Evaluating Self-Efficacy Pertaining to Cybersecurity for Small Businesses
Bibliographic record
Abstract
Small businesses are easy victims of cyberattacks due to their limited resources and insufficient training. Furthermore, many small business owners’ attitudes diminish their need for safeguards because they think that they are not likely to be attacked. Yet, small businesses experience Denial of Service (DoS) attacks, Distributed Denial of Service (DDos) attacks, phishing, vishing, and tail gating as well as theft of confidential information and hardware. Consequently, numerous small businesses close or experience detrimental results -- loss of consumer trust, lawsuits, credit monitoring fees, tarnished reputations, and lost operational costs. Since past research demonstrated that training positively impacts self-efficacy, this paper explores the effects of cybersecurity training on participants’ self-efficacy towards small business cybersecurity practices. Survey participants were face-to-face and virtual attendees at a public university’s Cybersecurity for Small Businesses Conference. To evaluate the attendees’ perceived self- efficacy, a pre-and post-survey included cybersecurity questions with demographic questions. The results show a significant difference in scores for overall cybersecurity self-efficacy before and after such training.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".