Applying Event System Theory to Organizational Change: The Importance of Everyday Positive and Negative Events
Bibliographic record
Abstract
Decades of research have examined how employees experience organizational-level change events (e.g., “the merger”). However, employees can also experience “everyday change events” that occur at the individual-level as the change becomes routinized for their jobs. That is, individuals can react to organizational change events that are occurring at different hierarchical levels. Drawing on event system theory, we argue that employees’ commitment to the organizational-level change event can shape how employees anticipate and experience subsequent everyday change events. These negative and positive everyday change events can impact (a) how employees engage with their work, impacting their performance and (b) whether employees perceive that they are fairly treated, impacting their subsequent evaluations of organizational-level change. Our hypotheses were generally supported in a field sample in which employees were surveyed immediately after a merger was announced, participated in a daily diary study as the merger was implemented, and completed a second survey 2 weeks after the diary study. By applying event system theory to organizational change, we provide important theoretical and practical insights, including how an organizational-level event can exert top-down direct effects by impacting how employees anticipate and experience change on an everyday basis as well as how everyday negative and positive change events can subsequently impact employees’ commitment to the organizational-level change, creating bottom-up direct effects. We also illuminate the importance of considering the frequency and strength of both negative and positive events to understand what it is about everyday negative and positive events that has implications for employees and organizations.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".