Customer Value Over price in the Battle for More Market share -a case study of Alfa Laval India Limited
Bibliographic record
Abstract
To optimize performance and create competitive advantage, which ultimately leads to greater market share, firm knowledge and understanding of where customer value resides is of upmost importance. Theory indicates that price is a significant factor affecting purchasing decisions but is considered to be of lesser importance for long-term commitments between firms where other factors of customer value play a greater role. This knowledge of customer value can be utilized in order to draft persuasive value propositions to retain and satisfy existing customers, set appropriate value-based price levels, and acquire new customers by developing new offerings that attract buyers.\nThe objective of the study is to research methods by which an MNC in an emerging market can capture more of the expanding domestic Indian food processing technology market share without competing on price. A case study approach involving field research in the dynamically emerging market of India is credited for confirming the authors‟ suspicions that price, although very important, is secondary to alternative factors of value when making a purchasing decision. Through the investigation of employees and customers of the case company Alfa Laval India Limited., as well as industry consultants, the authors have managed to isolate factors upon which an MNC in an emerging market can leverage its values and enhance its operations. Ultimately, this study allows for the authors to provide recommendations on how to increase market share in an emerging market by means other than competing on price, such as continuous innovation, providing total solutions, effective market communication, organizing the organization for valuable after-sales service commitment as well as other factors of value.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".