The Other Side of Acceptance: Studying the Direct and Indirect Effects of Emotions on Information Technology Use1
Bibliographic record
Abstract
Much ado has been made regarding user acceptance of new information technologies. However, research has been primarily based on cognitive models and little attention has been given to emotions. This paper argues that emotions are important drivers of behaviors and examines how emotions experienced early in the implementation of new IT applications relate to IT use. We develop a framework that classifies emotions into four distinct types: challenge, achievement, loss, and deterrence emotions. The direct and indirect relationships between four emotions (excitement, happiness, anger, and anxiety) and IT use were studied through a survey of 249 bank account managers. Our results indicate that excitement was positively related to IT use through task adaptation. Happiness was directly positively related to IT use and, surprisingly, was negatively associated with task adaptation, which is a facilitator of IT use. Anger was not related to IT use directly, but it was positively related to seeking social support, which in turn was positively related to IT use. Finally, anxiety was negatively related to IT use, both directly and indirectly through psychological distancing. Anxiety was also indirectly positively related to IT use through seeking social support, which countered the original negative effect of anxiety. Post hoc ANOVAs were conducted to compare IT usage of different groups of users experiencing similar emotions but relying on different adaptation behaviors. The paper shows that emotions felt by users early in the implementation of a new IT have important effects on IT use. As such, the paper provides a complementary perspective to understanding acceptance and antecedents of IT use. By showing the importance and complexity of the relationships between emotions and IT use, the paper calls for more research on the topic.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".