Quality management has always been a crucial aspect of businesses, but with the advent of the fourth industrial revolution, it has become more challenging than ever before. However, this has also brought new opportunities for businesses to adapt to the changing landscape and improve their quality management practices. One such paradigm of quality management is Quality 4.0, which emphasizes the use of technology and data-driven solutions to manage quality effectively.
It is no secret that big data analytics plays a critical role in Quality 4.0. User-generated content, such as online reviews and social media posts, has become an essential source of information for businesses to understand consumer satisfaction and perceived service quality. As a prime example, Airbnb has over 250 million reviews from hosts and guests worldwide, which provide valuable insights into customer preferences and expectations. Analyzing such vast amounts of unstructured data requires sophisticated tools and techniques, such as text mining, clustering, and topic modeling.
In recent years, sentiment analysis and topic modeling have gained popularity in analyzing large-scale databases of reviews and ratings. Sentiment analysis assigns a sentiment score to each review, indicating the polarity and intensity of the emotions expressed in the review. Topic modeling identifies underlying topics and themes in customer reviews, which can be mapped to service quality attributes. Latent Dirichlet allocation (LDA) and Structural Topic Model (STM) are some of the popular algorithms used for topic modeling.
Several studies have been conducted to analyze Airbnb customer reviews using sentiment analysis and topic modeling. For instance, a scientific paper focused on understanding the service quality attributes of Airbnb in four European countries - France, Italy, Spain, and Portugal. The study identified four dimensions of service quality attributes - Host service quality, Web responsiveness quality, Web efficiency, and Facility service quality. Using sentiment analysis and topic modeling, the study identified 37 service quality attributes, out of which eight had a statistically significant correlation with the sentiment score. The study's methodology included dataset extraction, data pre-processing, sentiment analysis, identification of the optimal number of topics, topic extraction, mapping topics onto service quality attributes, and labeling.
Another scientific paper analyzed Airbnb customer reviews in five Latin European countries - France, Spain, Italy, the UK, and Portugal. The study collected 2.7 million reviews from the most popular tourist destinations in Paris, Rome, Barcelona, and Lisbon. Topic modeling using the STM algorithm identified 65 optimal topics related to service quality attributes such as cleanliness, host friendliness, and proximity to restaurants and public transport.
These studies' results provide valuable insights into the preferences and expectations of Airbnb users in different countries and regions. The identified service quality attributes can help businesses to improve their service quality and gain a competitive advantage in the shared accommodation sector. However, these studies have certain limitations, such as not analyzing the evolution of the identified service attributes over time and not assessing potential differences in guest preferences among countries.
In conclusion, big data analytics and Quality 4.0 have opened up new avenues for businesses to manage quality effectively. Sentiment analysis and topic modeling are powerful tools that can help businesses extract valuable insights from user-generated content. The studies discussed above provide practical examples of how these tools can be used to identify service quality attributes and improve quality management in businesses like Airbnb. As the business landscape continues to evolve rapidly, it is essential for businesses to adapt and embrace new technologies and approaches to stay ahead of the competition.
Authors
Amat-Lefort, N., Barravecchia, F., & Mastrogiacomo, L. (2022). Quality 4.0: big data analytics to explore service quality attributes and their relation to user sentiment in Airbnb reviews. International Journal of Quality & Reliability Management, (ahead-of-print). https://doi.org/10.1108/IJQRM-01-2022-0024