Using the Internet for Organizational Research: A Study of Cynicism in the Workplace
Bibliographic record
Abstract
The Internet can be a valuable data collection tool for organizational psychology researchers. It can be less expensive than traditional paper-and-pencil survey methods, and the potential pool of participants is much larger. In addition, it can be used in situations where traditional data collection methods are not feasible, such as research involving sensitive issues such as negative employee attitudes or deviant behaviors at work. In this study, we examined the organizational attitudes of employees from various companies using (a) a snowball sample, who completed a traditional paper and pencil survey (n = 135), and (b) a sample recruited over the Internet, who completed an on-line survey (n = 220). Participants in both the non-Internet and the Internet group were asked to describe a negative incident involving their company, and answer a number of questions regarding how they felt about their company and how they behaved toward their company following the negative event. They also completed measures of organizational cynicism and job satisfaction. The two groups were compared on demographic characteristics and on their attitudes toward their organization. There were very few demographic differences between the two groups. The Internet group tended to be more cynical and to judge their organization more harshly than the non-Internet group; however, the response patterns of both groups were similar. These results suggest that, when used with caution, the Internet can be a viable method of conducting organizational research.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".