Critical Realism and Affordances: Theorizing IT-Associated Organizational Change Processes1
Bibliographic record
Abstract
Convincing arguments for using critical realism as an underpinning for theories of IT-associated organizational change have appeared in the Information Systems literature. A central task in developing such theories is to uncover the generative mechanisms by which IT is implicated in organizational change processes, but to do so, we must explain how critical realism’s concept of generative mechanisms applies in an IS context. Similarly, convincing arguments have been made for using Gibson’s (1986) affordance theory from ecological psychology for developing theories of IT-associated organizational change, but this effort has been hampered due to insufficient attention to the ontological status of affordances. In this paper, we argue that affordances are the generative mechanisms we need to specify and explain how affordances are a specific type of generative mechanism. We use the core principles of critical realism to argue how affordances arise in the real domain from the relation between the complex assemblages of organizations and of IT artifacts, how affordances are actualized over time by organizational actors, and how these actualizations lead to the various effects we observe in the empirical domain. After presenting these arguments, we reanalyze two published cases in the literature, those of ACRO and Autoworks, to illustrate how affordance-based theories informed by critical realism enhance our ability to explain IT-associated organizational change. These examples show how researchers using this approach should proceed, and how managers can use these ideas to diagnose and address IT implementation problems.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".