Communication and attention dynamics: An attention‐based view of strategic change
Bibliographic record
Abstract
Research Summary : The attention‐based view (ABV) has highlighted the role of organizational attention in strategic decision making and adaptation. The tendency to view communication channels as “pipes and prisms” for information processing has, however, limited its ability to address strategic change. We propose a broader role for communication as a process by which actors can attend to and engage with organizational and environmental issues and initiatives and argue that such a view can significantly advance understanding of strategic change. On this basis, we offer suggestions for future research on communication practices, vocabularies, rhetorical tactics, and talk and text in shaping organizational attention in strategic change. We also maintain that this enhanced view of the ABV can help advance research on dynamic capabilities, strategy processes, strategy‐as‐practice, and behavioral strategy. Managerial Summary : To further enhance our capabilities to manage strategic change and renewal processes in organizations, we need a better understanding of how to manage organizational attention. In this article, we highlight the importance of understanding the role of communication and discuss the use of different communication practices, vocabularies, rhetorical tactics, and talk and text as possible levers that can be used to dynamically shape organizational attention. We call for further research to advance the understanding of how these levers can be used to influence the ways in which different sets of strategic issues, initiatives, and action alternatives are handled. We believe that such an enhanced view of organizational attention can enable the development of new, improved strategy practices to manage strategic change and renewal processes.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".