Embracing Bewilderment: Responding to Disruption in Heterogeneous Market Environments
Bibliographic record
Abstract
Rapid advances in technology are causing disruption to more and more industries. Prior research has emphasized that heterogeneity in the demand side is important in determining how incumbents respond to technological disruption. However, at present we know too little about the implications this may have for incumbents, particularly multinational enterprises (MNEs), and for their strategic change processes. We address this gap by looking at detailed longitudinal data from Ericsson, which reveals how the company dealt with Cloud computing as an emergent disruptive technology. From analysis of internal documents and communications, questionnaires, and interviews with managers at different levels of the organization, together with field-based observation, we are able to identify three key areas: (1) market heterogeneity as an important source of strategic contradiction, (2) the tensions that market heterogeneity causes at different stages in the innovation process, and (3) the implications of such tensions for the strategic responses of the MNE. We conclude by discussing the capabilities required for competitiveness when companies face technological disruption in heterogeneous markets.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".