Enhancing Customer Journey with Chatbots: The Role of Anthropomorphism and Response Modality in Hedonic vs. Utilitarian Context
Bibliographic record
Abstract
As chatbots become integral to digital customer journeys, understanding how specific design elements influence user experience and behavior is increasingly critical. This study investigates the effects of two chatbot features—anthropomorphism (human-like cues) and response modality (visually enhanced vs. non-visual)—on cognitive and affective customer experiences and subsequent purchase intentions. Grounded in the Stimulus-Organism-Response (SOR) framework and informed by Media Richness Theory (MRT) and Regulatory Focus Theory (RFT), we conceptualize these design elements as environmental stimuli that elicit distinct psychological responses. Importantly, we examine how these effects vary across hedonic (pleasure-oriented) and utilitarian (function-oriented) product types, which represent different motivational orientations. Our findings aim to advance theoretical understanding of technology-mediated interactions by highlighting boundary conditions that shape the effectiveness of chatbot features. Practically, the study provides actionable guidance for aligning chatbot design with consumer expectations based on product context, ultimately enhancing customer engagement and optimizing digital touchpoints.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".