Towards a Theory of Serendipity: A Systematic Review and Conceptualization
Bibliographic record
Abstract
Abstract Serendipity – the notion of making surprising and valuable discoveries – plays a major role in the success of individuals and organizations alike. Previous research has established the importance of serendipity and identified important individual‐ and organizational‐level antecedents. However, the literature has been dispersed and the boundaries of the concept have been blurry, leading to a lack of conceptual clarity and structure, and thus limiting validity and managerial actionability. Based on a systematic literature review, I synthesize existing management‐related research on serendipity and explicate the emergence and composition of serendipity in the organizational context. I first identify three necessary conditions that differentiate serendipity from related concepts such as luck or targeted innovation: agency, surprise, and value. Then, I draw from the literature on sensemaking, event‐based theorizing, and quantum‐based approaches to management to conceptualize the process of cultivating serendipity in the organizational context as a process of enabling potentiality and materialization, and develop a multi‐level theory of (cultivating) serendipity. This conceptualization contributes to our collective understanding of how, why, and when (i.e., under what conditions) organizations can leverage the value in the unexpected, which opens up fruitful avenues for further research.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.037 | 0.028 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".