Exploring the Role of Information Technology in Organizational Downsizing: A Tale of Two American Cities
Bibliographic record
Abstract
This study explores the role information technology (IT) plays in organizational downsizing by studying two medium-sized American cities over a period of 10 years (1985-1995). Data were collected through 73 interviews, a questionnaire, and numerous documents. Four main findings emerged from the case studies. First, IT was found to facilitate organizational downsizing, but not to cause it. New City invested heavily in state-of-the-art IT over the years and more successfully downsized the organization than Old City, which lagged behind in IT investment and made no serious attempts to use IT as a tool to support strategic actions. Second, adverse environmental conditions triggered downsizing in both cities and determined the change strategies that managers used. When environmental pressures were mild (1985-1990), managers favored a convergent change strategy that resulted in limited downsizing efforts and small personnel reductions. In contrast, when environmental pressures were strong (1990-1995), managers of both cities engaged in strategic reorientation and in downsizing efforts that led to larger personnel reductions. Third, the role IT played in organizational downsizing varied according to the change strategy. IT was used to facilitate work redesign in a convergent change strategy and to facilitate more significant structural and work redesign in strategic reorientation. Fourth, more integrated and better use of IT allowed managers of New City to downsize more rationally and efficiently. It facilitated the transfer of personnel within departments, from middle management to the operations level, and across departments, from internally oriented to customer-oriented personnel. In doing so, managers of New City minimized operating costs while maintaining the same level of services. In contrast, IT in Old City did not facilitate such an agenda and managers downsized more superficially across the board, in all departments. Differences in IT consequences in the two cities are explained using the theory of slack resources in 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| 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".