Defrag and reboot? Consolidating information and communication technology research in I-O psychology
Bibliographic record
Abstract
Abstract Several decades of research have addressed the role of information and communication technologies (ICTs) in industrial-organizational (I-O) psychology. However, segmented research streams with myriad terminologies run the risk of construct proliferation and lack an integrated theoretical justification of the contributions of ICT concepts. Therefore, by identifying important trends and reflecting on key constructs, findings, and theories, our review seeks to determine whether a compelling case can be made for the uniqueness of ICT-related concepts in studying employee and performance in I-O psychology. Two major themes emerge from our review of the ICT literature: (a) a technology behavior perspective and (b) a technology experience perspective. The technology behavior perspective with three subcategories (the “where” of work design, the “when” of work extension, and the “what” of work inattention) explores how individual technology use can be informative for predicting employee well-being and performance. The technology experience perspective theme with two subcategories (the “how” of ICT appraisals and “why” of motives) emphasizes unique psychological (as opposed to behavioral) experiences arising from the technological work context. Based on this review, we outline key challenges of current ICT research perspectives and opportunities for further enhancing our understanding of technological implications for individual workers and organizations.
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 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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".