ORGANIZATIONAL COMMUNITY CHARACTERISTICS AND BARRIERS TO ENVIRONMENTAL MANAGEMENT: ADDRESSING THE ROLE OF COMMUNITIES OF PRACTICE, LANGUAGE, AND MENTAL MODELS IN ORGANIZATIONAL CHANGE
Bibliographic record
Abstract
The current state of organizational environmental management is inadequate to face the risks posed by the natural environment such as resource scarcities resulting from natural, social and/or economic processes such as increases in extreme weather events, natural resources-based conflicts, regulatory restrictions, or trading commodities fluctuations. These changes to the organizational environment call for adaptation so that we may maintain our ability to produce and function sustainably. \n\nOrganizations need to adopt practices and processes that consider the natural environment. The adoption of environmental management practices and processes has, however, met barriers. Faced with initiatives that promise considerable financial and environmental benefits, research has reported resistance to buy-in. I suggest that perception barriers, imbedded in language, have a role to play in this lack of change. I propose that individual’s mental model of the organization filters information for decision-making based on language. I suggest that the linguistic repertoire of communities of practice is used to filter information relevant to organizational decision-making.\n\nA quantitative study showed that differences in community of practice linguistic repertoires are found between the environmental and business communities. In addition, varying levels of familiarity with the repertoire of a community of practice were explored to determine whether community membership is reflected in the linguistic repertoires of individuals. The results suggested that the familiarity of graduate students with the linguistic repertoire of the community of practice they were studying was more akin to membership in a community of interest than a community of practice. In addition, the results suggested that environmental and business communities held opposing sets of relevant linguistic repertoires, providing grounds for communication barriers. \n\nFinally, in addition to exploring language’s potential as a barrier and opportunity for change, the in individual’s mental models ability to change was explored. Through case study observations, I showed that a change in the individual’s mental model of the organization could result from participation in an eco-efficiency program. The participants, high-level decision-makers in the organizations, introduced the natural environment as a new aspect to their future decision-making process following participation. Contributions to management and social theory are also discussed.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".