Negative Emotion and Seeking Social Support during the Early Stage of System Implementation: A Case Study
Bibliographic record
Abstract
Although humans are found to be hardwired for being influenced by referent others from the same social realm (Cialdini and Trost, 1998), our literature review indicates that conventional technology adoption models (such as the Unified Theory of Acceptance and Use of Technology, UTAUT) in Information Systems literature often implicitly assume that user beliefs are independent of what others say or do. Social information and communication are treated as external variables and are not considered directly. Moreover, the picture that these models paint is predominantly cognition-based with little colour of emotion except for Computer Anxiety (Compeau and Higgins, 1995). Although the technology adoption literature does have one research stream on non-instrumental outcomes, such as enjoyment (Davis, Bagozzi and Warshaw, 1992) and flow (Webster, Trevino and Ryan, 1993), its focus is mainly on human-computer interaction (Agarwal, 2000). Emotion-related topics that are ample in the organizational transformation literature are relatively less explored in extant technology adoption literature. The adoption of a new information technology at work invokes a series of changes in work procedures and relationships, which may be a hotbed of negative emotions such as anxiety and powerlessness. We draw on social information processing theory (Salancik and Pfeffer, 1978) and stress and coping theory (Lazarus and Folkman, 1984) to examine how case managers of one non-for-profit health resource coordination institute in Canada could be influenced by one another via seeking social support in order to regain the sense of control and/or to regulate the negative emotion aroused by the coming of a new information technology. Interviews with thirteen case managers were conducted to understand the role of communication in their emotional and behavioural response to the adoption of a new technology. At the end of the paper we propose an alternative conceptual model that incorporates communication and user emotion to enrich the existing understanding of technology acceptance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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 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".