Passenger intelligence as a competitive opportunity: unsupervised text analytics for discovering airline-specific insights from online reviews

Srinivas, Sharan; Ramachandiran, Surya

doi:10.1007/s10479-022-05162-9

Cited by 7 publications

(4 citation statements)

References 66 publications

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“…After theme identification, the model derived from the LDA algorithm was used to calculate the possibilities of the occurrence of each topic in each document. As a piece of customer review contains multiple topics, to measure the dominant topic accurately, each review was split into several sentences, and the prevalent topic in each was determined using the LDA topic model (Srinivas & Ramachandiran, 2020).…”

Section: Methodsmentioning

confidence: 99%

It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customers

Zhao

Srinivas

2023

Int J Consumer Studies

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The adaptive clothing market, which focuses on the inclusive design of clothing and footwear for people with varying degrees of disability, has grown substantially in recent years. However, few scholars have sought to understand the perspectives of online adaptive clothing consumers. This study employed topic modelling, sentiment analysis and collocation analysis to discover common themes and insights emerging from online customer reviews, scraped from a third-party review platform and three retailing web sites. We utilized customer value and functional, expressive and aesthetic theories to group the results from topic modelling into key themes. Clothing function is the most frequently discussed theme in online customer reviews, followed by customer service and clothing aesthetics. Collocation analysis revealed the cause underlying each theme vis-à-vis customer satisfaction (e.g., fit and material quality) and dissatisfaction (e.g., sewing defects and lost shipment). The findings contribute to understanding the clothing needs and wants of people living with disabilities. It also provides practical guidelines on product offerings and online service optimization for adaptive clothing retailers.

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Section: Methodsmentioning

confidence: 99%

It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customers

Zhao

Srinivas

2023

Int J Consumer Studies

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“…Even if COVID-19 restricts social life, passengers do not compromise on the perceived quality of primary service attributes (such as seat comfort, cabin staff, food and beverage, ground services, and value for money). The sentiment and topic modeling procedure adopted by Srinivas & Ramachandiran (2023) revealed that cabin and ground staff is the largest topic (40%) in passenger reviews for the period August 2017-September 2019. Similar research conducted by Sulu et al (2021) revealed that staff is one of the most important themes during the pandemic.…”

Section: Synopsis Of Findingsmentioning

confidence: 99%

Do We Scapegoat The Pandemic? Investigating The Changes in Satisfaction Drivers of Air Passengers With DWLS-SEM

İnce,

Atalık

2024

Journal of Aviation

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This paper aims to investigate whether there is an alteration in the drivers of air passenger satisfaction before and after COVID-19. We conducted the multigroup structural equation modelling with the diagonally weighted least squares estimation method as the variables are categorical. Lastly, we performed ANOVA to spot if there is a change in the drivers of passenger satisfaction between before and after the pandemic. The results suggest all service attributes have a significant impact on satisfaction in the pre-COVID-19 era. Even if it seems that in-flight entertainment and in-flight WiFi are not as influential as before in the post-pandemic, ANOVA results revealed the difference between the pre-pandemic and the new-normal period was not statistically significant. Accordingly, airlines should not ignore the need to improve service attributes, called premium services, and holistically improve service design. In addition, after value for money, the most important attribute for passengers is ground handling. Hence, speeding up the boarding process would return carriers in the form of more satisfied customers. To the best of our knowledge, this is the first paper employing Multigroup DWLS SEM to focus on changes in determinants of air passenger satisfaction in a holistic approach, focusing on pandemic periods.

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“…Additionally, future research can combine different research methods, such as sequence pattern mining and text mining [17][18][19], to construct consumer preference trends by integrating sequence rules, product attributes, and textual data. By integrating new methods [5,[20][21][22][23] into the sequence pattern extraction process, the accuracy and effectiveness of product recommendations can be further improved.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Uncovering Insights for New Car Recommendations with Sequence Pattern Mining on Mobile Applications

Liu

Wang

2023

Applied Sciences

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This study employs sequential pattern mining to analyze browsing behaviors and aid mobile app service providers in effectively promoting and recommending new products. We collected browsing history data from 66,004 mobile app users for new car info in Taiwan, totaling 1,263,614 records over two months. By utilizing sequence pattern mining, we identified frequent browsing sequences on the app that can indicate subsequence product interests and suggest new items to potential customers. The proposed method can improve the user experience for mobile app users and facilitate the development of the potential market for advertising. The study highlights the effectiveness of sequence pattern mining in recommending new products to car app users, benefiting small app vendors, improving user experience, and informing product development decisions in the automobile industry. Furthermore, the findings emphasize the importance of considering the sequential relationships between events or items in pattern mining, particularly in mobile app development. In conclusion, the proposed approach offers a cost-effective solution for small app vendors to recommend new products and improve the overall user experience, providing valuable insights for the automobile industry.

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Passenger intelligence as a competitive opportunity: unsupervised text analytics for discovering airline-specific insights from online reviews

Cited by 7 publications

References 66 publications

It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customers

It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customers

Do We Scapegoat The Pandemic? Investigating The Changes in Satisfaction Drivers of Air Passengers With DWLS-SEM

Uncovering Insights for New Car Recommendations with Sequence Pattern Mining on Mobile Applications

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