The Ongoing Quest for the it Artifact: Looking Back, Moving Forward
Bibliographic record
Abstract
More than 10 years ago, Orlikowski and Iacono (2001) examined the conceptualization of Information Technology (IT) in Information Systems Research (ISR) articles published in the 1990s. Their main conclusion was that the majority of these articles did not properly conceptualize the IT artifact. They recommended that IS researchers start to theorize about the IT artifact and employ rich conceptualizations of IT. The Orlikowski and Iacono paper provides a strong anchor point from which to analyze the evolution of the IS discipline. In order to obtain an up-to-date image of contemporary IS research, and to assess how the IS field has evolved since the 1990s, we carried out a similar analysis on a more recent and broader set of articles, that is, the full set (N = 644) of papers published between 2006 and 2009 by six top North American (ISR, MISQ, JAIS) and European (JIT, ISJ, EJIS) journals. The statistics in our results reveal no drastic advance in terms of deeper engagement with the IT artifact; more than 39% of the articles in our set are virtually mute about the artifact, and less than 16% employ an ensemble view of IT. Moreover, we note differences among the North American and European journals. Implications of the findings for two perspectives central to the IS research legitimacy debate are discussed.
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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 | 0.038 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.029 | 0.054 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".