Call for Papers for a Special Issue of the Journal of Service Research.
AI-Enabled Experience Innovation
Guest Editors: Schmitt B, Huang MH, Sood S & Hao S
Submission deadline: 30 September 2026
AI is innovating how service experiences can be designed, curated, and delivered. Service that emphasizes experiential components, such as sensory, affective, cognitive, behavioral and social elements, are considered experiential service (Schmitt 1999). Customer experience is multidimensional across multiple touchpoints (Verhoef et al. 2009), unfolds over the customer journey (Lemon and Verhoef 2016), can be enabled by various AI technologies (Ostrom et al. 2021), and can establish emotional connections with customers (Huang and Rust 2024).
This special issue seeks to advance our understanding of how AI can enable experience innovation. By enabling personalization, adaptive interaction, and multisensory engagement, AI allows service providers to create new, innovative experiences and enables customers to participate in journeys that evolve dynamically with their needs and preferences. For examples, augmented and virtual reality technologies (AR/VR) are used in retailing to innovate experiences; extended-reality technologies (XR) are used to bring static museum exhibitions to life, mixed-reality technologies (MR) are used to blend real and virtual skill development, and AI technologies are used to enhance sports or entertainment experiences through digital images and surround sound.
The special issue welcomes submissions from all disciplines with a service interest, such as consumer research, service marketing, strategic management, information systems, operations research, psychology, and sociology. Mixed methods blending conceptual, empirical, or computational approaches are encouraged. Potential topics include, but are not limited to:
– How do AI technologies (e.g., generative AI, agentic AI, AR, VR, XR, MR) innovate service experiences, and what design principles help service providers create engaging and meaningful experiences?
– How should AI-enabled experiences be orchestrated across physical, digital, and extended-reality touchpoints throughout the service journey?
– How can service providers create and scale AI-enabled experience innovations for human customers, hybrid human-AI customers, or autonomous AI customers to generate favorable outcomes for customers, providers, and ecosystems?
– What new experiential consequences and innovation outcomes arise from AI, and how can they be conceptualized, measured, and managed over time?
– What ethical, societal, or governance challenges arise and how can AI-enabled experience innovation be designed, governed, and managed responsibly?
Process and Timeline:
Papers will undergo no more than two rounds of full peer review. After the second round, a final decision will be made to ensure timely publication. The special issue operates on a tight timeline, and authors should expect quick turnarounds for reviews and revisions. Papers may be submitted from August 1 to September 30, 2026. The special issue is planned to be published in August 2027, and accepted papers will be published online first.
Special Issue Editors:
– Bernd Schmitt, Columbia University (bhs1@gsb.columbia.edu)
– Ming-Hui Huang, National Taiwan University (huangmh@ntu.edu.tw)
– Sanjay Sood, University of California, Los Angeles (sanjay.sood@anderson.ucla.edu)
– Shuyi Hao, ICN Business School & CEREFIGE (shuyi.hao@icn-artem.com)
I have completed a preliminary draft of an article titled：
“Beyond Reactive AI Service: How Proactive Interaction Signals Shape Consumer Responses to Digital Humans and Chatbots”.
The main content is as follows：
“With the rapid deployment of AI service agents in frontline customer service, understanding how proactive interaction strategies influence consumer responses has become a critical priority. Drawing on matching theory, this research examines how service agent type and proactive interaction signals jointly shape consumer interaction intention and underlying cognitive-evaluative processes. We propose that agent–signal congruence enhances interaction intention through two service experience pathways: social presence and perceived usefulness.
Two studies were conducted to test the proposed framework. Study 1, a 2 × 2 between-subjects survey experiment (N = 180), demonstrated a significant interaction between agent type and proactive signal type. Enjoyment-oriented signals generated higher interaction intention when delivered by digital humans, whereas effort-oriented signals were more effective when delivered by chatbots. Mediation analyses further showed that perceived usefulness was the primary mechanism underlying the matching effect, while social presence played a weaker and context-dependent role.
Study 2, a within-subjects ERP experiment (N=35), provided convergent neurophysiological validation. Behavioral results were generally consistent with the matching framework proposed in Study 1. ERP findings revealed that the three-stage temporal architecture of consumer processing includes early attentional differentiation (N2), intermediate motivational evaluation (P3), and late semantic elaboration (LPC). These findings demonstrate that consumers implicitly discriminate between AI service agent types and proactive signal meanings at multiple, successive levels of cognitive processing.
This research contributes to AI service literature by extending matching theory to proactive AI communication, identifying perceived usefulness as the primary mechanism through which agent–signal congruence influences interaction intention, and uncovering the temporal neural processes underlying consumers’ responses to proactive AI service interactions. The findings offer practical guidance for designing proactive communication strategies in AI-enabled frontline services.”
I really hope to submit a paper to the Special Issue of the Journal of Service Research. I would like to inquire about the specific procedures, such as how to submit the manuscript and any suggestions for revisions. Thank you very much for your hard work.