From Attention to Activism: Demystifying Corporate Engagement in Digital Issue Arenas
Bibliographic record
Abstract
Corporations increasingly face pressure to participate in sociopolitical discourse, yet their engagement patterns in corporate activism remain theoretically underexplained in the existing scholarship. In this study, we try to inductively build a theoretical explanation for the observed pattern of corporate activism using the theoretical lexicon of issue arenas and digital trace data from multiple sources. Using a computationally intensive theory construction (CITC) approach, the study analyzes digital traces from Wikipedia pageviews, Google search trends, and corporate Twitter posts (23,460 tweets from S&P 500 companies, 2015–2021). Conceptualizing digital spaces as issue arenas, the study introduces the concept of digitally enabled corporate activism (DECA). Then, first building qualitative-exploratory evidence for the interplay between collective public attention and DECA and subsequently employing quantitative analysis through Vector Autoregression (VAR) and Necessary Condition Analysis (NCA), the study establishes collective public attention to be a necessary trigger for DECA. Lastly, we strive to inductively theorize about the mechanism that connects collection attention to corporate activism by drawing from the theoretical foundation of social license and corporate legitimacy. The study contributes to the discourse on corporate activism and the IS scholarship focusing on corporate use of digital space for non-business activities.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".