Bibliographic record
Abstract
The objective of a marketing strategy formulation and appropriate execution is to improve the long run financial performance of a firm / brand that includes improving market share, improving market capitalization, improving return of investment and payback period. The marketing strategist should take cognizance of the market forces that influences the payoffs to the firms. The influence of the competitive scenario on a firm / brand’s payoff is significant and could even at times be substantial in an oligopolistic industry-market structure, where strategic inter-firm dependence could be high. In this scenario, firms / brands need to be systematically prepared to find customer favor in a competitive market; this indeed is the realm of building competitive edge. In this study, we outline the path of improving ability to the process of marketing strategy formulation and examine in greater detail the process of improving the ability to compete or the buildup of competitive edge. A buildup of competitive edge is expected to improve the firm’s ability to competitive choice in the market place, through a process of improving the pro-activeness and reactiveness of the firm with respect to competitors to the satisfaction of (i) the consumers / target market, (ii) the market-society at large, as well as (iii) the policy makers regulating the competitive environment in the country / world. The improvement in the firm’s ability to competitive choice should reflect both in market share as well as market profitability and lead to improved market capitalization and superior long run financial performance.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".