Dis-identification in Organizations and Its Role in the Workplace
Bibliographic record
Abstract
Considerable theory and research has revealed that organizational identification (OID) benefits individuals and groups and that OID facilitates the development of long-term commitment and support towards an organization. Prior studies have highlighted the importance of an identification mechanism in the workplace, i.e., how employees define their self-concepts vis-à-vis their connections with their organizations. In contrast to previous research, we explore the process by which employees divorce their identity from that of their organization, i.e., defining who they are by what they are not. Interestingly, how individuals dis-identify themselves from the organization still remains unclear, and the concept of dis-identification in organization (DiO) has not drawn much academic attention. The paucity of research in this area leaves theories under-developed; thus, our research seeks to shed new light on the concept of DiO and understand its importance at work. An anonymous questionnaire survey was conducted, recruiting 304 employees across eight organizations in Taiwan. Different from prior studies, this research stated that OID and DiO were neither heterogeneous nor independent constructs. Statistical evidence affirmed this statement further and explained that OID and DiO were inter-related constructs. Moreover, two DiO antecedents were discovered, including: person-organization fit and abusive supervision. Unlike in previous studies, DiO was not correlated with poor employee performance; rather, it was correlated with workplace deviance, an intention of quitting the job, and voice-extra-role-behaviour. Organizations are complex entities by their very nature. Whether an organization can continue, function and succeed may depend upon a series of organizational characteristics. An organization is like a social arrangement that pursues collective goals, controls its own performance, and has a boundary separating it from its environment. One such organizational characteristic is identification. With a better understanding of OID/DiO, managers and HR practitioners can better observe the influence of OID/DiO and develop policies to increase employees’ identification and decrease dis-identification. Ultimately, employers, employees and society will enjoy the benefits of better organizations, e.g., higher working morale, more performance output, stronger membership/cohesion, and lower turnover.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".