Constant Checking Is Not Addiction: A Grounded Theory of IT-Mediated State-Tracking
Bibliographic record
Abstract
“Constant checking” of digital devices has been a widely observed phenomenon: people check email clients, social networking systems, news websites, and other information technologies (ITs) at the expense of distracted driving, neglected children, and lost productivity. The predominant perspective on such phenomena of excessive IT use is that users are addicted to technology. But actually, we know very little about what exactly constant checking is, what causes it, and under what conditions negative outcomes might ensue. Based on qualitative data collected from 90 individuals, we develop a grounded theory that views constant-checking behaviors as information-seeking habits instead of an addiction in need of medical treatment. We find that these habits satisfy deep-rooted and recurring needs for information, are facilitated by today’s high accessibility of information, and are fueled by an interesting reward pattern. To represent constant-checking behaviors in light of our findings, we posit a new construct: IT-mediated state-tracking, defined as an individual’s habitual use of IT to seek information that closes the gap between their knowledge about a real-world domain’s state and its actual state. We also learn that the intended and unintended consequences of IT-mediated state-tracking are contingent upon situational factors, which suggests that a more balanced perspective on these behaviors is warranted. Our research steers the discussion about excessive IT use in a new direction by offering a new construct and a grounded theory that helps us to better understand the phenomenon of constant checking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".