Decision maker satisfaction in a web analytics context: the impact of analysts’ skills
Bibliographic record
Abstract
This quantitative research targets decision makers, who rely on the analysis of web data (web analytics) in order to make strategic decisions. As identified in the literature, important factors in a web analytics context are information quality, human factors and the presence of actionable insights. Thus, the research had two objectives. The first objective was to provide a better understanding of the skill(s) that matter(s) the most when hiring, training, or working with a web analyst, with the objective of obtaining actionable insights from the reports the team of web analysts prepares. The second objective was to propose a model – based on the DeLone and McLean’s (2002) Information Systems (IS) success model – that predicts web analytics success on the basis of decision maker satisfaction, which in turn depends on information quality, and business, analytical and technical skills of a team of web analysts. The results obtained from this study reveal that web data analysis expertise, and the ability to provide agreed upon practical insights are key skills that have a significant impact on decision maker satisfaction. These findings are beneficial to several stakeholders, such as academia, researchers, and the business community, as they provide empirical evidence that may help develop curriculum, provide directions for future research, and determine what profiles to target when hiring or training web analysts. Furthermore, these results provide evidence of the reliability of the proposed research model, and the applicability of the DeLone et al.’s (2002) IS success model to the WA context.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".