Advancing theory through the conceptualization and development of causal attributions for computer performance histories
Bibliographic record
Abstract
Attribution theory, advanced by Bernard Weiner and his colleagues is an important, though sometimes controversial, theory that has demonstrated vitality and longevity. The cross-disciplinary application of attribution theory to, for example, organizational behavior, marketing, and education, has stimulated the interests of researchers and contributed to its theoretical validity and reliability. Compared to other disciplines, the application and theoretical testing of attribution theory are in the "spring" of their existence in the field of Information Systems (IS). This paper proposes that conceptualization and measurement of the causal attributions individuals make for their computer performance and performance histories, positive and negative, are critical to understanding computer adoption and post-adoption behaviors. We first identify the causal attributions that enterprise resource planning (ERP) users make for their computer performance histories. We then describe the conceptualization and development of multi-item scales to capture these causal attributions. This work contributes to theory and practice through (1) the development and psychometric testing of several attributional scales for advancing our understanding of the multi-theoretical stream of research investigating technology adoption at the individual level, and (2) by providing a description of a theoretical multi-method approach for the rigorous scale development of causal attributions. Our work suggests that researchers must consider several fundamental principles of attribution theory when investigating IS artifacts during various adoption phases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".