Brand switching of high-technology capital products: how product features dictate the switching decision
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the factors that underpin brand switching of medical imaging products by mass-market users. Most of the literature on brand switching is focused on competitive market products, for which switching costs are manageable. However, little consideration is given to brand switching of high-technology capital products. Design/methodology/approach – The conceptual model is developed based on the existing literature on B2B brand switching. An online survey was developed and distributed to decision makers involved in purchasing medical imaging technology. Findings – The results confirm the expectation that product features is the most influential factor underpinning brand switching. Product features are critical for medical organizations who want to maintain their competitive advantage. The findings suggest that the set of factors that influence the decision to switch is unique for users of different market segments in the same industry (e.g. lead users and mass-market users). This difference stems from technology utilization of each market segment. Research limitations/implications – In high-technology markets, managers should develop a reliable strategy to evaluate the antecedents behind brand switching that are specific to their industry. Knowledge of the major factors that cause users to switch is essential to allow firms to determine the strategy needed to prevent the erosion of their market share. Originality/value – Although the literature reports considerable research on brand switching, this study is a first-of-its-kind in that it demonstrates that the factors underpinning brand switching vary within the same industry, based on the characteristics of each market segment. This paper develops new knowledge on the factors that influence the decision of users of high-technology capital products to switch between brands to renew or improve their internal capabilities.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".