic environment. A cursory appraisal of the environment will show that strategic business management may, or may not, have played a role in moderating the patterns in the adoption of emerging technology. Exploring the trends, trilogy, and country conditions (for a developing economy) are the springboards for this study. Among others, the study found that females use emerging technologies more than males with clear gender differences in the salience of various factors that determine an individual’s technology adoption decisions in the workplace. Furthermore, it was observed that strategic management in the form of regulations and policies or incentives, can affect the impact of technology diffusion within an economy. Finally, the study recommends an expansion of the study to include topical aspect of emerging technology like Internet of Things, Artificial Intelligence and 5G devices. Keywords: Emerging technologies, globalization, strategic business management, developing countries DOI: 10.7176/EJBM/13-13-01 Publication date: July 31 st 2021
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".