Evidence map›Paper›PMID 39985116›Full record

ArticleBMC psychology2025

The effects of response time on older and young adults' interaction experience with Chatbot.

Ya-Ling Wang, Chi-Wen Lo

Abstract read
In one paragraph

Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ya-Ling WangNational Taiwan Normal University, Taipei, Taiwan. ylwang47@gapps.ntnu.edu.tw.
Chi-Wen LoNational Taiwan Normal University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatbots, such as Siri, Alexa, and ChatGPT, are increasingly integrated into various domains, including customer service and virtual companionship, transforming human-computer interactions. However, there remains limited understanding of how response time-a critical social cue-affects user experience across different age groups, particularly in virtual companionship contexts. This gap is especially relevant in aging societies where older adults' emotional and relational needs require tailored technological solutions.

methodsA 2 × 2 between-subjects experiment was conducted with 160 Taiwanese participants from two age groups: young adults (18-23 years old) and older adults (56-81 years old). Participants interacted with chatbots under two conditions: instant responses (approximately 3 s) and delayed responses (10-60 s). User experience was measured using social presence (SP), service encounter satisfaction (SAT), and intention of use (IOU) through validated questionnaires. Two-way ANOVA was employed to analyze the main and interaction effects of response time and age group.

resultsThe findings revealed a significant interaction effect between response time and age group. While younger adults preferred instant responses, older adults showed a preference for delayed responses. Specifically: (1) Instant responses led to higher satisfaction and engagement for younger adults, who value efficiency and immediacy. (2) Delayed responses facilitated cognitive comfort and enhanced relational value for older adults, emphasizing the importance of conversational pacing.

conclusionsThis study highlights the critical role of response time in chatbot design, revealing how age-specific preferences influence user satisfaction in virtual companionship scenarios. Findings underscore the need for adaptive chatbot designs that align with cognitive and emotional needs across age groups. Broader implications emphasize the importance of balancing digitally assisted companionship with the risks of dehumanization. Future research should explore long-term interaction effects and cultural differences to enhance chatbot inclusivity and effectiveness.

Indexed as

AgingInterpersonal RelationsReaction TimeSocial InteractionUser-Computer InterfaceAdolescentAdultAgedAged, 80 and overAge FactorsFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedTaiwanAge differenceChatbotsDelayed responseOlder adultsResponse time

Identifiers

PMID39985116
PMCPMC11846305

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.