Implementing Disruptive Technologies: What Have We Learned?
Bibliographic record
Abstract
Rich research opportunities lie ahead for scholars interested in building a theory to explain why and how some organizations succeed while others fail in implementing disruptive technologies. As a complex socio-technical process, implementing disruptive technologies represents an endeavor fraught with challenges. Leaders need tools to assess whether implementing a potentially disruptive technology will succeed or fail; planners need a road map to navigate the implementation’s potential stepping-stones and stumbling blocks. Disruptive technology implementation scholarship is rich, has eclectic roots and conflicting findings, but lacks a success theory. To advance such a theory and guide scholars and practitioners, we conducted a structured and systematic literature review, and examined 139 empirical articles published between 1983 and 2020 in leading management and information systems journals. We focused our attention on answering two questions: How do incumbent organizations implement disruptive technologies successfully? How does the implementation of disruptive digital technologies differ from the implementation of other disruptive technologies? We employed a mixed-method approach using three criteria: technological category, challenges to successful implementation, and degree of implementation success. We identified strategic and technical implementation challenges, developed a technology implementation framework, and advanced propositions that together provide a current disruptive technology implementation success theory pending further testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.020 | 0.043 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".