1. (my biased thoughts on)
Enter the levels
(my biased thoughts on)
Recent trends in quantitative research in the field of tourism and hospitality
Nemanja Stanisic

2. The past
The future
Traditional surveys (paper questionnaires, interviews)
Small, simply structured samples
Single level models: linear regression, ANOVA, Structural equation modeling (SEM)
Simple, single-level questions
Research cannot be reproduced, modified, or built upon
Big data (crowdsourced)
Large samples with complex structures (repeated observations and hierarchical structures)
Multilevel models: multilevel regressions, Bayesian multilevel models, multilevel SEM
Complex, ‘multilevel’ questions
Research can be easily reproduced, modified, and built upon

3. Big data: TripAdvisor.com
3,488,473 customer reviews
provided by 2,233,671 unique registered TripAdvisor users of 210 different nationalities
relating to 13,410 hotels located in 80 capital cities around the globe.
a comprehensive list of attributes of all the reviewed hotels, the reviewer’s TripAdvisor rank and nationality, the review-specific type of travel, and the date of the review (between 31 August and 15 May 2015).

4. Data structure: We live in a multilevel world
Observations are clustered within the reviewers, within their nationalities, within the hotels as well as within the destinations.
There is a hierarchical structure in the data (reviewers may have only one nationality, and hotels may be located in only one destination).
Factors that affect customer satisfaction operate at different levels.

5. Data mining? Machine learning?
The initial reaction to the abundance of data was the adoption of data mining and machine learning techniques, which resulted in numerous inspiring research papers.
The capability of these tools to fully address complex research questions and, hence, contribute to the existing body of theoretical knowledge is limited, however.
While being highly efficient in detecting patterns and selecting a subset of relevant explanatory factors, these tools typically do not prompt the researcher to either define the structures present in the data or to include any prior information that might be relevant to the research problem at any point of the research process. Disregarding these important pieces of information results in suboptimal performance of the effect estimation, including biased estimates.
Furthermore, these tools typically stay opaque with regard to the underlying causal mechanisms (the “black box” effect).

6. Multilevel modeling
Multilevel modeling framework seems to be the next step forward due to some important advantages:
It accounts for the structures and hierarchical relationships present in the data.
It allows partitioning of the total variance in the values of the dependent variable into between- and within-unit components, which may be particularly interesting from a theoretical perspective.
It allows the inclusion of level-specific explanatory variables (such as destination- or nationality-specific variables) without committing ecological fallacy.
It allows for the fact that the examined effects might not be identical across units (e.g., properties, destinations, reviewers, nationalities) and even possibly correlated with the unit-specific effects.

7. Future research
Examples of prospective research questions that can be easily addressed within the multilevel framework:
Are there significant differences between the average rating scores across the destinations? (allow the intercepts to vary across the destinations)
Does the importance (indicating the reliability) of star classification vary across the destinations? (allow the slopes for the star classification variable to vary across the destinations)
Do hotels located in destinations that have implemented more reliable star classification systems get higher ratings on average? (examine the correlation between the random intercepts and the random slopes)
Can the differences in the reliability of the star classification systems implemented in the destinations be explained by their respective cultural characteristics (e.g., uncertainty avoidance)? (cross-level interaction between the cultural dimension variables and the star classification variable)

8. Research reproducibility and extensibility
For reproducibility purposes, the complete dataset and program code used for the analysis will be made publicly available through the MendeleyData service as soon as the accepted paper (listed under no. 3 on the next slide) is published online.
We encourage researchers to extend and build upon the existing multilevel platform or use the data set in even more creative ways.
Some of the promising approaches: Bayesian multilevel modeling, SEM multilevel

9. References
Radojevic, T., Stanisic, N., & Stanic, N. (2015). Ensuring positive feedback: Factors that influence customer satisfaction in the contemporary hospitality industry. Tourism Management, 51, 13–21.
Radojevic, T., Stanisic, N., & Stanic, N. (2015). Solo travellers assign higher ratings than families: Examining customer satisfaction by demographic group. Tourism Management Perspectives, 16, 247–
Radojevic, T., Stanisic, N., & Stanic, N. (2017). Inside the Rating Scores: A Multilevel Analysis of the Factors Influencing Customer Satisfaction in the Hotel Industry. Cornell Hospitality Quarterly, in press.