Directing Technology Addiction Research in Information Systems
Bibliographic record
Abstract
In this second part of a series of articles to direct technology addiction research in the information systems discipline, we discuss the history, conceptualization, and measurement of technology addiction. We admit that it is possible to label the phenomenon as overuse or excessive use as long as it is defined and measured by the presence and the magnitude of the six core symptoms of behavioral addictions: salience, mood modification, tolerance, withdrawal, conflict, and relapse. The advantage of this terminology is that it does not attribute one's problems to helplessness and does not pathologize the behavior, implying that it may possibly be corrected. Nevertheless, we posit that the term technology addiction is currently the most reasonable choice that may need to be adjusted as we learn more about this phenomenon and its potential similarities to and differences from established behavioral addictions. Dependence, obsessive/compulsive use, and pathological/problem use terms should not be used as synonyms for technology addiction as a form of mental disorder. Researchers should not include the name of the IT artifact as the subject of addiction (e.g., "Facebook addiction"). Instead, they should focus on the activity that is mediated through the IT artifact (e.g., "addiction to Facebook use").
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".