Measurement of Perceived Importance and Urgency of Email: An Employees’ Perspective
Bibliographic record
Abstract
Abstract This article was motivated by the lack of research, and research instruments, informing academics and practitioners on the email factors used by knowledge workers when triaging their email. This article reports on the development and validation of two measures that categorize the types of emails employees send/receive into two different constructs based on the perceived importance and importance and urgency. The measures were developed using Buss and Craik’s Act Frequency Approach. Analysis determined that our six-item important email and our eight-item important and urgent email scales were both reliable and valid measures of the constructs. Construct validity was demonstrated by embedding our measures in a nomological network linking email demands to employee well-being. The measures were found to be significant predictors of work-role overload, even when the more traditional measures used to quantify the demands imposed on employees by email were taken into account. Lay Summary This article presents two scales that can be used to measure the extent to which an employee perceives an email to be: (a) important and (b) both important and urgent. As expected by theory, both measures were found to predict employees’ feelings of being overloaded by their work. The interesting findings from this study are the following. First, employees seem to consider urgent emails to be important, perhaps without consideration to the email’s actual importance to the employee. Second, emails that are both important and urgent usually involve a key stakeholder being impacted if the email is not acted on quickly. Third, important emails are not necessarily considered urgent. To make an email urgent the sender has to explicitly state how the contents of the email will negatively impact key stakeholders. Finally, our findings suggest that reducing the volume of email someone has to process may not, on its own, lead to reduced employee stress. Rather, employers who seek to improve employee well-being should focus their efforts on reducing the volume of emails employees consider to be important and both important and urgent.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".