Bibliographic record
Abstract
Most research on the consumer journey has focused on product purchases. This research, however, examines the consumer journey for experiences – in this case a long-distance hike. Two studies examine how people become aware of, learn more about, prepare for, evaluate, and reflect on a 200-mile hike on the John Muir Trail in California. The first study interviewed hikers while on the trail. The second study analyzed online discussion groups dedicated to the trail. These studies reveal that the idea of a long-distance hike often arises from a mention by others where people add the idea to a mental “bucket list”. For those with whom the idea resonates, they may quickly decide to hike the trail, though their motivation may not always be clear, and the trip may not occur for quite some time. Getting a permit is frequently a barrier, but online communities often offer advice. Preparation for the hike is typically a high-involvement process that involves purchasing equipment and physical training. The hike itself is a paradox of aesthetic appreciation in the face of physical struggle. Importantly, many hikers later report the journey as an important experience and serve as evangelists for other hikers. The two studies corroborate certain notions about the consumer decision process while calling others into question. For instance, they validate the significance of word-of-mouth recommendations from individuals with similar backgrounds, which plays a pivotal role in raising awareness. Simultaneously, they challenge conventional thinking by demonstrating that the decision-making process is influenced not solely by rational factors but also by emotional elements. Finally, these findings underscore the importance of employing multiple research methods to comprehensively grasp the intricate aspects of the consumer journey, especially the consumption of experiences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".