Towards an environmental awareness model integrating formal and informal mechanisms – Lessons learned from the Demise of Nortel
Bibliographic record
Abstract
This case study uses multiple lines of enquiry to better understand how Nortel went from being a ‘global powerhouse’ at the turn of the century to filing for bankruptcy just nine years later. It tracks competitive intelligence as well as other environmental awareness capabilities of the company and theorizes on how they have contributed to its rise and fall. The findings suggest that Nortel was a company with significant environmental awareness capability in the early 90’s that had all but lost this competency by the year 2000, which impacted their ability to make decisions consistent with a changing environment. Through interviews with 48% of all Nortel officers that were there during the period of interest as well as other stakeholders, the researchers identify a two-layer typology that includes a set of cognitive factors as well as three broad categories of monitoring practices that can help companies better understand their environment: 1) formal external monitoring practices, such as competitive intelligence units; 2) informal external monitoring practices such as board meetings with members with industry connections and knowledge, and 3) internal monitoring practices with external insight capability, such as performance management reviews and accounting reports. Cognitive factors identified include decision maker orientation, as either technical or business, internal vs., internal focus, cognitive complexity and open mindedness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".