Call for Paper for a Special Issue for the Service Industries Journal.
Benefits, Challenges and Ethical Implications of Using (Gen) AI in Service Research
Guest-editors: Do TK, Chan KW & Kaehne A
Deadline: 5 February 2026
The rapid advancements of artificial intelligence (AI), particularly generative AI (Gen AI), have transformed service research paradigms and methodologies, radically influencing how service scholars in this field generate new ideas, conduct literature review, collect and analyze data, and write papers (Sigala et al., 2024). Gen AI-powered tools, including large language models (LLMs) like ChatGPT, Google Gemini, Microsoft Copilot, or DeepSeek, are now increasingly used to automate repetitive research tasks, support theoretical development, and improve qualitative and quantitative analyses in service studies. While AI holds great promise for fostering research productivity, its uncritical application also raises concerns about the originality and transparency of AI-generated insights in the service field.
The integration of AI in service research could offer multiple benefits. AI enhances systematic literature reviews by processing vast unstructured scholarly datasets, identifying trends, and summarizing key themes efficiently (Yoo et al., 2025). Many Gen AI models like Sora (OpenAI), Midjourney, or Stable Diffusion are also great tools for generating videos and images for experimental design (Blanchard et al., 2025)–an increasingly used method in service research (Leung et al., 2024). In data collection, AI-generated synthetic data (qualitative customer reviews or quantitative responses for surveys and experiments) can supplement traditional methods, providing scalable and cost-effective solutions for many service researchers (Sarstedt et al., 2024), especially when collecting data from real consumers is challenging (e.g. disabled consumers, the elderly). Furthermore, AI-driven sentiment and thematic analyses improve qualitative research by reducing human bias (Jalali, & Akhavan, 2024), while advanced reasoning models, such as the recent launch of OpenAI-o1, refine quantitative predictions for many service sectors. Gen AI can also be utilized for proofreading and copyediting as a cost-effective and widely accessible tool for many service researchers, particularly those who face research budget constraints.
Despite these potential advantages, the use of AI, especially Gen AI, in service research presents significant challenges and ethical concerns. AI-generated data may lack contextual understanding (Jalali & Akhavan, 2024), along with faked and non-referenced sources (Haman & Školník, 2023), raising questions about the validity and accuracy of AI-generated outputs. Given the “black-box” nature of AI, the governance of AI ethics also remains a pressing problem, particularly in ensuring transparency, scientific integrity, and accountability when using AI-generated insights (Moffatt & Hall, 2024). Additionally, over-reliance on AI may erode critical thinking and reflexivity among researchers, impacting individuality and creativity in service scholarship (Huang & Rust, 2025; Lindebaum & Fleming, 2023). Establishing guidelines for the ethical and responsible use of AI in service research is therefore essential to ensure academic integrity and inclusivity in our field.
Topics of interest for this SI include, but are not limited to:
– Using (gen) AI for conducting literature review (e.g. systematic review, bibliometric analysis)
– Using (gen) AI for data collection (e.g. synthetic data, silicon samples)
– Using (gen) AI for qualitative data analysis (e.g. thematic and sentiment review analysis)
– Using (gen) AI for quantitative data analysis (e.g. forecasting, experimental analysis)
– Ethical and responsible use of (gen) AI for service research
– The dark side of (gen) AI in service research (e.g. fabrication of data and references)
– Governance of ethics for (gen) AI use for service research
– AI, inclusivity and service research
– Use of AI and service research productivity
– Use of AI and well-being of service researchers
– Co-creation of research ideas through service industry-academia collaboration leveraged by gen AI platforms
– Integration of gen AI to service early career research training
– New knowledge creation in service with gen AI
Submission Instructions
All submitted manuscripts should follow the submission instructions provided by The Service Industries Journal on its website.
Type of paper
With growing preference for a multi-method approach in our field, this special issue welcomes both conceptual manuscripts (e.g. systematic review) and empirical manuscripts applying various qualitative and quantitative methods to study this timely topic, such as interview, ethnography, text mining, secondary data analysis, survey, experiment, quantitative modelling, machine learning, etc.
Timeline
Abstract submission: 30 September 2025 (500-word extended abstract to state the aim, expected contributions, and intended type of paper, i.e. empirical or conceptual
Abstract decision: 5 November 2025
Full paper submission: 5 February 2026
Revision and final decision: 10 July 2026
Guest Editors:
The Khoa Do (Bin), Royal Holloway-University of London
Kimmy Wa Chan, Hong Kong Baptist University
Axel Kaehne, Edge Hill University
