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Film markets worldwide are rebounding and showing positive growth after the impact of the pandemic. Gower Street Analytics forecasts the global box office to reach $32 billion in 2023, marking a 23% increase from 2022. China is expected to contribute $6.8 billion to this total. While numerous movies are released worldwide each year, not all of them achieve success. For example, only three to four out of every ten Hollywood blockbusters are profitable (Vogel, 2014). As the film industry continues to grow, finding ways to generate profits from film investment remains a challenging task. Understanding how to improve box office revenue is crucial for film practitioners. The rise of numerous movie review websites has provided a platform for movie fans to freely share their thoughts, making the role of internet word-of-mouth (IWOM) increasingly significant in the industry. Consumers highly value their movie-watching experience, as it requires considerable time, money, and energy. To minimize risks, they rely on third-party word-of-mouth platforms to gather information about movies and make informed decisions.
Unlike IWOM of other products, IWOM of the movie industry has the following three characteristics. Firstly, due to the short life cycle of movies and the emphasis on timeliness, IWOM undergoes dynamic iterations before and after movie releases. People tend to discuss the movie after watching it, and the intensity of IWOM decreases over time (Dellarocas et al., 2007). For instance, a study by Liu (2006) analyzing forty movies found that word-of-mouth was most active during pre-release and premiere weeks, gradually declining thereafter. Moreover, word-of-mouth tends to have a lagged influence, as the growth of subsequent word-of-mouth is shaped by preceding word-of-mouth (Guo & Zhou, 2016). Secondly, the film industry has specialized film critics. They provide consumers and producers with criteria to identify good and bad films by virtue of their high levels of knowledge and professional skills in film aesthetics (Corciolani et al., 2020; Hsu, 2006). These specialized critics serve as valuable resources for both consumers and producers, providing guidance in movie selection and contributing to the marketing of movies. Thirdly, specialized critics in the movie industry often focus their IWOM on various aspects of the movie, including stars, directors, aesthetics, and overall assessment (Boatwright et al., 2007), but often do not involve movie details or spoilers, leaving some room for imagination.
Overall, existing research has mainly focused on IWOM information characteristics like IWOM number and valence, but few studies have explored their dynamics changes (Gelper et al., 2018). How antecedent word-of-mouth is acting on postecedent word-of-mouth and how IWOM dynamically affects box office revenues are not sufficiently studied. Moreover, the research on how IWOM publishers affect the growth of IWOM is insufficient. Therefore, it is necessary to conduct a time series study on IWOM and provide management suggestions for enterprises to formulate appropriate marketing strategies according to the changing characteristics of IWOM in different periods. This study focuses on the role of IWOM publishers in the iterative process of IWOM growth and explores the following three related questions by building a time series model: (1) What is the impact of IWOM publishers on IWOM growth and iteration? (2) How does the iterative growth of IWOM affect the box office of movies under the role of IWOM publishers? (3) Do the above effects have different lags?
The Chinese Spring Festival season (New Year's Eve to the sixth day of the first lunar month) is a seven-day period of concentrated cinema screenings in China. This season holds significant importance, contributing to 16.5% of the total national box office in 2021 and 20% in 2022. During the 2021 Spring Festival season in China, Hello, Lee Hwan-young achieved tremendous success, grossing $822 million worldwide, largely driven by the Chinese movie market. Douban.com, China's largest film community sharing site with over 200 million users, serves as an important platform to express the opinions regarding films. This study utilized Python to collect comments and related information from Douban.com within thirty days of the film's release. Employing a vector autoregressive (VAR) model, the study aimed to explore the interaction mechanism between IWOM publishers, IWOM, and movie theater revenue.