Exploring Hotel Consumer Purchase Intentions Through Web Analytics: A PLS-SEM Approach

Abstract

Consumer purchasing behaviour is a multifaceted process influenced by various factors, from personal and psychological to social and cultural influences (Kotler, 2010). Factors such as age, life-cycle stage, education, and economic status play a role in shaping individual preferences and choices. Moreover, psychological factors like motivation, perception, learning, beliefs, and personality attitudes significantly impact consumer decision-making processes (Duffett, 2015). The increasing prevalence of online hotel booking has attracted considerable attention from academic researchers (Cantoni et al., 2011). Consumers now rely extensively on digital channels to explore, evaluate, and ultimately make reservations for accommodation. This paradigm shift in consumer behaviour necessitates a deeper examination of the psychological and behavioural factors that influence online purchase intention, particularly in the context of hotel bookings. Various studies have identified information quality, website quality, perceived price, brand, and trust as crucial factors influencing purchase intention in the online hospitality industry (Law & Hsu, 2005; Rong et al., 2009). However, while these factors are widely recognized, there is still much to explore regarding their interplay and relative importance. Leveraging web analytics tools like Google Analytics provides valuable insights into consumer behaviour and website performance. These tools enable firms to measure customer purchase intention, track website traffic, understand demographics and interests, and identify popular web pages (Lu et al., 2010). By analysing web analytics data, hospitality firms can optimize their marketing efforts, improve customer acquisition and retention strategies, and make informed decisions to enhance their strategic decision-making processes (Mnyakin, 2023). By employing Partial Least Square Structural Equation Modelling (PLS-SEM) (Jöreskog & Wold, 1982) on web analytics data, the study aims to provide a comprehensive analysis of the factors that influence consumers' decision-making processes when booking hotels online, exploring the intricate relationships among Trust, Brand Orientation, and Engagement attitude. Our estimation model treats the collected information as constructs to estimate latent variables related to guest buying activity. This approach differs from previous studies (Rosenman et al., 2011) that have relied on survey questionnaires to gauge consumer engagement, citing concerns over accuracy and response bias. Our research, instead, follows those scholars (Liu et al., 2021) who have increasingly turned to analysing consumer engagement through behavioural data collected from the web and social media platforms as an alternative method. Our model serves as an example of the kind of information that can be extracted from the big amount of data a hotel generates daily. Managers should recognize the value of this knowledge as it is critical for developing strategies, planning future business advancements, and optimising the use of their resources. Furthermore, in a rapidly evolving and competitive industry, knowing the guest’s path and her/his level of trust can be crucial for tailoring more customized products and services. Leveraging web data allows revenue and hospitality management to adopt more comprehensive, science-based predictions, incorporating scenario analysis, decision support, and yield management.