Cornell Hospitality Quarterly

In response to this special issue, concerned with methods and measurements, a comprehensive review of the last 5 years of qualitative research was conducted in the top five journals that primarily publish articles pertaining to the hospitality industry. A total of 197 articles were read and analyzed for this review with a focus on the state of trustworthiness in the contemporary hospitality literature. An outline of the methods, techniques, and successes are presented in this review as are recommendations for scholars, journal editors, journal reviewers, and our partners in industry who use qualitative data for many reasons including but not limited to employee satisfaction surveys, market focus groups, and employee exit interviews. In addition, the relatively novel and nascent ideas regarding empirical rigor such as transparency and replicability are introduced to the hospitality field.

This systematic literature review aimed to investigate the use of case study method in hospitality and tourism research to increase the awareness about the use of case study as a research method. Data were collected (n = 871) from 10 leading hospitality and tourism journals published between 1974 and 2020. A thematic analysis of the data was conducted using Leximancer as a computer-aided analysis software. The study findings reveal an overall mislabel and misuse of the case study method. Suggestions are provided to improve case study method applications and increase case study research for more theory development in hospitality and tourism research.

Practitioners and researchers are conducting more focus groups online as a qualitative data collection method, yet rigorous methodological studies investigating the diversity of findings versus traditional in-person focus groups are limited. Previous studies primarily focused on health topics, varied on topic scope (broad to sensitive), format (synchronous vs. asynchronous), and online platform (several no longer exist). This experimental study sought to address that gap by randomly assigning participants into treatment groups to brainstorm sustainable practices for the hospitality industry (i.e., a broad topic) on a popular publicly available platform (i.e., Reddit). Although the in-person focus groups generated a greater word count and number of ideas, they generated an equivalent number of unique ideas. In terms of idea diversity, thematic analysis revealed a relatively high degree of overlap in themes from both groups. Of 13 themes, 10 (77%) occurred in both treatment groups. The overlapping themes represented 91% of all key words generated across both groups. These results highlight the potential for online focus groups to generate idea diversity at a level that is comparable to in-person focus groups. For practitioners seeking to benefit from guest insights, the findings help to substantiate the value of a lower cost, faster-to-market data collection method.

The emancipatory approach in disability research takes the political position of promoting the voices of people with disabilities to make possible transformative changes to their lives. Based on auto-ethnographic research notes made while applying the emancipatory approach to qualitative research conducted with employees with disabilities in the Vietnamese hospitality industry, this article suggests guidelines that include four steps: preparing/planning, recruiting, conducting interviews, and confirming the data. Practical strategies for each stage in the process are also suggested, with the aim of better including the voices of employees with disabilities in qualitative hospitality research. The article will benefit future researchers conducting qualitative research on employees with disabilities by highlighting the value of the emancipatory approach, which has not been previously reported on in the hospitality literature.

While organizational and management research has implemented the use of experience sampling methods (ESM), hospitality management research has yet to reap the benefits of this method and design. ESM involves collecting data at several time points from participants as they experience organizational phenomena, measuring the variations and oscillations in attitudes, behaviors, and performance. This article seeks to define ESM for hospitality research, highlight the strengths, outline the challenges of ESM, and offer best practices by using ESM data from three hospitality industry examples. Each example compares cross-sectional data collection methods and analyses to ESM data collection methods and analyses to compare the different results of the data collection methods.

Advancements in technology enable hospitality organizations to rely on digital recruitment efforts such as websites to attract applicants. Reflecting this industry trend, a small, but growing body of literature from the hospitality industry examines how applicants react to online recruiting using fictitious websites of hypothetical companies in experiments. The purpose of this article is to validate the use of fictitious websites as an experimental data collection method. Two quasi-experiments were guided by theories and model of applicant perceptions of fit and organizational attraction. Fit was manipulated by matching the career preference of active job seekers (e.g., a job seeker in the hotel sector) with a fictitious website (e.g., a hotel’s careers page) or not (control group). The results from the two quasi-experiments showed person–organization fit (Study 1) and person–job fit (Study 2) led to more organizational attraction under conditions of matches (e.g., a job seeker in the hotel sector evaluating a hotel’s careers page) than in the control groups. The findings of the two studies not only support the use of fictitious websites as a viable data collection method but also open a new line of research for hospitality research and human resources. Future hospitality scholars can use this technique to manipulate organization’s human resource practices (e.g., recruitment, selection, training, performance evaluation, compensation, and benefits) and examine attitudes of individuals (e.g., applicants, employees, and managers). The current data collection method also allows for researchers to not only manipulate information but also maximize the realism of the experimental stimuli.

As consumers increasingly research and purchase hospitality and travel services online, new research opportunities have become available to hospitality academics. There is a growing interest in understanding the online travel marketplace among hospitality researchers. Although many researchers have attempted to better understand the online travel market through the use of analytical models, experiments, or survey collection, these studies often fail to capture the full complexity of the market. Academics often rely upon survey data or experiments owing to their ease of collection or potentially to the difficulty in assembling online data. In this study, we hope to equip hospitality researchers with the tools and methods to augment their traditional data sources with the readily available data that consumers use to make their travel choices. In this article, we provide a guideline (and Python code) for how to best collect/scrape publicly available online hotel data. We focus on the collection of online data across numerous platforms, including online travel agents, review sites, and hotel brand sites. We outline some exciting possibilities regarding how these data sources might be utilized, as well as discuss some of the caveats that have to be considered when analyzing online data.

In this paper, we examine published research in six top-tier hospitality journals to explore response rates for different survey distribution methods across specific characteristics like research context, respondents, and geographical regions. Data were analyzed from 1,389 papers published from January 2001 to December 2019. By looking at a large set of published response rates, distribution and enhancing methods and type of respondents, findings from this study will aid researchers in designing more effective surveys and successfully collecting necessary data. The implications for response rate in hospitality research are also presented.

Moderation testing through latent factor models is relatively underutilized in hospitality and tourism research. The purpose of this research is to highlight the differences in the treatment of measurements of reflective constructs as composite indices versus latent factors in moderating effect tests in hospitality research. For this research, we build our primer on the investigation of the differences in customer satisfaction with the perceived entertainment experience at a hospitality/tourism attraction, contingent on customers’ personality trait extraversion, borrowed from the Big-Five mini marker inventory. Our findings illustrate the consequences of the measurement conceptualization and the representation of constructs in statistical models with interaction effects. While using composites simplifies the estimation of the regression paths and provides a reasonable sense of the direction of the effect and its statistical significance, it is not always aligned with the theoretical and conceptual underpinning of the employed constructs. A statistical model with composites may underestimate an interaction effect, whereas a model with a dichotomized moderator may overestimate the interaction effect. The findings of this research draw the attention of the hospitality and tourism research community on different representations of reflective constructs in their measurement and statistical models.

Partial least squares path modeling (PLS-PM) and generalized structured component analysis (GSCA) are two key estimators derived from a full-fledged composite-based structural equation modeling (SEM). The analyses of PLS-PM and GSCA have been recently extended to mimic factor-based SEM, and the extended approaches are called PLS_C and GSCA_M, respectively. Simulation studies have confirmed that the relative performance of PLS-PM is comparable with that of GSCA. Similarly, GSCA_M, PLS_C, and the traditional factor-based SEM perform equally well in parameter recovery. Although composite-based SEM perfectly fits into the current research landscape that focuses on a prediction-oriented approach, empirical research in the hospitality context that uses PLS-PM, GSCA, PLS_C, and GSCA_M estimators is extremely rare. To encourage hospitality researchers to adopt these methodologies, we demonstrate an illustrative example using PLS-PM, GSCA, PLS_C, and GSCA_M based on the confirmatory composite analysis (CCA) procedure. Measurement and structural invariances, applications of model fit, PLS_predict, and importance-performance map analysis are incorporated into our example. Finally, practical management in the hospitality field based on this methodology is discussed.

Recently, research of the servicescape has expanded to include a social element in addition to the traditionally identified physical/tangible element. Typically, this social servicescape construct has been treated as a measured variable, reflecting the other customers in the service environment across three dimensions (i.e., similarity, behavior, and appearance). However, the exclusive use of measurement to operationalize a phenomenon limits both the types of methods that can be used and, correspondingly, the types of research questions that can be asked. Accordingly, the purpose of this research is to propose and test a scenario-based manipulation of the customer social servicescape construct so that future research can address the phenomenon using experimental design. Scenarios crossing the social servicescape with social density (i.e., crowding) are constructed in three different domains (restaurant, hotel, and retail) and tested in terms of their nomological validity by assessing the effects of the manipulated variables on attitudes and satisfaction. Our results demonstrate that the three elements of the social servicescape—similarity, appearance, and behavior—each had a direct and significant effect on attitude and satisfaction. In addition, these results were consistent across the hotel, restaurant, and retail contexts. The clarity and consistency of these findings indicate the viability of the social servicescape manipulations as a research tool.