Analyzing the Online Reputation and Positioning of Airlines

Ahmed, Ayat Zaki; Rodríguez‐Díaz, Manuel

doi:10.3390/su12031184

Cited by 10 publications

(4 citation statements)

References 84 publications

(151 reference statements)

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“…In the context of the airline industry, managers can use this dynamic method to identify the different labels to achieve the maximum customer satisfaction across time and position their service offerings. Our findings are in line with the literature on the important role of Big Data in providing airlines with a sustainable competitive advantage [12,96,97]. The practical implications of this study have strategic relevance for airlines.…”

Section: Discussionsupporting

confidence: 89%

“…The online reputation is the basis for the different research lines being developed to improve knowledge and provide useful tools for airlines to better understand their clientele's preferences and the competitiveness of their service offerings [11,12]. The objective is for the image transmitted by companies on the Internet through social media and specialized websites to correspond to the service perceived by customers [13].…”

Section: Introductionmentioning

confidence: 99%

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Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Ahmed

Rodríguez‐Díaz

2020

Sustainability

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Sentiment analysis is becoming an essential tool for analyzing the contents of online customer reviews. This analysis involves identifying the necessary labels to determine whether a comment is positive, negative, or neutral, and the intensity with which the customer’s sentiment is expressed. Based on this information, service companies such as airlines can design and implement a communication strategy to improve their customers’ image of the company and the service received. This study proposes a methodology to identify the significant labels that represent the customers’ sentiments, based on a quantitative variable, that is, the overall rating. The key labels were identified in the comments’ titles, which usually include the words that best define the customer experience. This database was applied to more extensive online customer reviews in order to validate that the identified tags are meaningful for assessing the sentiments expressed in them. The results show that the labels elaborated from the titles are valid for analyzing the feelings in the comments, thus, simplifying the labels to be taken into account when carrying out a sentiment analysis of customers’ online comments.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Ahmed

Rodríguez‐Díaz

2020

Sustainability

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“…They found geographic and low-cost strategy differences in service quality. They identified 295 significant tags associated with overall ratings [22]. Lucini et al used text-mining techniques on over 55,000 online reviews from 170 countries, representing over 400 airlines.…”

Section: Latent Dirichlet Allocationmentioning

confidence: 99%

Understanding Antecedents That Affect Customer Evaluations of Head-Mounted Display VR Devices through Text Mining and Deep Neural Network

Maeng,

Lee,

Yun

2023

JTAER

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Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors are expected to influence customer evaluations. Therefore, the present study aims to: (1) analyze customer reviews of hands-on HMD VR devices, provided with new user experience (UX), using text mining, and artificial neural network techniques; (2) comprehensively examine variables that affect user evaluations of VR devices; and (3) suggest major implications for the future development of VR devices. The research procedure consisted of four steps. First, customer reviews on HMD VR devices were collected from Amazon.com. Second, candidate variables were selected based on a literature review, and sentiment scores were extracted. Third, variables were determined through topic modeling, in-depth interviews, and a review of previous studies. Fourth, an artificial neural network analysis was performed by setting customer evaluation as a dependent variable, and the influence of each variable was checked through feature importance. The results indicate that feature importance can be derived from variables, and actionable implications can be identified, unlike in general sentiment analysis.

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“…The issue of online reputation assessment is not new; over time, it has become the subject of several international regulations and standards. Studies and research conducted in recent years showed that a company's online reputation strongly affects CSR consumer interpretations because the company is more accepted and credible when engaging in CSR [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Online Company Reputation—A Thorny Problem for Optimizing Corporate Sustainability

et al. 2020

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The new economic environment, characterized as volatile, uncertain, complex, and ambiguous, has been generated through the evolution of a world in which online reputation and corporate social responsibility have become closely interconnected. In this study, we assessed whether corporate social responsibility is a goal with multiple implications for the image of the organization and its financial results, which present a model for measuring online reputation in the context of optimizing corporate sustainability. We used an econometric approach that showed that the main purpose of the model is to determine the best value of the dependent variable by providing a number of new sets of explanatory variables on the exact quantification of the company’s online reputation. The main results can support the decision-making process in building and maintaining online reputation in the short or long term, and the information provided is useful for implementing online reputation management programs. We provide a rather nuanced picture of the relationship between the credibility of corporate sustainability claims, financial security, and the chance of preserving the online reputation built. The research shows that online reputation risk management is no longer optional; online reputation must become a strategic resource for sustainable business.

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Analyzing the Online Reputation and Positioning of Airlines

Cited by 10 publications

References 84 publications

Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Understanding Antecedents That Affect Customer Evaluations of Head-Mounted Display VR Devices through Text Mining and Deep Neural Network

Online Company Reputation—A Thorny Problem for Optimizing Corporate Sustainability

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