Examining chatbot usage intention in a service encounter: Role of task complexity, communication style, and brand personality
Bibliographic record
Abstract
This study investigates the role of chatbot communication style (task vs. social oriented), task complexity (high vs. low), brand personality (sophisticated vs. sincere), and anthropomorphism on consumer trust and chatbot usage intention. Data is collected through three experiments conducted among US respondents ( N = 328, 200, and 336). The results offer mixed insights as only one experiment supports that task complexity moderates the effect of communication style on trust, such that, task-oriented communication style of the chatbot leads to higher trust under high task complexity conditions. No significant differences in the moderating effect of task complexity on the relationship between communication style and trust is observed between sincere and sophisticated brands. Consistent across the three studies, it is observed that perceived anthropomorphism mediates the effect of communication style on trust which, in turn, affects intention to use the chatbot. The study contributes to literature on AI-enabled conversational agents, human computer interaction , anthropomorphism, and trust. Practically, the study offers insights for managers and service providers who wish to integrate chatbots and other AI enabled technology to enhance service delivery by providing efficient, cost-effective, and consistent support.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".