Re-examining the Status of "IT" in IT Research - An Update on Orlikowski and Iacono (2001)
Bibliographic record
Abstract
Nearly 10 years ago, Orlikowski and Iacono examined the conceptualization of Information Technology in Information Systems Research (ISR) articles published in 1990s, and found that the majority of these articles were not thoroughly engaged with IT artifact. They proposed that IS researchers should start to theorize about the IT artifact and employ rich conceptualizations of IT. In order to assess the field’s response to Orlikowski and Iacono’s recommendations, and obtain an up-to-date image of the contemporary IS research, we carried out a similar analysis on a recent set of articles, i.e. the full set of papers published in the last three years of ISR, Management Information Systems Quarterly (MISQ), and Journal of the Association for Information Systems (JAIS). Our results reveal no drastic progress in terms of deeper engagement with IT artifact; 30% of the articles in our set are virtually mute about the artifact, and only 10% are employing an ensemble view of IT. Nevertheless, there are informative discrepancies between patterns in our results and those in the original study, and noticeable differences among the three journals. Implications of these findings for future research will be 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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.042 | 0.049 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.032 | 0.044 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".