A typology of user liability to IT addiction
Bibliographic record
Abstract
Abstract To date, information systems (IS) research mainly has provided a monolithic view of information technology (IT) use, considering it to be a desired behaviour with positive outcomes. However, given the dramatic increase in the use of technology during the last few years, susceptibility to IT addiction is increasingly becoming an important issue for technology users and IS researchers. In this paper, we report the results of a study that focuses on identifying variations in user liability to IT addiction, which reflects the susceptibility of individual users to develop IT addiction. First, a review of the literature in different disciplines (e.g. health, psychology and IS) allows us to better understand the concepts of IT addiction and liability to addiction. The literature review also provides an overview of the antecedents and consequences associated with IT addiction. Then, building on the analysis of 15 in‐depth interviews and 182 exploratory open‐ended surveys collected from smartphone users, we apply the concept of liability to addiction in the IT use context and propose a typological theory of user liability to IT addiction. Our typology reveals five ideal types; each can be associated to a user profile (addict, fanatic, highly engaged, regular and thoughtful). Building upon both the extant literature and our results, we put forth propositions to extend the theoretical contributions of the study. We finally discuss the contributions and implications of our paper for research and practice.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".