Scale development for the inter-firm market orientation concept
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop and validate a scale for inter-firm market orientation (IMO) based on an original conceptualization by Elg (2008). Building on the MARKOR scale, the IMO scale is introduced to better understand the market orientation efforts that occur within business relationships. Design/methodology/approach After establishing a conceptualization of IMO, an initial list of scale items is developed by adapting the original MARKOR scale. Several phases of qualitative pre-tests have been conducted with both academic experts and several manufacturer and reseller partner executives to assess the applicability and clarity of the measurement instrument. Using a quantitative survey design, the measurement instrument has been validated by relationship partner managers from both manufacturer and reseller companies. Findings The results of the analysis reveal that IMO is a second-order formative construct consisting of two first-order reflective constructs termed joint intelligence cooperation and joint customer responsiveness. Practical implications The operationalization of IMO suggests to manufacturers and their partners that the market intelligence cooperation efforts between them should be more focused on intelligence about the end users and less on general market trends. Further, the customer responsiveness efforts between the partners tend to be more reactive in nature, unlike the proactive stance in intra-firm market orientation. Originality/value The paper extends the notion of focal firms’ market orientation to IMO, which exists between partners in business relationships, and does so by developing a conceptualization and measurement instrument for IMO. This newly developed construct and scale can be used in future research to explore in greater depth the interplay between IMO and firm performance.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".