Exploring Customer Resistance to Travel Subscriptions: The Case of Passive and Active Innovation Resistance
Bibliographic record
Abstract
ABSTRACT Drawing upon innovation active and passive resistance, this study explores consumers' resistance to travel subscriptions following a constructivist tradition. This qualitative inquiry begins with a hybrid approach to deductively and inductively analyze social media content before the inductive analysis of 15 semi‐structured interviews. Findings reveal that trip constraints trigger situational passive resistance. Traveler planfulness fosters cognitive passive resistance, while subscription‐specific barriers lead to active resistance. This study contributes to innovation resistance decisions by illustrating how situational passive resistance arises before cognitive resistance. Here, situational passive resistance leads to the passive rejection of adoption. This study also demonstrates that passive resistance cannot be mitigated when deciding on travel subscriptions, challenging the view that passive resistance must be overcome to introduce innovations successfully. An Adoption Hurdle Funnel is developed to depict the sequential order of barriers in the innovation‐resistance process and provide insights for service providers applying subscription models within travel products.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".