Purchasing Higher-Value, Higher-Price Offerings in Business Markets
Bibliographic record
Abstract
Purpose: A conceptual framework is proposed and tested to better understand customers' purchase of higher-value, higher-price offerings in business markets. Ambiguity about superior value and consequences of obtaining superior value are the constructs in this framework. Ambiguity about superior value is meant to capture the concern and doubt that managers at customer firms have about whether their business will actually realize the cost savings or ability to earn incremental revenue and profits that suppliers claim for their offerings. Consequences of obtaining superior value refers to the outcomes that a customer manager anticipates or experiences in making a purchase decision for higher-value, higher-price offerings. Methodology: Two operationalizations of each construct are studied in a pair of experiments with purchasing managers and plant maintenance managers. Findings: Value evidence and incentive to change each receive significant support as mechanisms to reduce ambiguity about superior value. Notably, reference customers and pilot programs appear to be equally effective as value evidence in reducing ambiguity about superior value. In addition, the results provide strong empirical support that incentive to change operates as a threshold phenomenon, as predicted from social judgment theory. While no significant differences in purchase preferences are found for area of responsibility (purchasing versus plant maintenance managers), significant support is found for performance review and reward system as a manipulation of consequences of obtaining superior value. Contribution: The conceptual framework and empirical results significantly contribute to our understanding of how suppliers in business markets can use monetary as well as nonmonetary means to persuade customers to purchase higher-value, yet higher-price offerings.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".