Zoom Communication Inc.: keeping the good times going?
Bibliographic record
Abstract
Purpose The purpose of this study is to describe how Zoom became the tope video conferencing service across the globe. Research methodology This case was developed from secondary sources including industry reports, academic, newspaper, periodical sources, company annual reports, social media sites and company websites. This case has been classroom tested with undergraduates in a strategic management course as a capstone course. Case overview/synopsis The case study describes the rapid growth of Zoom Communications Inc., a San Jose based publicly traded video conferencing company founded in 2011 by Eric Yuan. It illustrates the competition in the online meeting solutions industry in late 2020, during the COVID-19 lockdown. To explain how Zoom became the top video conferencing service across the globe, the case highlights the attractiveness of the market and the competitive advantage of Zoom over its rivals. Students can evaluate the internal capabilities and competencies of Zoom as well as identify key challenges in the external environment for sustaining Zoom’s competitive advantage. Complexity academic level This case study is suitable for strategic management classes for upper-level undergraduates and at the graduate level for MBA and/or master students. It prepares students to discuss core concepts in strategy, such as competitive strategy and competitive forces that shape strategy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".