Resource-based Commitment to a Customer-centered Strategy
Bibliographic record
Abstract
Abstract To create and sustain a resource-based competitive advantage, managers acquire and develop specialized resources as they grow their firms. The authors argue that an important part of committing to a resource-based strategy is a willingness to keep spending on specialized resources during periods when sales and profits are down. The authors seek to validate this conjecture by examining whether such resource-based commitment to a customer-centered strategy results in improved customer satisfaction. The authors use the stickiness of selling, general, and administrative (SG&A) expenses to capture this commitment empirically. The authors first document that future customer satisfaction is positively associated with SG&A cost stickiness, consistent with the premise that the retention of specialized SG&A resources during low demand periods helps firms to build and maintain relationships with customers over time. Next, the authors test whether expected future benefits of customer satisfaction are enhanced when SG&A cost stickiness is higher. The authors find that the positive relation between Tobin’s Q and customer satisfaction is positively moderated by SG&A cost stickiness. Finally, the authors test whether earnings persistence, a quality of earnings associated with sustained performance over time, is positively associated with the interaction between customer satisfaction and SG&A cost stickiness. The authors find that it is. Their evidence supporting these predictions is consistent with the conjecture that resource-based commitment reflected in cost stickiness is an important dimension of creating and sustaining a resource-based competitive advantage.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".