Advanced services and differentiation advantage: an empirical investigation
Bibliographic record
Abstract
Purpose This study theoretically articulates and empirically validates a model of relationships between market complexity (competition intensity, heterogeneity and technological change), strategic focus on product and service differentiation, ADS offerings and differentiation advantage. Design/methodology/approach The authors develop and test hypotheses through structural equation modeling based on data from the Sixth International Manufacturing Strategy Survey (IMSS-VI), involving 931 manufacturers from 22 countries. Findings The results indicate that (1) market complexity has a positive impact on strategic focus on product and service differentiation; (2) focus on product and service differentiation, but not market complexity, has a positive impact on the extent to which business units offer ADS to their customers; (3) ADS have a positive impact on service differentiation advantage, but no influence on product differentiation advantage. Practical implications Managers should incorporate decisions related to ADS provision as part of their manufacturing strategy formulation processes to align markets, strategic focus on product and service differentiation, and ADS provision. ADS seem an appropriate lever for market differentiation, because they appear not only to support service differentiation advantage, but also to be consistent with strategic focus on product differentiation. Originality/value The study provides novel insights and large-scale empirical evidence on the influence of the market environment on the offering of ADS, as well as on how relationships between the product and service activity in the manufacturing organization may affect differentiation advantage.
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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 | 0.000 | 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 | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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".