Cultural roadblocks? Acceptance of blockchain-based hotel booking among individualistic and collectivistic travelers
Bibliographic record
Abstract
Purpose Blockchain technology is predicted to revolutionize the tourism and hospitality industry through peer-to-peer hotel bookings with little or no involvement of intermediaries. Outstanding features of this technology are its distributed form of storing data, its collaborative way of identifying the “true state” of a system and the immutability of data. These features may lead to a perceived loss of controllability among travelers. Based on the Agentic Theory of Human Behavior, the purpose of this study is to propose that this assumed loss of control matters more to travelers with an individualistic rather than a collectivistic predisposition. Design/methodology/approach In two studies (n = 475 and n = 196) using verbal scenarios, this study manipulates the perceived controllability of a blockchain-enabled hotel booking app by varying the number of additional services linked to the app. This study tests for the interaction of controllability with individual-level measures of individualistic versus collectivistic (I-C) predisposition. Findings Collectivistic travelers are more willing than individualistic travelers to use blockchain technology for their hotel bookings. This effect can be mitigated by offering additional services that give individualistic travelers an enhanced sense of “being in control”. Practical implications Blockchain-enabled applications facilitating direct hotel bookings without any additional intermediary services are more readily accepted by travelers with a collectivistic mindset. Blockchain applications addressing individualistic travelers require added services that establish a sense of controllability. Originality/value To the best of the authors’ knowledge, this paper is the first to investigate the interaction of I-C predisposition with perceived controllability in tourism and hospitality. Furthermore, it is the first in the technology-acceptance literature to test this interaction using individual-level measures of I-C predisposition and an experimental manipulation of perceived controllability.
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 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".