Evidence map›Paper›PMID 37389284›Full record

ArticleComputers in human behavior2023

Are we ready for hotel robots after the pandemic? A profile analysis.

Fatemeh Binesh, Seyhmus Baloglu

Abstract read
In one paragraph

Article in Computers in human behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Medical teleconsultation from the patient's perspective. A demographic segmentation.The European journal of health economics : HEPAC : health economics in prevention and care · 2025
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Fatemeh BineshUniversity of Florida, Department of Tourism, Health, and Event Management (THEM), PO Box 118209 Gainesville, FL 32611, USA.
Seyhmus BalogluUniversity of Nevada Las Vegas, William F. Harrah College of Hospitality, Box 456021 4505 S. Maryland Pkwy., Las Vegas, NV 89154-6021, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has changed many aspects of the hospitality and tourism industry, including technology-oriented and contactless solutions. Despite the increasing number of service companies using robots on their premises, most of the previous attempts and practices of adoption have remained unsuccessful. Prior research hints that socioeconomic factors could influence the successful adoption of these emerging technologies. Nevertheless, these studies ignore the role of profile factors and assume a homogenous response to using robots in service operations during the pandemic. Based on the theory of diffusion of innovation and a sample of 525 participants, this study investigates the differences in customers' attitudes, their level of involvement, and optimism for service robots as well as their intentions to use service robots in the five main areas of hotel operations (front desk, concierge, housekeeping, room service, and food and beverage) based on five profile factors (age, gender, income level, education, and purpose of trip). MANOVA tests show significant differences in all variables based on demographic factors; male, younger, more educated, higher income, and leisure travelers show more positive attitudes, higher involvement, optimism, and intention to use service robots across various hotel departments. In particular, mean scores were found to be smaller for the traditionally human-oriented functional areas of the hotel operations. We also clustered the participants based on their level of comfort and optimism about using service robots in hotels. Given the rapid changes in the service industry and the increasing adoption of service robots, this paper adds a much-needed contribution to the ongoing research on service robots in the service industry by investigating the impact of profile factors on guests' behavior towards service robots.

Indexed as

Artificial intelligenceCOVID-19Customer behaviorHotelService industryService robotTechnology adoption

Identifiers

PMID37389284
PMCPMC10291269

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.