Measuring hotel performance:Toward more rigorous evidence in both scope and methods

This paper extends the literature on hotel performance in both scope and methods. We introduce a model that accounts for heterogeneity in a flexible way and allows for the measurement of both efficiency and productivity. The model also accounts for the endogeneity problem in inputs and the issue of unobserved prices. We use a large sample of hotel companies that spreads across multiple geographical regions and locations, and accounts for some interesting and key determinants of hotel performance. We provide more validation to some contradictory findings in the literature. We show that large hotels do not necessarily outperform small hotels, and that hotel efficiency differs based on location, geographical region and type of service. The results further indicate that productivity growth is not a driving force in the industry

Unobserved heterogeneity in hospitality and tourism research

Despite the growing complexity of structural equation model (SEM) applications in tourism, it is surprising that most applications have estimated these models without accounting for unobserved heterogeneity. In this article, we aim to discuss the concept of unobserved heterogeneity in more detail, highlighting its serious threats to the validity and reliability of SEMs. We describe a Bayesian finite mixture modeling framework for estimating SEMs while accounting for unobserved heterogeneity. We provide a comprehensive description of this model, and provide guidance on its estimation using the WinBUGS software. We illustrate the importance of unobserved heterogeneity and the finite mixture modeling framework using a didactic application on brand equity where heterogeneity is likely to play an important role because of the differences in how consumers perceive the different dimensions of brand equity. We compare between various models and illustrate the differences between the standard and heterogeneous SEM and discuss the implications for research and practice

Changing The Basics:Toward More Use of Quantile Regressions in Hospitality and Tourism Research

The aim of this paper is to encourage more use of Quantile Regressions (QRs) in hospitality and tourism research. More importantly, we focus on the Bayesian estimation of QRs and discuss its advantages over traditional estimation techniques. We also discuss a Bayesian QR model that accounts for heteroscedasticity. We illustrate the performance of the two models using an interesting application on corporate social responsibility and firm value

Bayes factors vs. P-values

The use of p-values for hypothesis testing has always been the norm in the tourism literature. This paper proposes the use of Bayes factors as an attractive alternative for hypothesis testing. As the Bayes factor is based on the Bayesian approach, which relies solely on the observed sample to provide direct probability statements about the parameters of interest, it is more suited for the purpose of hypothesis testing. Importantly, in this paper we show that the Bayes factor has nicer properties than the p-value, a fact that should be of interest irrespective of whether the user is Bayesian or not. We discuss in more details the advantages of Bayes factors, and provide several interesting recommendations throughout the paper

The estimation and decomposition of tourism productivity

This paper estimates a total factor productivity index that allows for a rich decomposition of productivity in the tourism industry. We account for two important characteristics: First, the heterogeneity between multiple tourism destinations, and second, the potential endogeneity in inputs. Importantly we develop our index at the macro level, focusing on cross-country comparisons. Using the Bayesian approach, we test the performance of the model across various priors. We rank tourism destinations based on their tourism productivity and discuss the main sources of productivity growth. We also provide long-run productivity measures and discuss the importance of distinguishing between short-run and long-run productivity measures for future performance improvement strategies

Non-parametric regression for hypothesis testing in hospitality and tourism research

The goal of this paper is to promote the use of Non-Parametric Regression (NPR) for hypothesis testing in hospitality and tourism research. In contrast to linear regression models, NPR frees researchers from the need to impose a priori specification on functional forms, thus allowing more flexibility and less vulnerability to misspecification problems. Importantly, we discuss in this paper a Bayesian approach to NPR using a Gaussian Process Prior (GPP). We illustrate the advantages of this method using an interesting application on internationalization and hotel performance. Specifically, we show how in contrast to linear regression, NPR decreases the risk of making incorrect hypothesis statements by revealing the true and full relationship between the variables of interest

Advertising spending, firm performance, and the moderating impact of CSR

This article investigates the potential of corporate social responsibility (CSR) to influence the link between advertising spending and firm performance. Drawing upon the literature of CSR, we hypothesize that CSR positively moderates the relationship between advertising spending and firm performance. We focus on two types of firm performance: sales and firm value. Using two samples from both the hotel and restaurant industries, we found that firms with higher levels of CSR enjoy higher returns on advertising spending than firms with lower levels of CSR. We discuss the theoretical and managerial implications of these findings and provide direction for future research

Revisiting shape and moderation effects in curvilinear models

Testing hypotheses using curvilinear models in the form of U-shaped, inverted U-shaped or S-shaped relationships has increased considerably over the last decade. However, researchers in the field continue to make common mistakes in analysing such models; for example, ignoring important steps in validating the shape (e.g. U-shape) of a curvilinear relationship, or failing to properly test for different types of moderation. In this paper, following Haans, Pieters, and He (2016), we aim to provide clear guidelines on how to properly test and theorize for shape and moderation effects in curvilinear models. We provide illustrations from different contexts, including U-, inverted U- and S-shaped relationships. We also describe a new procedure that simplifies the process of testing within such contexts. The simplification works for both theoretical derivations as well as for the computation of marginal and moderation effects and curvature

Bayesian dynamic panel models for tourism research

This paper describes several innovative dynamic panel data models that allow variations in slope coefficients both across time and cross-sectional units. We replace time variation with a dynamic (autoregressive) component and introduce several variations of the so-called Mundlak device in which random intercepts are linear function of the average values of the regressors. We develop all models in a Bayesian framework, and test their performance using an interesting application on the impact of advertising on firm sales. We provide technical details of all these models and present tools to compare their performance in a Bayesian framework. Moreover, model averaging and posterior model pools are presented to gain more insight into the relationship between advertising and sales