Strategic alliances and firms’ chances to survive “black swans” in B2B industries
Bibliographic record
Abstract
Purpose This study aims to propose that, in business-to-business (B2B) industries, number of strategic alliances firms established before a “black swan” event enhances their chances to survive the black swan, and the enhancements take place through moderation effects. Changes in firms’ core structures – their stated goals, authority structure, core technologies and marketing strategies – to adapt to business jolts have adverse effects on firm performance. Firms’ existing B2B strategic alliances moderate the effects negatively by outsourcing different goals, authority structures, core technologies and marketing strategies to partners who fit the changed environment. Design/methodology/approach This study collected quantitative data and analyzed the data with the regression method. Findings Using data from Chinese firms in five technology industries during the 2007–2009 economic crisis, this study finds that firms’ internal adaptation is negatively correlated with their performance during economic crises, and B2B strategic alliances negatively moderate this relationship. Research limitations/implications First, this study focuses on B2B strategic alliances, and it is not clear whether the findings apply to B2C industries, where strategic alliances may not be common. Perhaps firms can use other means of survival in addition to strategic alliances in B2C industries. Second, this study does not differentiate between fast-moving and slow-moving industries, and it is not clear whether strategic alliances play the same role in both industries. Third, this study does not differentiate firm ages and sizes. It remains unclear how large, established and small, young firms differ when facing crises. Finally, this study is based on the Chinese setting, and it is not clear whether the findings apply to other markets as well. These issues should be explored in future studies. Practical implications Changing firms’ core structures harms their performance during black swan crises because such crises are unpredictable, and planned changes may not adapt firms to crises. Managers should not attempt to change their core structures during crises. B2B strategic alliances provide an effective means for firms to survive crises. Originality/value This paper makes two contributions to the existing literature: First, this paper demonstrates that changes of one of the four core structures of a firm to cope with black swan events have negative impacts on firm performance. Second, this paper identifies the importance of holding a variety of strategic alliances previously to the black swan events to reduce the negative impacts of changing core structures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".