Theoretical perspectives on Generative and Agentic AI adoption in service environments

Closes:
Journal
Journal of Services Marketing
Guest editor(s)
Mark Anthony Camilleri, Levent Altinay, Sang M. Lee, Cheng Lu Wang,

Introduction

Generative Artificial Intelligence (GenAI) and Agentic Artificial Intelligence (Agentic AI) are transforming how services are designed, delivered, experienced and led. While GenAI refers to systems, such as large language models (LLMs), that produce content in response to human prompts; Agentic AI technologies may be considered as active agents that can implement tasks (rather than merely functioning as passive generators) (Acharya et al., 2025). The latter can monitor situations, allocate resources, initiate and manage processes as well as co-ordinate multiple activities (Gonzalez et al., 2026). Hence, Agentic AI algorithms and their governance affect service outcomes.

Generative AI capabilities often constitute the communicative and cognitive foundations of Agentic AI. In other words, many Agentic AI systems rely on GenAI models to reason, communicate and interact. Together, these AI technologies challenge conventional assumptions about agency, control, responsibility and value creation in service environments (Ferraro et al., 2024; Wirtz & Stock-Homburg, 2025). Unlike earlier forms of automation and analytics, these AI systems can engage in social interactions, reason in a contextual manner and may dynamically adapt to changing situations. As such, they raise profound theoretical questions about anthropomorphism, social presence, trust, autonomy, creativity, emotion, accountability, responsibility and moral agency (Banh & Strobel, 2023; Ng et al., 2026; Sun et al., 2026).

These capabilities indicate that Generative and Agentic AI represent more than incremental advances in automated technologies. They introduce different forms of interaction and agency that cannot be fully explained by utility-driven adoption frameworks (Camilleri, 2024). Consequently, there is a growing need for theory-driven and conceptually rigorous research that explains how, why and under what conditions Generative and Agentic AI are deployed, adapted, governed, or even resisted in service environments.

This special issue seeks to advance services marketing research by encouraging scholars to utilize, extend, integrate or critically evaluate existing theories to investigate user engagement with Generative and Agentic AI across diverse service settings. In this light, the guest editorial team particularly welcomes submissions that move beyond descriptive accounts. Prospective contributions are expected to offer strong theoretical explanations of AI acceptance and usage in services.

 

Theoretical perspectives

The editors of this special issue particularly welcome submissions that explicitly draw upon, refine or combine well-established theories that have been influential in service and technology research, including (but not limited to) the following ones (as discussed in Camilleri & Troise, 2023):

  • Anthropomorphism theory (e.g., human-likeness, emotional attachment and/or moral attributions to AI).
  • Affordance theory (perceived action possibilities enabled or constrained by GenAI and/or Agentic AI interfaces).
  • Assemblage theory (AI as part of dynamic socio-technical service systems).
  • Behavioral reasoning theory (reasons for and against AI use in service encounters).
  • Cognitive fit theory (task–AI alignment and decision quality).
  • Commitment–consistency theory (habit formation and sustained AI use).
  • Communication accommodation theory (linguistic and stylistic adaptation in human–AI interaction).
  • Contingency theory (contextual conditions that can have an impact on AI effectiveness).
  • Diffusion of innovations theory (organizational and market-level adoption trajectories).
  • Expectancy and expectation-violation theories (surprise, delight, discomfort or distrust in AI services).
  • Flow theory in computer-mediated environments (engagement, creativity and immersion).
  • Functionalist theory of emotion (affective responses to AI-enabled services).
  • Human–computer interaction / human–machine communication theories.
  • Information systems success model (service quality, satisfaction and net benefits of AI).
  • Politeness theory (face-management and social norms in AI communication).
  • Self-determination theory (autonomy, competence and relatedness in AI use).
  • Situational theories of problem-solving and publics.
  • Social cognitive theory (learning AI use through observation and social influence).
  • Social presence and social response theories.
  • Structural role theory (AI as role-performing service actors).
  • Technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT).
  • Theory of conversation.
  • Theory of planned behavior (TPB) and its related theory of reasoned action (TRA).
  • Trust–commitment theory.
  • Uses and gratifications theory.

Submissions that integrate multiple perspectives, compare existing conceptual frameworks and develop new theoretical models specific to GenAI and Agentic AI in services are especially encouraged for this special issue.

Illustrative research questions may include (but are not limited to): How and to what extent do customers and employees anthropomorphize Generative versus Agentic AI in service encounters? Which GenAI and Agentic AI affordances drive value co-creation, trust, reliance or resistance in services? How do emotional cues, social presence and politeness strategies influence engagement with AI-driven service agents? Under what contingencies does AI adoption enhance or undermine service quality, relationships and well-being? How do expectations and expectation violation aspects influence satisfaction and continued use of AI-enabled services? How do organizations implement Agentic AI within broader service systems? What ethical, relational, psychological and accountability tensions emerge from sustained human–AI interactions, particularly when AI acts autonomously?

The special issue welcomes conceptual, qualitative, quantitative, experimental or mixed-methods approaches, provided that the contributing authors demonstrate strong theoretical grounding and relevance to the underlying objectives of this journal.

 

List of topic areas

  • Theoretical perspectives on Generative and Agentic AI adoption in service environments.
  • Comparative or multi-theoretical frameworks for studying human-AI interaction in services.
  • Anthropomorphism, social presence and human-AI relationships.
  • Perceived affordances, interface design and service experiences.
  • Emotions, expectations and psychological responses to AI.
  • Adoption, acceptance and continued use of AI in services.
  • Trust, ethics, accountability and relational governance.
  • AI as a service actor within socio-technical systems.
  • Contextual and contingency-based perspectives.
  • Value co-creation, value co-destruction and service outcomes.
  • Organizational, strategic and policy implications of Generative and Agentic AI in services.

 

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available here.

Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/jsm

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

 

Key deadlines

Opening date for manuscripts submissions: 23/06/2026

Closing date for manuscripts submission: 26/02/2027

Email for submissions: [email protected]