The Path to Hedonic Information System Use Addiction: A Process Model in the Context of Social Networking Sites
Bibliographic record
Abstract
Addiction to hedonic information systems yields significant negative consequences for users. Although we know about the causes of addictions, particularly those related to individual differences, recent evidence suggests that addiction evolves gradually over time and is rooted in shared characteristics of users and technology. This paper provides a longitudinal perspective over how and why hedonic information systems (IS) use addiction develops. Based on our analysis, we break down this process into three phases characterized by different types of use, whether nominal, compulsive, or addicted. Each phase highlights salient psychological needs that motivate, technology features that enable, and affordances that are actualized into each type of use. We also provide a detailed account of individuals’ self-control mechanisms, explaining how deficiencies in sensing, comparing, or regulating behavior facilitate one’s transition toward addiction. These insights are applicable to other hedonic IS that are similar in terms of ubiquity and constant access through mobile apps. They point to heterogeneous (preventive or intervening) strategies that can be used to help people regain their control over use, depending on where they are in their trajectory toward addicted use. Our findings carry implications for the design of systems and features that can help reduce the likelihood of addiction development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".