The influence of product features on brand switching: the case of magnetic resonance imaging equipment
Bibliographic record
Abstract
Purpose This paper seeks to provide evidence that the long‐term success of capital‐intensive technology products requires continuous integration of innovations in the form of new features and capabilities that meet broad user preferences. Design/methodology/approach Magnetic resonance imaging (MRI) research centers, which represent lead users in this industry, are used as a case study. An online survey was developed to identify and rank the main factors behind brand switching, then secondary sources are used to confirm the research results. Findings A multi‐faceted approach to data collection is used to show that product innovations in the form of specific features are the main motive for switching to a new technology, consistent with the expectation that lead users seek technologies that maintain leading‐edge positions. Research limitations/implications There are limitations to generalizing from this case study to other industries. The findings can be generalized to industries with similar characteristics, such as aircraft and heavy machinery manufacturing. In practice, managers should find a reliable strategy to assess factors underpinning brand switching that is unique to their industry. Determining the main factors behind switching is a critical matter when defining the appropriate strategy to keep their market share from eroding. Originality/value The literature reports considerable research that investigates brand switching. However, most of it focuses on highly competitive markets for consumer goods. This paper addresses a paucity of knowledge about what influences lead users of capital‐intensive products to switch between brands.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".