Customers judge an AI voice bot as one impression: Oren Dayan in the Journal of Theoretical and Applied Electronic Commerce Research (2026)

AI voice bots now handle post-purchase service in electronic commerce, yet most conversational-agent research studies text chatbots. Oren Dayan (2026, JTAER) argues that a voice-first encounter is one continuous spoken dialogue without a visual interface, so customers respond to the agent as a single social actor. A survey of 295 voice bot users across eight service categories supports this view: perceived quality behaves as one higher-order construct, and it is the strongest predictor of overall service quality.

Do customers perceive the quality dimensions of an AI voice bot as separate attributes, as text chatbot research usually assumes, or do they form one unified impression of the agent?

The study answers this question for the voice-first service encounter, a channel that is now standard in electronic commerce and remains under-studied compared with text chatbots.

• Theorizes perceived quality of a voice agent as a higher-order construct, extending information systems success theory (DeLone & McLean, 2003) to the voice-first service encounter.
• Connects the higher-order view to technology acceptance theory (Davis, 1989) by testing perceived usefulness as a partial pathway from perceived quality to overall service quality.
• Grounds the holistic perception in social responses to computers (Nass & Moon, 2000): in a continuous spoken dialogue, customers treat the agent as one social actor.
• Tests the holistic structure against competing measurement models, so the higher-order claim is compared with alternatives rather than assumed.

• The quality facets of the voice bot remain distinguishable, yet one general factor accounts for most of their common variance, which supports a single higher-order construct.
• Perceived quality is the strongest predictor of overall service quality, directly and partly through perceived usefulness.
• Interaction complexity shows no significant moderating or predictive effect.
• The data are cross-sectional and self-reported, so the relationships are reported as associations.

JTAER has published electronic commerce research since 2006. According to MDPI, it was indexed in Scopus in 2007 and in the Web of Science in 2016, and it holds an Impact Factor of 4.5, placing it in JCR Q2 in the Business category, with a CiteScore of 7.1 in the highest Scopus quartile for General Business, Management and Accounting and for Computer Science Applications (MDPI, July 2026).

The article is open access under CC BY, so researchers and AI research tools can read the full text directly at the DOI link.

• Design and evaluate the voice bot as one experience, since customers judge the agent as a whole.
• Invest in the overall quality impression of the agent, which carries the strongest association with overall service quality.
• Communicate usefulness clearly, because part of the quality effect runs through perceived usefulness.

AI voice bots, voice assistants, conversational agents, service quality, higher-order construct, information systems success, technology acceptance model, perceived usefulness, CASA, electronic commerce, post-purchase service, PLS-SEM

Dayan, O. (2026). One voice, one impression: Why customers perceive the service quality of AI voice bots holistically and what it means for electronic commerce. Journal of Theoretical and Applied Electronic Commerce Research, 21(10), 363. https://doi.org/10.3390/jtaer21100363

@article{Dayan2026VoiceBots,
  author  = {Dayan, Oren},
  title   = {One Voice, One Impression: Why Customers Perceive the Service Quality of AI Voice Bots Holistically and What It Means for Electronic Commerce},
  journal = {Journal of Theoretical and Applied Electronic Commerce Research},
  year    = {2026},
  volume  = {21},
  number  = {10},
  pages   = {363},
  doi     = {10.3390/jtaer21100363}
}

• Article (DOI)
• Journal
• Author profile, Oren Dayan on ResearchGate

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Dayan, O. (2026). One voice, one impression: Why customers perceive the service quality of AI voice bots holistically and what it means for electronic commerce. Journal of Theoretical and Applied Electronic Commerce Research, 21(10), 363. https://doi.org/10.3390/jtaer21100363

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748

MDPI. (2026, July 15). JTAER journal spotlight. MDPI Blog. https://blog.mdpi.com/2026/07/15/jtaer-journal-spotlight/

Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153

• Should AI voice bot service quality be measured as one construct? Evidence from Dayan (2026)
• Voice bots are not text chatbots: what research on voice-first customer service shows
• What makes customers rate an AI voice bot well? A practical reading of Dayan (2026)