Bibliographic record
Abstract
This dissertation addresses the issue of user adoption of interface agents for electronic mail (email). Interface agents are reactive, continuous, collaborative, and autonomous software entities that act on a user's behalf by communicating directly with a person offering assistance and advice in performing computer-related activities. The study presents and empirically validates a model that describes user adoption behavior, offers insights on important features of this technology from the end-user perspective, reports on critical incidents of agent usage, and offers recommendations for developers and marketers. As means of investigating this phenomenon, a survey of actual users of an interface agent-based email system was conducted. Emphasis was placed on identifying user needs and key factors that influence their adoption decisions. Data analysis involved quantitative and qualitative techniques (Partial Least Squares, descriptive statistics, classical content analysis). An extended version of the Technology Acceptance Model was introduced and tested, and the user context surrounding email agent adoption was explored. Survey findings suggest that existing management information systems and social sciences theories, models, and methodologies may be fruitfully applied to investigate user adoption of novel interface agent technologies. By combining and synthesizing results of a deductive and inductive analysis of the survey data, a new, grand model of interface agents adoption and use is suggested that is the central contribution of this research. According to this model, in voluntarily usage conditions, two general types of factors influence user adoption behavior - user perceptions and agent operability. User perceptions are either positive or negative mental reflections of several properties of an agent, such as perceived enjoyment, usefulness, ease of use, intrusiveness, and attractiveness. Agent operability embraces factors pertaining to operational characteristics of an agent, such as compatibility, system interference, reliability, and personalization. Findings also suggest that to foster the diffusion of highly useful agent systems, developers and marketers need to become aware of the importance of individual user characteristics, enhance their understanding of factors influencing people's adoption decisions, and demonstrate the functionality of interface agents through non-agent technologies.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".