How readable are mission statements? An exploratory study
Bibliographic record
Abstract
Purpose Mission statements are fairly ubiquitous, particularly among large organizations. However, if they are to have a chance of achieving the desired positive outcomes, they must first be readable and comprehensible to the targeted stakeholders. The purpose of this paper is to investigate this issue, to answer the question of whether the mission statements of large companies are readable or not. Design/methodology/approach Content analysis and appropriate scores were employed to investigate the readability of the mission statements collected from a random sample of 100 firms in the Fortune 500 annual rankings. Findings The results indicate that on average, the mission statements of the studied companies are not that readable, and that in the case of many of them, the mission statements assume the readings skills of a university graduate. Research limitations/implications The results of this paper suggest that if the target audience of a mission statement is broad, and includes stakeholders such as customers and lower level employees, then firms would do well to test the readability of their mission statements, and revise them where necessary. Mission statements are not the only tools that organizations use to communicate with stakeholder. This encourages future research on readability analysis of other communication tools in firms. A larger sample of companies and other approaches to measure readability can be included in future research. Originality/value This paper adds to the related literature, as the level of readability of mission statements has received limited attention in the past.
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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| 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".