Digital Self-publishing as Planned Behaviour: Authors' Views on E-book Adoption
Bibliographic record
Abstract
A popular school of thought in the study of publishing, exemplified by the influential Long Tail theory, suggests that the economic advantages of e-books will lead to a boom in self-publishing. However, this position focuses on economic factors at the expense of other potential influences. This thesis applied Azjen's (1991) Theory of Planned Behaviour to explore which factors have the most influence on authors' decision to self-publish e-books, and, conversely, which factors influence others' decision not to. Qualitative interviews were conducted with 11 authors in the Ottawa area who have self-published or who are considering doing so in the near future. We discovered that there is significant resistance to e-books as a format for self-publishing, and that normative factors such as a lack of prestige and different promotional requirements were particularly influential. While e-books were seen to reduce economic risk, they were believed to be a less prestigious format, and so to represent an elevated risk to what Bourdieu called symbolic-capital. Some authors were also resistant because they felt unable to promote e-books in the way they are expected to. However, most said they would be willing to abandon their resistance if they perceived sufficient demand from their audience. These results open up paths for future study, including more focused examinations of the resistance factors that emerged; more longitudinal studies to see how authors' opinions change over time, particularly those of the non-adopters; and a further examination of the digital skills developed by adopters.
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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".