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This paper aims to serve as an important guide for more rigorous quantitative research in tourism and hospitality.

This paper relies on comments from several methodological experts in the field, as well as the authors’ main observation of the literature.

This paper identifies ten important areas of concern. In each of these areas, the authors provide recommendations for best practices.

There are certainly other issues and concerns that are not covered in this paper. However, the issues addressed can be applied or generalized to most methodological contexts.

This paper does not present results from original research but provides interesting and comprehensive recommendations for more rigorous quantitative research.

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.

This paper introduces more advanced panel data specifications that would exploit heterogeneity and allow for arbitrary forms of autocorrelation and heteroskedasticity in the error terms.

In line with Assaf and Tsionas (2019a, 2019b), this paper builds on the Mundlak device to propose panel data models to allow for random slope coefficients, as well as time slope coefficients. This paper allows for arbitrary heteroskedasticity and autocorrelation, thus mitigating possible model misspecification. This paper develops and estimates the model in a Bayesian framework. This paper’s methods can be generalized to many nonlinear models including limited dependent variable models.

This paper compares several competing models such as a classical panel data model, which has only firm effects. This paper also examines the role of standard deviations in the formation of firm effects and time effects in the Mundlak device. This paper clearly shows that our framework introduces the best flexibility and model fit.

This paper illustrates the importance of using more flexible models (i.e. unit-specific and time-varying coefficients) for future estimation of panel data in the field.

This paper discusses techniques that will improve panel data estimation in the hospitality and tourism literature.

The purpose of this note is to describe the concept of regression trees (RTs) for hospitality data analysis.

RT is an effective non-parametric predicting modelling approach that would free researchers from the need to force a certain functional form. The method does not require normalization or scaling of data.

The authors illustrate how RTs can be used to find a model that would result in the best prediction.

A common challenge facing hospitality researchers is to estimate a regression model with the correct specification. RTs can help researchers identify the best explanatory model for prediction.

This paper describes the concept of RTs for the modelling of hospitality data.

This paper aims to focus on addressing endogeneity using instrument-free methods. The authors discuss some extensions to well-known techniques.

This paper discusses some attractive methods to address endogeneity without the need for instruments. The methods are labeled are “harmless” in the sense that instruments are not needed and the distributional assumptions are kept to a minimum or they are replaced by more flexible semi-parametric assumptions.

Using a hospitality application, the authors provide evidence about the effectiveness of these techniques and provide directions for their implementation.

Finding valid instruments has always been a key challenge for researchers in the field. This paper discusses and introduces methods that free researchers from the need to find instruments.

The paper discusses techniques that are introduced from the first time in the tourism literature.

Design documents’ deficiencies (DDDs) are a major issue that plagues the construction industry in many countries. The purpose of this paper is to investigate the major causes of DDDs in a fast expanding economy, where errors can translate to an adverse impact on the economy. This paper aims to identify and assess the causes of DDDs for large construction projects from the consultants’ perspective.

In total, 20 causes of DDDs were shortlisted through an extensive literature survey. In total, 37 consultants based in the Eastern Province of Saudi Arabia participated in the study. The respondents were requested to provide their opinion with respect to the relative importance of the identified causes. The result of their responses was then used to rank the causes.

Based on their significance indices, the most significant causes of DDDs were “assigning design tasks to designers who lack work experience,” “team members’ communication skills,” “the level and quality of the design professional’s education,” “cross-disciplinary coordination,” “effective design team,” and “lack of knowledge transfer mechanism.”

Recommendations were provided to mitigate the issues of DDDs. It is envisioned that through a better understanding of the major causes of DDDs, design firms will be better positioned to recognize DDDs’ causes and establish appropriate measures for reducing their occurrence. The findings of this paper are beneficial to all project stakeholders, including design firms, clients, contractors, and end users.

This paper contributes to the literature by providing an assessment of the various causes of DDDs in the context of a rapidly expanding economy, where any inefficiency can lead to massive losses to the economy.

The purpose of this paper, a critical reflection, is twofold. First, by comprehensively reviewing scale development procedures in hospitality research, a concerning lack of nomological validity testing is demonstrated. Second, the need for nomological validity testing is discussed and both conceptually and empirically reasoned.

This research systematically reviews scale development studies in three leading hospitality journals, including Cornell Hospitality Quarterly, International Journal of Contemporary Hospitality Management and International Journal of Hospitality Management over ten years (2012–2021) to analyze the completeness of scale development procedures. Specifically, the authors evaluate whether the reviewed studies engage in testing the nomological and predictive validity of the newly developed measures.

The results indicate a concerning gap in the current practices in hospitality research. Specifically, only 33.3% of the examined studies assess nomological validity. These findings collectively underscore the need for improving the comprehensiveness of scale development processes in hospitality research.

The study offers important implications for hospitality researchers. The paper provides an extensive discussion on the importance and benefits of testing for nomological validity in scale development studies, contributing to the completeness and consistency of scale development procedures in the hospitality discipline.

This research critically assesses prevalent, and widely accepted, scale development procedures in hospitality research. This research empirically demonstrates the neglect of nomological validity issues in scale development practices in hospitality research. Scale development is an essential scientific practice used to create a research instrument in a field of study, improving our understanding of a specific phenomenon and contributing to knowledge creation. Considering the significance of scale development in advancing the field of hospitality research, the validation procedures involved in the scale development processes are of utmost importance and should be thoroughly applied.

This paper aims to foster a new discussion on endogeneity in hospitality and tourism research.

This paper elaborates on some of the common sources of endogeneity and the methods available to address them.

The authors present a variety of methods that can be used to mitigate the endogeneity problem. The authors provide simulation evidence regarding the risk of incorrectly selecting instrumental variables. The authors also provide several important practical recommendations for future research.

There are other issues and methods of correcting for endogeneity, that is not covered in this paper. However, the paper focuses on issues and methods that can be generalized to most contexts.

The paper provides practical recommendations for more rigorous regression estimation.

This study examines empirically the development of efficiency levels in the hotel industry and its key drivers, by juxtaposing pre- and post-COVID-19 results in the four most important cities in the United States in terms of international arrivals.

A two-step approach was employed by first utilising the data envelopment analysis method to characterise the efficiency of United States hotels. A multiple linear regression analysis was performed to compare hotel performance in the two years prior to the COVID-19 outbreak with the first two years after the outbreak.

The results indicated the positive effect of size and the economy hotel category on efficiency, while increasing property age, urban location and a lack of brand affiliation showed a negative effect. During the pandemic, size and the economy category remained significant and positive, whereas urban location continued to represent a negative effect.

This study assists decision makers regarding prospective investments, supports existing and future portfolio analyses, contributes to observations on competition, and offers a wider perspective of the industry.

First, this study investigated the evolution of factors that influenced efficiency before and after the outbreak of the COVID-19 pandemic. Second, the results offer insights into the impact of the pandemic on hotels' efficiency levels. Third, the study proposes directions for the restoration of hotel efficiency to pre-COVID-19 levels.

In Bangladesh (BD), delays in infrastructure are common. Many previous studies have explored the causes of infrastructure delays. However, this study investigated the causes of delays by taking responses from the stakeholders who are responsible for planning, design, funding, approval and implementation. There are few studies that have related infrastructure project delays to heterogeneity in stakeholders’ perceptions.

A structural equation (SE) model is developed with 350 normally distributed data points to understand the heterogeneity in stakeholders’ perceptions regarding delays in infrastructure projects in BD. Additionally, the relative importance index (RII) approach is used to assess the responses, validating the SE model.

The study finds that among the three latent variables, “Project itself related delay” has more influence on delays in infrastructure projects. Among the observed variables under the “project itself related delay” latent variable, “DPP approval process” has the most significance. From the heterogeneity analysis, the study found differences in responses among the stakeholders from “the Engineering Department,” “the Planning Office” and “the Construction Firm/Industry.” An important class of stakeholders believes that their stage is not being delayed and that other stages require attention.

The data sample is 350. More data can improve the accuracy of the findings. Most of the respondents are civil engineers (74%) and represent the owner of the project. Sample data from more stakeholders’ will enhance the accuracy of the result.

This study addresses the requirements of Bangladeshi project stakeholders and how their interactions cause delays in projects. Furthermore, the opinions of other stakeholders are taken into consideration when determining the specific factors of individual stakeholders that are causing delays. Practically, the distance between stakeholders should be reduced. A project manager can play a role in this regard. Initiatives should be taken on how to complete the project quickly by eliminating the requirements discussed among the stakeholders and bureaucratic complications. Instead of placing blame on one another, stakeholders should take the initiative to figure out how to work together to finish the project on schedule. The Planning Commission’s approval of the Development Project Proposal (DPP) and Revised Development Project Proposal (RDPP) should be obtained as soon as possible by owner stakeholders. In order to avoid frequently changing the DPP, owners should also exercise greater caution when choosing contractors. Contractor stakeholders should use efficient and proper manpower and equipment so that unexpected delays are not created during the execution of work. Since the role of the contractor stakeholder is the most important among the three types of stakeholders, the contractor should raise awareness and urge the owners to get the RDPP approved quickly.

The findings from the study can help mitigate delays in infrastructure projects in BD, taking into account the perceptions of various stakeholders.