<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Background</h3>
<p>Public opinion about experience and expectation on services that appear on Twitter platforms provides valuable insights into satisfaction and experience.</p>
</sec>
<sec><h3>Objectives</h3>
<p>This research investigates consumer perception and opinion toward AirAsia’s services after the COVID-19 outbreak.</p>
</sec>
<sec><h3>Methods/Approach</h3>
<p>A framework is proposed by integrating the customer satisfaction model with the digital service quality dimension: product quality, price, situational factors, personal factors, service quality, and digital service quality. Nvivo is used to extract and analyse Twitter data for sentiment analysis, thematic analysis, and word frequency calculations.</p>
</sec>
<sec><h3>Results</h3>
<p>Findings demonstrated that AirAsia had received more negative sentiments than positive sentiments, indicating a lower level of satisfaction across all dimensions of customer satisfaction.</p>
</sec>
<sec><h3>Conclusions</h3>
<p>This research provides the airline industry, especially AirAsia, with an opportunity to better understand the sentiments and preferences of its customers. AirAsia can use the findings of this research to evaluate the quality of their services, especially in terms of service quality, to improve customer satisfaction, gain customer loyalty, and enhance customer experience.</p>
</sec>
</abstract>

Background
Public opinion about experience and expectation on services that appear on Twitter platforms provides valuable insights into satisfaction and experience.

Objectives
This research investigates consumer perception and opinion toward AirAsia’s services after the COVID-19 outbreak.

Methods/Approach
A framework is proposed by integrating the customer satisfaction model with the digital service quality dimension: product quality, price, situational factors, personal factors, service quality, and digital service quality. Nvivo is used to extract and analyse Twitter data for sentiment analysis, thematic analysis, and word frequency calculations.

Results
Findings demonstrated that AirAsia had received more negative sentiments than positive sentiments, indicating a lower level of satisfaction across all dimensions of customer satisfaction.

Conclusions
This research provides the airline industry, especially AirAsia, with an opportunity to better understand the sentiments and preferences of its customers. AirAsia can use the findings of this research to evaluate the quality of their services, especially in terms of service quality, to improve customer satisfaction, gain customer loyalty, and enhance customer experience.

Twitter Sentiment Analysis of the Low-Cost Airline Services After COVID-19 Outbreak: The Case of AirAsia

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution 4.0 International License.

Background
Public opinion about experience and expectation on services that appear on Twitter platforms provides valuable insights into satisfaction and...

Twitter Sentiment Analysis of the Low-Cost Airline Services After COVID-19 Outbreak: The Case of AirAsia

Online veröffentlicht: 25. Apr. 2024

Seitenbereich: 1 - 23

Eingereicht: 12. Dez. 2022

Akzeptiert: 16. Sept. 2023

DOI: https://doi.org/10.2478/bsrj-2023-0009

Schlüsselwörter
Twitter, sentiment analysis, AirAsia, airline industry, service quality, customer satisfaction

© 2023 Nor Hasliza Md Saad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Twitter Sentiment Analysis of the Low-Cost Airline Services After COVID-19 Outbreak: The Case of AirAsia

Online veröffentlicht: 25. Apr. 2024

Seitenbereich: 1 - 23

Eingereicht: 12. Dez. 2022

Akzeptiert: 16. Sept. 2023

DOI: https://doi.org/10.2478/bsrj-2023-0009

SchlüsselwörterTwitter, sentiment analysis, AirAsia, airline industry, service quality, customer satisfaction

© 2023 Nor Hasliza Md Saad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Twitter, sentiment analysis, AirAsia, airline industry, service quality, customer satisfaction