Market Mavericks in Emerging Economies: Redefining Sales Velocity and Profit Surge in Today’s Dynamic Business Environment
Bibliographic record
Abstract
This research aims to explore market mavericks by redefining sales velocity and profit surge in today’s dynamic business environment in emerging economies. The study focuses on the interplay between Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM). Data from 180 companies (2021–2023), provided by financial leaders, were analyzed using SPSS (23.0) and AMOS (23.0) software. The analysis employed exploratory factor analysis (EFA), reliability analysis, and confirmatory factor analysis (CFA). The results highlight the critical role of these factors in shaping market mavericks and their significant impact on sales and profits in emerging economies. Specifically, SE enhances sales and profits when supported by effective strategies, SC drives organizational change by aligning service quality with SE, and MA drives sales velocity and profit surges through accurate forecasting. SR positively influences sales results by aligning sales with corporate strategy, while DSM is critical for motivating salespeople and shows strong links to SC and SR for successful adaptation in a dynamic business environment. The study reveals the interdependence of these factors and emphasizes the need for seamless integration and coordination to drive effective organizational change. These findings have significant implications for corporations seeking to improve their sales strategies and achieve sustainable growth in a rapidly evolving marketplace in emerging economies. This research explores market mavericks, redefines sales velocity and profit surge, and provides valuable insights into the critical factors shaping market mavericks and their impact on sales and profits. It offers guidance for organizations seeking sustainable growth.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".