You’ve got mail – whether you want it or not: An emic investigation into how email use can be managed
Bibliographic record
Abstract
Despite the best attempts of researchers and the tomes of advice in the consulting and grey literature, many continue to experience email overload and the volume of email employees manage is staggering. A troubling problem given that email overload and volume have been linked to negative wellbeing outcomes for employees. This paper reports on a qualitative study undertaken to help researchers and practitioners better understand email management from the point of view of email users. A sample of 30 knowledge workers were interviewed and asked to identify personal and workplace changes that could help them better manage their use of email. Fifteen informants worked in the public sector (education) while the other 15 worked in a private sector firm (insurance industry). The study took an interpretivist approach with content coding of the semi-structured interviews to develop sensitizing constructs. Analysis of the data uncovered a strong link between what users were telling us and some of the major tenants of attribution theory: locus of causality and stability. Most importantly, we found that most of the knowledge workers we spoke to felt that they could do little personally to manage their use of email. Rather, they felt email management was the responsibility of others (e.g., policies, training, technology). Responses were consistent with a self-serving attribution bias and consistent with the norms in place in organizations supporting an ideal worker culture. This study contributes to the literature in several ways. First, it shows most employees do not take responsibility for their email management problems. Implying that email management needs to be tackled at the organizational level. Second, it provides organizations and employees with practical advice on how they can start to address issues with email management. Third, our findings contribute to theoretical development in this area by exploring email management issues through an attribution theory lens. • Employees blame others and their organizations for their email problems. • As long as employees make a self-serving attribution bias, we cannot rely people to fix their email issues on their own. • As long as the culture and the organization reward after hours work, we will not be able to solve the email problem.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".