During the
online shopping process, retailers encourage customers to share their
product reviews on digital platforms such as e-commerce websites and social media, which in turn helps other shoppers to have a better understanding of the product. Online consumer reviews play a crucial role in providing product information before consumers make a
purchase decision. These reviews, full of desires, preferences and behavioural insights, are a valuable source of data for both consumers and businesses. By understanding
consumer behaviour and preferences, businesses can develop strategic plans to improve the quality of their services and tailor their offerings to better meet the needs of their customers. For example, when consumers do an online search for hotels, they can compare prices, locations, services and other aspects of various potential hotels on the site. The platform can also provide
personalised recommendations based on a user's
search history and preferences. Based on the attributes listed for each hotel, consumers can make an informed decision that is influenced by the consistency between their perceived hotel performance and their preferences – a classic
multi-attribute decision making (MADM) problem. Vocabulary-based
sentiment analysis is incorporated into online reviews to create product rankings that take into account the sentiment score of the review, the brand ranking of the product and the usefulness of the review. In the context of travel, travellers' choices and behaviours when selecting restaurants are heavily influenced by their travel classification or purpose, such as leisure, business or adventure. The study's modelling results suggest that travellers show diverse preferences in terms of dining behaviour, depending on factors such as environment, type of cuisine, price range and dietary restrictions. While the study provides valuable insights into restaurant decision-making, it also acknowledges limitations and suggests other directions for research to further explore consumer preferences in various contexts. However, the sheer volume of online reviews and the need to consider various attributes when making decisions can be overwhelming for consumers. In many cases, it can be a challenge to discern genuine reviews from fake ones or marketing-driven content. Therefore, tools and methods must be developed to help consumers make informed choices by helping them rank product candidates based on other consumers' reviews and their preferences. The use of artificial intelligence and
machine learning algorithms has the potential to help sift through large amounts of data, extract useful insights and provide personalised recommendations to consumers. In short, online consumer reviews are an important resource for shoppers and businesses alike. Using this information can help businesses better understand consumer preferences, improve their offerings and ultimately increase customer satisfaction. For consumers, having access to aggregated, relevant and trustworthy information can greatly enhance their decision-making process and overall online shopping experience. == Effect of a price change ==