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Open research questions in AI in Service Interactions

1,253 gap statements mined from AI in Service Interactions papers in our 7.9M-paper local library, which holds 4,757 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The current research was limited to the following parameters: 1- Subject Matter: The subject matter focused on the practical component of the “Digital Platforms” course. Unlimited Knowledge for Publishing and Distribution. Finally, this study addresses a significant gap in the literature by examining digital entrepreneurship and future-oriented problem-solving within a single educational intervention.

    Enhancing Higher-Order Cognitive Abilities Through AI-Powered Smart Assistants: Implications for Digital Entrepreneurship and Future Problem-Solving in Higher Education · 2026 · DOI

  • These different modes of platform-dependence need to be further investigated in future research; comparative, longitudinal studies on third parties with different resources should show how market power and infrastructural resources shape complementors’ agency, dependence, and exit options.

    The complementor perspective: Examining how Dutch developers and industry professionals navigated Google Assistant as a platform (2018–2021) · 2026 · DOI

  • Future research should examine these relationships in clinical populations and among older adults with active health concerns.

    Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: Randomized Controlled Experimental Study · 2026 · DOI

  • Future research should pursue the following specific directions. First, future studies should test more substantive cognitive interventions beyond text highlighting, specifically uncertainty indicators that display the AI’s confidence or uncertainty alongside its output, to determine whether these engage users at a deeper evaluative level.

    Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: Randomized Controlled Experimental Study · 2026 · DOI

  • Future research could address these limitations through longitudinal and experimental designs. Future studies may investigate whether or not privacy mediates emotional credibility and emotional response or satisfaction and purchase intent.

    Emotional Responses to AI Chatbots in Online Travel Agencies: An S-O-R Perspective · 2026 · DOI

  • While research shows that AI can positively influence learning experiences and outcomes, systematic explanations of how AI affects learners’ mental health remain lacking (Klug & Maier, 2015).

    Smarter AI, Healthier Students? How Perceived AI Assistant Intelligence Shapes Medical Students’ Mental Health Through Learning Goal Progress and Academic Anxiety · 2026 · DOI

  • For their part, the disclosure hypotheses were not supported: the results of the equivalence tests suggest that, within the limits of the present design, stating the origin of the advertisement did not produce a performance deterioration exceeding 10% for reach, video plays, and clicks; for messages, equivalence at ±10% was not established and the evidence remains inconclusive (Appendix A, Table A6).

    Human Spokesperson Versus Artificial Intelligence Avatar and Authorship Disclosure in Digital Advertising: Effects on Performance Metrics · 2026 · DOI

  • Given the orthogonal nature of its underlying dimensions, this resulted in an expectedly low average variance extracted (AVE), representing a measurement limitation; future research should consider respecifying the Big Five formatively or disaggregating it into separate predictors.

    The Influence of Artificial Intelligence, Social Media, and Personality Traits on Purchase Decision-Making Among Generation Z University Students · 2026 · DOI

  • The absence of a direct effect in Sierra Leone reveals a critical boundary condition on CASA: in fragile economies marked by institutional fragility and weak data governance, surface-level social heuristics are insufficient to drive adoption.

    Anthropomorphic AI and Consumer Skepticism: A Behavioral Study of Trust and Adoption in Fragile Economies · 2026 · DOI

  • The lack of rigorous methods for deciding which generative AI applications to prioritise in sustainable tourism. The scarcity of evaluations of generative AI in real operating settings, with most studies remaining at the proof-of-concept stage.

    Not just hype: a multi-criteria decision framework for ranking generative AI strategies in sustainable tourism · 2026 · DOI

  • To mitigate this, future work will explore adaphttps://doi. In parallel, future work may explore adaptive calibration strategies for domain boundaries and clarity thresholds, as well as pathways for generalization beyond poultry production.

    A Decision Support AI-Copilot for Poultry Farming: Leveraging Retrieval-Augmented LLMs and Paraconsistent Annotated Evidential Logic Eτ to Enhance Operational Decisions · 2026 · DOI

  • This pattern suggests that technical competence functions as a necessary but insufficient condition for acceptance: AI-generated music must be perceived as technically adequate, yet this perception alone does not account for the richness of acceptance variance.

    When Algorithms Create Culture: An Integrative Model of Consumer Acceptance of AI-Generated Music · 2026 · DOI

  • Future studies could examine these relationships in live interactions while documenting the response errors and recovery content that participants actually encounter. Their generalizability to deployed LLM chatbots and real human handoffs remains to be established.

    The impact of AI hallucination on the chatbot service quality in e-commerce: the moderating role of human agent based service recovery · 2026 · DOI

  • The study identifies the challenge of uniform deployment of AI in tourism, which risks alienating segments of the market that remain sceptical or anxious. The paper notes the challenge of identifying complex patterns in consumer behaviour using traditional segmentation approaches.

    Mapping the Attitudinal Continuum Toward AI in Tourism: An Explainable Segmentation of a Croatian Consumer Sample · 2026 · DOI

  • Limited attention to psychological mechanisms underlying user experience. Lack of understanding of the effects of chatbot characteristics on user satisfaction. Need for a model that differentiates between affective and cognitive processes.

    How Behavioral and Normative Characteristics Influence User Satisfaction with Gen-AI Chatbots: The Mediating Roles of Emotional Empathy and Proficiency Evaluation · 2026 · DOI

  • DTPB served as a conceptual lens rather than a quantitatively tested model, and the proposed extension of “Perceived Need” remains to be operationalized and validated through larger-scale measurement.

    Students’ Use and Perception of Educational GenAI Chatbot in High School Computing: Insights from the Decomposed Theory of Planned Behavior · 2025 · DOI

  • Moreover, our study focused solely on medical students, and it remains unclear whether findings would translate similarly to students from other disciplines with different learning needs, curricular structures, or baseline familiarity with AI tools.

    Evaluating a Custom Chatbot in Undergraduate Medical Education: Randomised Crossover Mixed-Methods Evaluation of Performance, Utility, and Perceptions · 2025 · DOI

  • Future research should focus on these characteristics, analyzing how advanced multimodal AI adoption affects long-term educational outcomes and how to best use these technologies at scale in a variety of educational settings.

    Artificial Intelligence and Immersive Technologies: Virtual Assistants in AR/VR for Special Needs Learners · 2025 · DOI

  • One important avenue for future work is the integration of additional user groups into the chatbot system, particularly by developing a chatbot for administrators. An admin chatbot could assist administrators during routine tasks such as managing data entries and generating reports from the database.

    Impact of Chatbots on User Experience and Data Quality on Citizen Science Platforms · 2025 · DOI

  • Amid a wave of conversational artificial intelligence rapidly reorganizing everyday work, the deep question for the psychology of work is not whether artificial systems will replace employees but what kind of colleagues humans are becoming to the machines and, through them, to one another.

    From Tool to Social Actor: A Systematic Review of the Psychological Mechanisms Through Which Conversational AI Reshapes the Employee Experience · 2026 · DOI

  • Cost, general ethical concern, and subjective job-displacement anxiety were common. The majority preferred conversational tools over search engines for information retrieval. These statements describe this sample. They are not generalised to other UK professionals.

    Perceptions and Barriers in Workplace Artificial Intelligence Adoption Among Industry Professionals: A Cross-Sectional Descriptive Survey · 2026 · DOI

  • Experimental results show several strengths, especially the flawless accuracy of the Semantic Firewall, which greatly reduces inappropriate advice and regulatory violations.

    Semantic Firewalls with Online Ensemble Learning for Secure Agentic RAG Systems in Financial Chatbots · 2026 · DOI

  • Specifically, the construct captures rationale-based transparency (i.e., explanations regarding why a recommendation is provided), feature-based transparency (i.e., information about which user attributes or behaviors influenced the recommendation), and data-based transparency (i.e., explanations regarding how user data is collected and used).

    AI Transparency and User Behavior in Human–AI Collaboration: Evidence from E-Commerce Recommendation Systems · 2026 · DOI

  • This evolution demonstrates that Generative AI can serve not only as a technological enhancement but as a catalyst for pedagogical innovation.

    Enhancing Language Learning with Generative AI: The Case of the OpenLang Network Platform · 2025 · DOI

  • This study aims to fill a critical gap in understanding the antecedents of this AI dependency by investigating the roles of instant gratification preference and perceived AI efficacy.

    The Double-Edged Sword of AI Efficiency: Self-Efficacy Erosion as a Mediator Linking Instant Gratification and Perceived AI Efficacy to AI Dependency · 2026 · DOI

Most-cited papers in AI in Service Interactions

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Open questions raised by papers listed on this page include: (1) Future research should examine these relationships in clinical populations and among older adults with active health concerns (“Trust in Generative AI for Health Information Consumption and the Effect of…”, 2026); (2) Future research could address these limitations through longitudinal and experimental designs (“Emotional Responses to AI Chatbots in Online Travel Agencies: An S-O-R…”, 2026); (3) The lack of rigorous methods for deciding which generative AI applications to prioritise in sustainable tourism (“Not just hype: a multi-criteria decision framework for ranking generative AI…”, 2026); (4) In parallel, future work may explore adaptive calibration strategies for domain boundaries and clarity thresholds, as well as pathways for generalization beyond poultry production (“A Decision Support AI-Copilot for Poultry Farming: Leveraging…”, 2026); (5) Future studies could examine these relationships in live interactions while documenting the response errors and recovery content that participants actually encounter (“The impact of AI hallucination on the chatbot service quality in e-commerce:…”, 2026). 1,253 gap statements have been mined from AI in Service Interactions papers in our 7.9M-paper local library, which holds 4,757 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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