Unveiling key drivers for social robot adoption in the hospitality sector: Two-phase confirmatory factor analysis and structural equation modeling approach

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-02-03 DOI:10.1016/j.dib.2025.111360

Rashmi Ranjan Panigrahi , Judit Oláh , Subhodeep Mukherji , Abdul Bashiru Jibril , Kiran Cotha

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引用次数: 0

Abstract

This data set measures the hotel industry's intention to adopt social robots. Data was collected from the employees of five-star hotels. Data-based research is based on primary surveys conducted at five-star hotels, and a standardised questionnaire was established to conduct interviews. Following the conclusion of the procedure for collecting the data, a structural equation modelling approach was employed to evaluate the hypothesis. The results provide exploratory factor analysis, confirmatory factor analysis and structural equation modelling. This data set will contribute significantly to the literature on social robots in the hospitality sector. This data set will help the practitioners work on major problem factors for improving the quality of partnering relationships between key participants in their present and future hospitality sectors. By removing or minimizing these problem factors, the practitioners will be contributing considerably towards effective hotel sectors. The data would be valuable for academics and industry professionals working with the hotel business nationally and internationally.

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.