Evidence map›Paper›PMID 37998670›Full record

ArticleBehavioral sciences (Basel, Switzerland)2023

Analyzing Online Reviews to Uncover Customer Satisfaction Factors in Indian Cultural Tourism Destinations.

Aura Lydia Riswanto, Seieun Kim, Hak-Seon Kim

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Aura Lydia RiswantoDepartment of Global Business, Kyungsung University, Busan 48434, Republic of Korea.
Seieun KimDepartment of Global Business, Kyungsung University, Busan 48434, Republic of Korea.ORCID 0000-0001-6131-7456
Hak-Seon KimSchool of Hospitality & Tourism Management, Kyungsung University, Busan 48434, Republic of Korea.ORCID 0000-0001-5361-6368

Funding

Ministry of Education of the Republic of Korea and the National Research Foundation of Korea NRF-2022S1A5A2A03052622
6 · The paper itself

Abstract

Tourism to Indian heritage destinations has been on the rise due to the increasing demand for heritage tourism. Increasing customer satisfaction and promoting Indian culture require tourism businesses to understand factors influencing tourists' experiences and behavior towards these destinations. Therefore, this study analyzes four popular heritage tourist destinations in India by using online reviews collected from Google Travel. Data are refined, processed, and visualized using the R programming language and UCINET 6.0. Furthermore, we explore the fundamental framework and interconnections among these characteristics through the utilization of exploratory factor analysis and linear regression analysis with the assistance of the SPSS software package. Based on customer reviews obtained from Google Reviews, an analysis was conducted on 6618 reviews of four heritage tourism destinations in India. From the top 60 words, four clusters of words were created, including "Physical characteristic", "Cultural and historical link", "atmosphere", and "area". Through explanatory factor analysis and linear regression analysis, we found that Physical characteristic, Cultural and historical link, atmosphere, and area all play a significant role in customer satisfaction. This study provides heritage destination managers and Indian government with insights into which attributes impact customer satisfaction the most and offers valuable marketing insights. As a result of this study, we are able to gain a greater understanding of the Indian heritage tourism market, and in doing so, we provide businesses with implications on how to enhance customer service.

Indexed as

big data analyticscultural heritage tourismcustomer experiencesemantic network analysis

Identifiers

PMID37998670
PMCPMC10669890

What OpenQuestion holds

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LicenceCC BY
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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.