Jul 2026· International Conference on Conversational User Interfaces· pp. 1-14· 0 citations· 98 references
Computer Science
TL;DR
Investigating whether the effectiveness of humanized CAs depends on users’ relational perceptions, specifically whether they view the CA like a servant, friend, or rational agent showed that humanized design negatively affects perceived attitudes toward voice shopping.
Abstract
We rely on conversational agents (CAs) for obtaining a variety of information, but rarely for shopping. A primary design characteristic of CAs shaping user acceptance ever since their inception, is their anthropomorphic nature. They have become increasingly human-like, taking on human roles such as assistant, friend, or servant. While anthropomorphic features are often intended to foster engagement, voice shopping continues to lag behind market expectations. Could humanizing CAs undermine interaction quality rather than enhancing user experience through greater engagement? We investigated whether the effectiveness of humanized CAs depends on users’ relational perceptions, specifically whether they view the CA like a servant, friend, or rational agent. In an online experiment (N = 407), participants viewed standard or humanized videos of voice shopping interactions. Results showed that humanized design negatively affects perceived attitudes toward voice shopping. Furthermore, humanized design decreased perceived benefits when the CA was perceived more like a servant. Findings question widespread design emphasis on humanization and inspire new designs for CAs.
The widespread adoption of intelligent technologies in marketing and sales processes has significantly transformed the way businesses interact with customers. Among these technologies, chatbots have emerged as one of the most important artificial intelligence-based tools, playing an increasingly prominent role in customer service delivery, guidance, and support throughout the purchasing journey. One of the key approaches in chatbot design and implementation is anthropomorphism, whereby chatbots are designed to exhibit human-like characteristics in terms of appearance, language, tone, and behavior. In the contemporary digital environment, the emergence of conversational commerce has established a new paradigm in brand–customer interactions, where conversations increasingly replace traditional website-based search activities. However, the success of this technology largely depends on how customers perceive their interactions with artificial intelligence systems. One of the most effective strategies for enhancing these interactions is chatbot anthropomorphism, which facilitates distinctive customer experiences by incorporating human-like attributes. In this context, Generation Z, as a digitally native cohort that conducts a substantial portion of its online shopping activities through messaging platforms, is particularly sensitive to the qualitative characteristics of chatbots. Despite the importance of this issue, a significant research gap remains regarding how anthropomorphism translates into purchasing behavior through psychological mechanisms such as perceived enjoyment and social presence. Therefore, this study aims to investigate the mediating role of perceived enjoyment and social presence in chatbot commerce, with a particular emphasis on the effect of anthropomorphism on the purchase intention of Generation Z consumers. This study is applied in terms of purpose and employs a survey-based research design for data collection. The statistical population consisted of active users of intelligent chatbot platforms in 2026. Given the unlimited population size, a sample of 190 respondents was determined using Cochran’s formula. Descriptive statistics and demographic data analysis were conducted using SPSS version 24, while inferential analyses were performed through Structural Equation Modeling (SEM) using SmartPLS 3 software. Data were collected using standardized questionnaires. The findings revealed that chatbot anthropomorphism plays a critical role in shaping positive perceptions among Generation Z consumers and significantly enhances purchase intention through two complementary pathways: perceived enjoyment and social presence.
Fatemeh Movahed Nia, Hamed Abbasi, Monireh Tahaei et al.· Digital Transformation and A...· 0 citations
This study aims to investigate the interaction effect between service recovery providers (AI chatbot vs human) and recovery strategies (psychological vs tangible) on consumers’ purchase intentions in the context of chatbot minor service failures. The research further seeks to provide companies with insights to enhance chatbot-based service recovery.
Four experiments were conducted to test the proposed hypotheses.
The results indicate that when chatbots encounter minor service failures, self-recovery using psychological strategies significantly enhances consumer purchase intentions. Conversely, human-mediated recovery is more effective when paired with tangible strategies. Perceptual evaluations of competence play a stronger mediating role than warmth in shaping consumer responses. In case of high task complexity, human-mediated recovery is more effective regardless of whether a psychological or tangible strategy is used. Furthermore, when a highly intelligent chatbot encounters a minor service failure, its self-recovery – whether using psychological or tangible strategies – proves highly effective.
This study contributes to understanding the role of service recovery in the context of chatbot minor service failures. It examines the effectiveness of chatbot self-recovery and human-mediated recovery strategies (psychological or tangible) on consumer purchase intentions, highlighting the critical mediating role of perceptual competence.
: Anthropomorphic chatbot design is reshaping how businesses engage consumers by bringing perceived humanity to digital service encounters. This review synthesizes recent theoretical and empirical work on how anthropomorphic characteristics in chatbots shape consumer trust and brand relationship quality (BRQ). Drawing on anthropomorphism theory, relationship marketing theory, and the Computers-Are-Social-Actors (CASA) paradigm, it examines the roles of perceived authenticity, comfort, social presence, and engagement in shaping consumer responses to human-like conversational agents. The reviewed evidence suggests that moderate anthropomorphism, expressed through warmth, personalization, responsiveness, and natural language, tends to increase trust, satisfaction, engagement, and emotional connection with a brand, while excessive human-likeness can trigger discomfort, privacy concerns, unrealistic expectations, and an erosion of trust, consistent with the uncanny valley effect. The review further highlights that these effects are highly context-dependent, and that transparent or hybrid chatbot designs may help preserve emotional connection while retaining functional credibility. The paper closes with a synthesis of theoretical, managerial, and future-research implications for brand managers designing AI-mediated customer touchpoints.
Unknown authors· International Journal of Sci...· 0 citations
Organizations increasingly adopt chatbots as strategic tools to engage with consumers and strengthen brand outcomes. This study explores how chatbots that use name‐based personalization in their communications can more efficiently influence consumer behavior towards the brand. Across five experimental studies, we demonstrate that name‐based personalization within chatbot messages enhance perceptions of chatbot sincerity and brand sincerity, leading to more favorable consumer attitudes and higher purchase intentions toward the brand. The effect of personalization on perceived chatbot sincerity is mediated via perceived anthropomorphism. Further, we identify a few boundary conditions for the positive effects of name‐based personalization, namely chatbot performance and perceived overuse of name‐based personalization. These findings contribute to the literature on personalization, anthropomorphism, branding, and the Computers Are Social Actors (CASA) theory, and offer practical implications for marketers seeking to design effective chatbots for brands.
Ji Min Lee, Vivian Pontes, S. Khan et al.· Psychology & Marketing· 0 citations
The present study aims to empirically investigate how the language styles of artificial intelligence (AI) chatbots, specifically their use of abbreviations, formality and message length, influence consumer perceptions of sincerity, effort and brand-related outcomes in service interactions.
Four experiments (n = 878) were conducted to test the effects of language style in various service and brand contexts. The studies manipulated chatbot communication features and measured their influence on perceptions of sincerity, engagement, brand attitudes and behavioral intentions.
The results suggest that abbreviation-heavy messages consistently reduce perceptions of sincerity and a service agent’s effort, especially when the agent is human. Informal and concise chatbot responses enhance engagement and perceived sincerity, but only when aligned with brand context. Informality strengthens brand attitudes and purchase intentions when psychological brand closeness is high (Study 3) and when the brand is positioned as premium (Study 4).
Since the studies use mock interactions in online experiments, real-world generalizability may be limited. For validation, future research could explore these effects via longitudinal and field-based studies.
This study offers actionable insights for service designers and brand managers on tailoring chatbot communication to enhance consumer trust, engagement and brand alignment. Language style should be strategically aligned with brand positioning and the strength of the consumer–brand relationship.
This study advances the understanding of chatbot communication by framing language features as social signals, extending social response theory to AI-mediated service contexts and revealing boundary conditions for effective chatbot design.
S. Sands, Carla Ferraro, Vladimir Demsar et al.· European Journal of Marketin...· 0 citations
As conversational artificial intelligence becomes increasingly embedded in digital service environments, understanding user resistance to chatbot-based information systems has become critical. While prior research has focused primarily on adoption drivers, less attention has been given to the mechanisms underlying resistance. Drawing on user resistance theory and human–computer interaction research, this study examines how intrapersonal factors (technological anxiety, perceived incompetence, and privacy concerns) and system-related factors (repetitiveness, impersonality, and irrelevance) jointly influence resistance and subsequent usage intention. Survey data from 400 chatbot users were analyzed using partial least squares structural equation modeling. The findings reveal that technological anxiety, perceived incompetence, impersonality, and irrelevance significantly increase resistance, which in turn reduces intention to continue use. By contrast, privacy concerns and repetitiveness show limited effects. By jointly modeling intrapersonal and system-related barriers within a single framework, the study clarifies their relative contribution to resistance and offers chatbot developers concrete guidance for reducing both psychological and interactional sources of user disengagement. The study advances understanding of resistance in artificial intelligence–based information systems and provides implications for improving interaction quality and sustaining user engagement.
Hyeon Jo, Jee-Sun Oh· Journal of information scien...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
