The Service Industries Journal

Innovative Research Methodologies in Tourism, Hospitality and Service Research

Tourism, hospitality and service phenomena are inherently dynamic. They unfold through journeys, encounters and relationships, often taking embodied and emotional forms while also being mediated by platforms, algorithms, immersive interfaces, and intelligent service technologies. Their consequences emerge over time (e.g., objective and subjective experience of time) and across places through interactions (with customers, employees, and organisations), and service interfaces (e.g., physical, digital, virtual) (Singh & Bridge, 2023). Many of the field’s central phenomena are therefore difficult to observe through any single methodological lens.

Established methods remain indispensable, but familiar research routines may not fully reveal these dimensions of tourism, hospitality and service domains. Cross-sectional surveys can flatten temporal change, self-reports may overlook rapid or implicit responses, and text-based interviews may underrepresent sensory, spatial and embodied experience. Meanwhile, the proliferation of digital traces, reviews, images, video, sensor streams and platform data has created an abundance of information without necessarily producing deeper understanding. Recent service research demonstrates how methodological innovation can help address this challenge. Machine learning and topic modelling have been used to analyse large bodies of service research (Antons & Breidbach, 2018), while natural-language processing and novel qualitative analysis have been integrated to identify service research priorities (Ostrom et al., 2021). Given the rapid technological, societal and methodological change Mende et al. (2026) introduced an AI-assisted research agenda for the service discipline. They integrated LLMs with machine learning and identified four critical research clusters to extend service research in future. Emerging work also compares human and synthetic data in service research (Bickley et al., 2025). In tourism and hospitality, generative AI has supported qualitative analysis (Sun et al., 2025), while configurational approaches have enabled closer examination of causal complexity and necessary conditions (Rasoolimanesh, 2026).

Against this background, the Special Issue positions methodological innovation as a means of understanding difficult service phenomena rather than as an end in itself. Method innovation may involve developing a new approach, adapting a method from another discipline, combining established methods in a consequential research design, or extending and critically reassessing existing techniques (Lê & Schmid, 2022). We therefore welcome methodological pluralism across qualitative, quantitative, computational, experimental, participatory and mixed-method traditions, with innovation potentially occurring in data generation, measurement, analysis, integration or the representation of findings.

Aims and scope

We invite conceptual and empirical papers that develop, adapt, combine, apply, compare or critically evaluate innovative research methodologies in tourism, hospitality and wider service research. Submissions should show how the contribution deepens understanding of service phenomena, advances theory or overcomes limitations in established research practices. Areas of interest include but not limit to:

1. Technology-enabled, computational and data-intensive research. We welcome research using artificial intelligence as an instrument, simulated participant or analytical infrastructure.

2. Methods for embodied, affective, implicit dimensions of experience. Contributions may employ response-latency methods, the implicit association test, eye tracking, facial coding, electrodermal activity, electroencephalography and other biometric, psychophysiological or neuroscientific tools to examine responses that participants may be unable or unwilling to articulate (Fazio, 1990; Greenwald et al., 1998; Li et al., 2023).

3. Creative, visual and participatory qualitative methods. Drawing on qualitative traditions across the social sciences, we welcome methods that move beyond conventional verbal interviews to elicit tacit, sensory, spatial or difficult-to-articulate experiences, including LEGO® Serious Play®, visual elicitation, photovoice, participant-generated imagery sensory or mobile ethnography, walking interviews, projective techniques, collaborative mapping and arts-based methods (Harper, 2002; Rose & Furness, 2024).

4. Methods for studying dynamics, causal complexity and heterogeneity. This area includes, but is not limited to, longitudinal, processual and experience-sampling research; temporal and sequence analysis; configurational and asymmetric approaches such as QCA (e.g., fsQCA and csQCA), necessary condition analysis (NCA) and necessary configuration analysis (NConfA); causal machine learning; and Bayesian, multilevel, network, simulation and agent-based modelling methods.

5. Integrative, reflexive and responsible methodological designs. We welcome mixed- and multi-methods of research that bring together qualitative, quantitative, experimental, computational, archival, behavioural or physiological evidence.

6. Using Item Response Theory analysis. Most of the scales developed and published in tourism, hospitality and services domains are based on the classical test theory (CTT) which has its own limitations. We welcome submissions using the item response theory (IRT) (Hambleton et al., 1991).

References

Antons, D., & Breidbach, C. F. (2018). Big Data, Big Insights? Advancing Service Innovation and Design With Machine Learning. Journal of Service Research, 21(1), 17-39. https://doi.org/10.1177/1094670517738373

Bickley, S. J., Chan, H. F., Dao, B., Torgler, B., Tran, S., & Zimbatu, A. (2025). Comparing human and synthetic data in service research: Using augmented language models to study service failures and recoveries. Journal of Services Marketing, 39(1), 36-52. https://doi.org/10.1108/JSM-11-2023-0441

Fazio, R. H. (1990). A practical guide to the use of response latency in social psychological research. In C. Hendrick & M. S. Clark (Eds.), Review of Personality and Social Psychology, 11, 74–97. Sage.

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464

Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26. https://doi.org/10.1080/14725860220137345

Hambleton, R. K., Swaminathan, H., & Rogers, J. H. (1991). Fundamentals of item

response theory. Newbury Park: Sage Publications.

Kirilenko, A. P., & Stepchenkova, S. (2025). Facilitating topic modeling in tourism research: Comprehensive comparison of new AI technologies. Tourism Management, 106, 105007. https://doi.org/10.1016/j.tourman.2024.105007

Lê, J. K., & Schmid, T. (2022). The practice of innovating research methods. Organizational Research Methods, 25(2), 308–336. https://doi.org/10.1177/1094428120935498

Li, S., Chark, R., Bastiaansen, M., & Wood, E. (2023). A review of research into neuroscience in tourism: Launching the Annals of Tourism Research curated collection on neuroscience in tourism. Annals of Tourism Research, 101, 103615. https://doi.org/10.1016/j.annals.2023.103615

Mende, M., Ostrom, A., Baek, J., Shin, D., Lee, S., & Scott, M. L. (2026). The 2026 Service Research Priorities: AI-Driven Discovery, Interdisciplinary Influence Dynamics, and the “Interactive Service Scholarship Incubator” for Personalized Priorities. Journal of Service Research, https://doi.org/10.1177/10946705261447646.

Ostrom, A. L., Field, J. M., Fotheringham, D., Subramony, M., Gustafsson, A., Lemon, K. N., ... & McColl-Kennedy, J. R. (2021). Service research priorities: managing and delivering service in turbulent times. Journal of Service Research, 24(3), 329-353. https://doi.org/10.1177/1094670521102191

Rasoolimanesh, S. M. (2026). Applying fsQCA, NCA and NConfA in tourism research. Annals of Tourism Research, 116, 104069. https://doi.org/10.1016/j.annals.2025.104069

Rose, S., & Furness, P. J. (2024). Lego Serious Play in psychology: Exploring its use in qualitative interviews. Qualitative Research in Psychology, 21(4), 578–609. https://doi.org/10.1080/14780887.2024.2388610

Singh, J., & Bridge, R. G. (2023). Interfaces, interactions, time, and the frontline nexus: foundational constructs and focus for the field of organizational frontlines. Journal of Service Research, 26(3), 310-329.

Sun, H., Kim, M., Kim, S., & Choi, L. (2025). A methodological exploration of generative artificial intelligence (AI) for efficient qualitative analysis on hotel guests’ delightful experiences. International Journal of Hospitality Management, 124, 103974. https://doi.org/10.1016/j.ijhm.2024.103974