Creation and Adaptation of Norms in a Tire-Mold Manufacturing Organization
Bibliographic record
Abstract
This case study examined the ways in which organizational members created norms that sustained the organizational culture in a tire-mold manufacturing organization, the effects of those norms, and members' motivation to comply with them.Within the organization, cocultures maintain identical values but employ different norms due to the nature of their work.Despite the standardized processes for designing and manufacturing tire-molds, norms that sustain the values of autonomy and creativity enable innovation that competition demands.This kind of study adds to organizational communication research about norms and can benefit organizational leaders, researchers, and consultants when assessing cultural values to determine strategies for change.The tire industry is competitive and requires change in products, procedures, and communication.Because the values and norms are imbedded in the culture, any such changes must address these strongholds.This case study can be used as a comparison to other organizations in a competitive environment.To conduct an assessment of the culture and its norms, the researcher audio taped face-to-face interviews with 30 members of a tire-mold manufacturing organization in a small Midwest town and observed organizational members over a period of three days.The tapes were transcribed verbatim and analyzed using Grounded Theory (Glaser and Strauss, 1967;Strauss and Corbin, 1998).Themes that emerged revealed the cultural values and the norms that sustain them.The strength of this case study lies in its ability to identify cultural elements attributed to the norms.However, the restricted time spent in the organization limited the amount of contact with organizational members who were administrators; thus, findings would be richer and more conclusive with additional time.iv DEDICATION This work is dedicated to my soul-mate and rock, Gene, whose love and support sustained me through this journey and who celebrated each milestone as a significant event of personal, relational, and professional growth.v ACKNOWLEDGEMENTS I would like to thank the many people who helped and encouraged me throughout this project.I attribute my sense of self and the success of this project to my community of supporters.First, I would like to thank my advisor and mentor, Dr. Lynda Dee Dixon, whose belief in me spurred greater things than I had ever dreamed possible.Her mentoring extended beyond the normal duties of an advisor or instructor.Even as I completed the final edits to this document, a few weeks after a major surgery, with her arm in a sling, she phoned to offer more feedback after an evening class.Her mentoring inspired my interest in Health Communication and created an acute awareness and appreciation of the challenges caregivers of loved ones with Alzheimer's face.I would also like to thank my committee members, Dr. Radhika Gajjala, whose expertise in cultural communication and CMC enabled me to consider possibilities for using unconventional channels to initiate and maintain relationships; Dr. John Makay whose knowledge of rhetoric and organizational communication helped me critique and understand organizational members; and Dr. Gregory Garske, whose mentoring in the Career Counseling class helped me understand the value of job satisfaction and its relationship to a positive work environment.I would also like to thank Dr. Dennis Hale for the endless supply of research questions he helped me consider in his classes and when he offered advice on this project.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".