No abstract

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.

No abstract

This study synthesizes existing empirical results about the effect of personal innovativeness on the intention to use technology in hospitality and tourism studies published from January 2010 to March 2020 via meta-analysis. The meta-analysis with a random effects model was conducted on 29 effect sizes of this relationship documented in 28 studies collected from over 7,000 search results on Google Scholar and Scopus. The results of the analysis suggest a significant positive medium effect of personal innovativeness on the intention to use technology in hospitality and tourism research with the overall effect size (ESr) of .38 (95% CI = .32, .44, z = 10.62, p = .001). The study also found that the effect does not change significantly across industries (hotels, restaurants, and tourism and travel), types of technology by task (with transaction function and without transaction function), age groups (younger than 30 years old and 30 years old and older), and power distance cultural differences of the respondents (high-power distance and low-power distance cultures). Based on the results of this study, the authors suggest adding personal innovativeness as a construct in technology adoption models in future research in hospitality and tourism studies and continue investigating potential moderations that could explain variations in effect sizes of the impact of personal innovativeness on the technology adoption intention across different populations. From the industry perspective, hospitality and tourism organizations may rely on customers with high perceived innovativeness to serve as change agents and drive customer adoption of new technology.

Traditionally, price has been one of the main revenue management levers that allow hotels to sell the right product to the right customer at the right time. Recent research in the area of electronic word-of-mouth has found that travelers' online reviews may also be an influential factor affecting other customers' booking decisions. However, it is challenging to determine the value of online reviews for customer decision making and to compare this value to hotel room prices. Therefore, the main goal of this study is to investigate which factors (room rate, online reviews' rating score, or the number of reviews by other travelers) are more important for customers' booking decisions and the optimal combination of these factors for booking a hotel. Conjoint analysis was applied to measure the relative importance of the aforementioned factors for travelers' booking decisions. The results revealed that the overall online rating was the most influential factor with regard to customer decision making about