Managing MIS project failures : a crisis management perspective
Bibliographic record
Abstract
This study describes a conceptual framework that portrays information system project failures as organizational crises. The main assumption of this study is that such failures will invariably happen and thus there is a need to make them less costly and more beneficial to organizations. To identify the behaviors and factors that influence an organization's ability to effectively manage a project failure, this dissertation reviews the crisis management literature. Based on this review, a three-stage model is formulated. To understand the mechanisms underlying this model, a number of hypotheses (which are informed by a number of related organizational behavior areas) are generated. These hypotheses focus on three key crisis management factors: the organization's ability to promptly detect an impeding failure, its capacity to manage the failure's impacts, and its propensity to learn from it. To empirically assess the validity of the conceptual model, three case studies of Canadian public organizations were conducted. The empirical findings provide strong support to the model's conjectures and indicate that project failures generate several crisis-related behaviors and responses. More specifically, the findings suggest that an organization's proactive preparation for a failure can have a significant moderating effect on its impact. However, the findings clearly show that an organization's ability to promptly detect (and prepare for) a failure is impeded by behaviors that are motivated by escalation of commitment. Such behaviors lead to a prolonged pre-crisis denial period and have a suppressing effect on whistle-blowing, which is pursued as a denial-curtailing strategy by non-management participants. The empirical findings describe both operational and legitimacy tactics used by organizations to cope with the aftermath of a project failure and indicate that credibility restoration is a significant concern during large crises. Finally, the empirical evidence indicates that organizational learning and adaptation are more likely to follow major project failures than less significant ones. This contradicts threat-rigidity arguments and provides support to the failure-induced learning theory.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".