CC BY
0From Digital Social to Box Office Revenue: Analysis of the Impact and Trends of Electronic Word-of-Mouth
Weihua Qiao
Cheongju University. Cheongju, Korea. Email: qiaoqiao0100[at]gmail.com ORCID https://orcid.org/0o09-0005-9940-8640
Received: 3 April 2024 | Revised: 25 May 2024 | Accepted: 7 June 2024
Abstract
In the digital era, Electronic Word-of-Mouth (eWOM) — the online sharing of opinions and information about movies — has become a crucial factor influencing box office performance. Social media posts, reviews, and ratings shape audience perceptions and impact their choices. This study aims to comprehensively analyze the influence of eWOM on movie box office revenue, identifying key research areas, seminal works, and influential authors in this field, along with highlighting research trends through keyword analysis. The results reveal that the research field is divided into three stages: initial, exploratory, and empirical, with recent emphasis on factors such as the quantity, quality, and credibility of eWOM, significantly affecting consumer behavior. The study also identifies key eWOM research trends, including movie marketing, relevance analysis, and sentiment analysis of reviews. Target audiences include film industry professionals and academic researchers interested in understanding the role of eWOM in enhancing box office performance.
Keywords
Electronic Word-of-Mouth; Box Office Revenue; Movie Revenue; Citespace
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This work is
icensed under a Creative Commons "Attribution" 4.0 International License
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От цифрового общения к кассовым сборам: анализ влияния и тенденций эффекта электронного сарафанного радио
Цяо Вэйхуа
Университет Чхонджу. Чхонджу, Корея. Email: qiaoqiao0100[at]gmail.com ORCID https://orcid.org/0009-0005-9940-8640
Рукопись получена: 3 апреля 2024 | Пересмотрена: 25 мая 2024 | Принята: 7 июня 2024
Аннотация
В эпоху цифровых технологий электронное сарафанное радио (eWOM) — онлайн-обмен мнениями и информацией о фильмах — стало важным фактором, влияющим на кассовые сборы. Посты, отзывы и рейтинги в социальных сетях формируют восприятие зрителей и влияют на их выбор. Данное исследование направлено на всесторонний анализ влияния eWOM на кассовые сборы фильмов, включая выявление ключевых областей исследований, значимых работ и авторов в данной сфере, а также определение исследовательских трендов с помощью анализа ключевых слов. Результаты показывают, что исследовательская область делится на три этапа: начальный, исследовательский и эмпирический, с акцентом в последние годы на такие факторы, как количество, качество и достоверность eWOM, что существенно влияет на поведение потребителей. Исследование также выделяет ключевые тренды в области eWOM, такие как маркетинг фильмов, анализ релевантности информации и эмоционального отклика в отзывах. Целевая аудитория включает специалистов киноиндустрии и академических исследователей, заинтересованных в изучении роли eWOM для повышения кассовых сборов.
Ключевые слова
электронное сарафанное радио; кассовые сборы; доходы от фильмов; Citespace
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Это произведение доступно по лицензии Creative Commons "Attribution" («Атрибуция») 4.0 Всемирная
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Introduction
The emergence of digital platforms and social media has fundamentally transformed the way individuals communicate and exchange information. With the proliferation of online review platforms, social networking sites, and forums, consumers now have unprecedented access to opinions, critiques, and recommendations regarding various products and services, including movies. (Chiu et al., 2022) Electronic Word of Mouth (eWOM), defined as the dissemination of consumer opinions, experiences, and recommendations through digital channels, has emerged as a potent force shaping consumer attitudes and behaviors (Marchand et al., 2017).
Over the past two decades, numerous studies have explored the relationship between eWOM and box office revenue. In the realm of the film industry, online word-of-mouth plays a crucial role in influencing audience perceptions, preferences, and ultimately, movie consumption decisions. Positive online word-of-mouth can generate buzz, create anticipation, and drive ticket sales, while negative online word-of-mouth may deter audiences and adversely impact box office performance. (Fan et al., 2021; Sangjae & Joon Yeon, 2023). Furthermore, an increasing number of researchers are focusing on the quality, quantity, and sources of electronic word-of-mouth itself, which affects movie revenue (Chiu et al., 2022; Daowd et al., 2020). Additionally, different social media platforms and the identities of reviewers can influence consumer behavior (Yeap et al., 2014; Bao & Chang, 2014).
Despite the extensive body of literature, several gaps remain in the current research. First of all, research on the impact of online word-of-mouth on box office is inconsistent. Previous findings suggest that higher eWOM do not necessarily correlate with better box office revenue performance. Indeed, appropriate negative reviews may even increase consumer interest in movies (Aghakhani et al., 2020). These various factors pose challenges for film distributors during the promotion period. Second, there is a lack of comprehensive analysis using advanced biblio-metric methods to map the development and trends in this research area over an extended period. Existing research often emphasizes individual factors of electronic word-of-mouth (eWOM), such as quantity, quality, credibility, or sentiment. Including eWOM in different social media, the impact on the box office is also different. Baek et al. (2017) found that Twitter had a greater impact on movie revenue in the early stages of broadcasting, while online review sites had a relatively greater impact on movie revenue in the later stages — and even the influence of word-of-mouth generated by celebrities or key opinion leaders (Fan et al., 2021). However, due to the lack of a comprehensive review of research hotspots and an anticipation of research trends, the direction of research in this field remains unclear and the conclusions are inconsistent. This lack of clarity does not provide a solid theoretical foundation for subsequent researchers. Moreover, for movie industry practitioners, there is no systematic reference on how to better leverage
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the positive impact of eWOM on box office revenue amid the multitude of influencing factors. Therefore, understanding the dynamics of electronic word-of-mouth and its impact on movie revenue and box office success has become a critical area of study for researchers, practitioners, and industry stakeholders alike.
The field of electronic word-of-mouth (eWOM) research employs various theoretical frameworks to understand its impact. Prominent among these are the Signaling Theory, the Theory of Planned Behavior, and the Information Adoption Model (IAM). Signaling Theory is often used to explain how eWOM acts as a signal of product quality, influencing consumer perceptions and behaviors (Fan et al., 2021). The Theory of Planned Behavior provides insights into how eWOM shapes consumer intentions through attitudes, subjective norms, and perceived behavioral control (Ramírez-Castillo et al., 2021). However, for the purposes of this study, which focuses on the impact of eWOM on box office revenue, the Information Adoption Model (IAM) is particularly pertinent. The IAM explains how individuals adopt and act upon information based on its perceived usefulness and credibility (Hussain et al., 2017). This model is especially relevant for analyzing eWOM, as it captures the dynamics of how online reviews and ratings influence consumer decision-making processes (Lee & Choeh, 2020). By applying the IAM, this research aims to provide a more targeted understanding of how eWOM affects moviegoers' choices and, consequently, box office performance.
Addressing these gaps, the present study employs CiteSpace, a powerful bibliometric tool, to analyze nearly 20 years of literature on the application of eWOM in the film industry (Chen, 2016). By utilizing bibliometric analysis, this research not only identifies the hotspots and emerging trends in the field but also highlights the key contributors and seminal articles that have shaped the discourse (Donthu et al., 2021). This comprehensive approach provides a more nuanced understanding of eWOM's role in influencing box office revenue and offers insights into the evolving landscape of digital communication in the context of the movie industry.
The primary objective of this study is to comprehensively examine the impact of electronic word-of-mouth on movie revenue and box office performance. Specifically, the study aims to:
• Identify and analyze research hotspots within the field of online word-of-mouth and its influence on the film industry.
• Explore the temporal trends and evolution of research topics related to electronic word-of-mouth and movie revenue/box office performance.
• Conduct co-citation analysis to identify seminal works and influential authors in this field.
• Utilize co-word analysis to uncover prevalent themes and concepts within the literature.
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Analyze keywords to identify emerging trends and areas of interest within the research domain.
Data and Methodology
This study primarily relies on the Sciences Citation Index (SCI), Social Sciences Citation Index (SSCI) and Arts & Humanities Citation Index (AHCI) databases within the Web of Science Core Collection (WOSCC) as the main sources of data. The unit of retrieval is set as "Topic" (Figure 1). Firstly, this study conducted a search in the database using the search condition TS="ewom" OR "online word-of-mouth" OR "electronic word of mouth", resulting in 1812 relevant records. Subsequently, this study conducted another search using the condition TS="movie revenues" OR "box office revenue", yielding 117 records. Finally, the merged the two search conditions using the formula: TS="movie revenues" OR "box office revenue" AND "ewom" OR "online word-of-mouth" OR "electronic word of mouth", resulting in 1442 records. Then the results were filtered by selecting English language and article or review article types. After removing duplicates and merging records using Citespace software, a total of 1426 records were obtained.
CiteSpace is utilized as the primary tool for literature analysis. Developed in Java, CiteSpace is a bibliometric visualization software that integrates network analysis, co-occurrence analysis, co-citation analysis, clustering analysis, and coupling analysis. It can automatically generate scientific knowledge maps in a specified field based on the researcher's input of literature data in pure text format and parameter settings, providing unique advantages in literature visualization analysis (Chen, 2005).
1,426 results from Science Citation Index Expanded (SCI EXPANDED), Social Sciences Citation Index (SSCI), Arts &
Humanities Citation Index (A&HCl); Q. "movie revenues" (Topic) or "bo* office revenue" (Topic) and "electronic word of mouth" (Topic) or "ewom" (Topic) or "online word -of-mouth" (Topic)
Analyze Results Citation Report Д Create Alert
@ Add Keywords Quick add keywords: + ewom + electronic word-of-mouth + electronic word-of-mouth ewom + online word of mouth + online word-of-mouth >
Refined By: {^Languages: English X ^ ^Document Types: Article cr Review Article X Clear all
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Figure 1. Screenshot of search criteria
Research overview
S b ect Categor
According to the analysis of subject categories in the Web of Science (WOS), research on eWOM and movie box office performance is not limited to the fields of film or communication studies (Figure 2). The majority of studies appear in the business domain, with a total of 594 articles (Table 1). Following closely is the management field, with 279 articles. Additionally, there is notable representation
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in the fields of Hospitality Leisure Sport Tourism, Computer Science Information Systems, and Information Science Library Science.
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Figure 2. Su ect Category Classification
Ta le 1. Su ect Category Detailed List
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We of Science Categories Record Count
Business 594
Management 279
Hospitality Leisure Sport Tourism 239
Computer Science Information Systems 227
Information Science Library Science 162
Environmental Studies 85
Psychology Multidisciplinary 78
Environmental Sciences 72
Communication 70
Green Sustainable Science Technology 70
P blication trend
Figure 3 illustrates the publication trend in this field. According to the exponential line indicated, there is an overall increasing trend in the number of publications over the years. The formula for the publication trend index is represented by Y = 5E-171e0.1965x.
From the graph, we can observe that before 2010, research in this field tended to be flat, with an average of 8 publications per year. However, from 2011 to 2021, over approximately a decade, there has been a rapid increase in the number of publications in this field. This can be largely attributed to the widespread adoption of social media. After reaching a peak in 2021, there is a slight downward trend, but the overall trend shows a year-on-year increase. This indicates that an increasing number of researchers and practitioners are paying attention to the relationship between eWOM and movie box office performance.
Pubiication count
—Publication Count
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2006 2007 200S 2009 2010 2011 2012 2013 2014 2015 2015 2017 201B 2019 2020 202 1 202 2 202 3 2024
Figure 3. Num er of pu lications over years
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Co ntr Region Distrib tion
Citespace can conduct country analysis regarding the contribution to specific research domains. By analyzing indicators such as publication volume, citation counts, and collaboration networks, it's possible to assess the research strength and influence of different countries in a particular research field. This analysis can help identify research hotspots and trends, providing valuable insights for research planning. Additionally, it promotes international research cooperation and exchange, enhancing international competitiveness.
From the Figure 4, we can observe the distribution of the top 50 contributing countries in this field. Combined with Table 2, regarding research on the impact of
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eWOM on movie box office performance, European countries generally contribute more than other regions. Among them, the country with the highest centrality is the United Kingdom, which began research in this field in 2009. The United States follows with a centrality of 0.25, ranking third is China. Spain and France rank fourth and fifth, respectively.
Ta le 2. Countries/regions with most pu lications
Ranking Centrality Year Country
1 0.29 2009 ENGLAND
2 0.25 2006 USA
3 0.19 2007 CHINA
4 0.14 2009 SPAIN
5 0.13 2014 FRANCE
6 0.10 2017 SAUDI ARABIA
7 0.09 2008 AUSTRALIA
8 0.09 2014 MALAYSIA
9 0.09 2016 PAKISTAN
10 0.08 2011 CANADA
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Instit tionsanal sis
Analyzing institutions using CiteSpace offers several benefits. Firstly, it helps researchers understand the influence and status of institutions in specific research
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fields by analyzing their frequency of appearance and collaboration relationships in the literature, revealing leading research institutions and their collaborative networks. Secondly, it aids in identifying research hotspots and trends within the field, providing important references for institutions to formulate research directions and strategies. Additionally, analyzing institutional collaboration networks can promote cooperation and exchange between institutions, facilitate the sharing and dissemination of research results, and thereby promote the progress and development of academic research. In summary, utilizing CiteS-pace to analyze institutions provides a more comprehensive perspective and deeper understanding, helping to enhance the academic reputation and influence of institutions and promote cooperation and development in academic research.
Figure 5 illustrates the situation of research institutions in the field of eWOM and movie box office performance. According to Table 3, the institution making the largest contribution in this field is the State University System of Florida, with a total of 49 related articles from 2008 to the present. Following closely are City University of Hong Kong and University System of Georgia, with 25 related studies each. Ranked fourth and fifth are Hong Kong Polytechnic University and California State University System, with 23 and 21 related studies, respectively. In terms of centrality in this field, City University of Hong Kong and Sejong University have relatively high centrality values of 0.17, indicating outstanding contributions from these institutions in this field.
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Figure 5. Network diagram of institutions
Ta le 3. Top 10 pu lication issued y institutions
Num er Count Centrality Year Institution
1 49 0.05 2008 State University System of Florida
2 25 0.17 2007 City University of Hong Kong
3 25 0.11 2008 University System of Georgia
4 23 0.14 2009 Hong Kong Polytechnic University
5 21 0.04 2006 California State University System
6 20 0.08 2011 Pennsylvania Commonwealth System of Higher Education
7 17 0.02 2009 University System of Ohio
8 17 0.11 2018 Audencia
9 15 0 2016 University of Valencia
10 14 0.17 2019 Sejong University
thors anal sis
Using CiteSpace, authors within a specific research domain can be analyzed to help researchers understand prominent scholars and their academic contributions in the field, thereby identifying experts and authoritative figures within the domain. Additionally, analyzing collaboration relationships and networks among authors can reveal collaboration patterns and trends within the research field, fostering academic exchange and cooperation. Furthermore, author analysis aids in discovering emerging researchers and novel research directions, providing important references for the disciplinary development of the field.
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Figure 6. Author colla oration network analysis
Figure 6 provides a visual representation of researchers in the field, and in conjunction with Table 4, it shows that Filieri Raffaele is the most prolific contributor in this field, with a total of 18 related publications from 2014 to the present. Following closely is Dwivedi Yogesh K, who has published 9 articles in this field from 2020 to the present, spanning four years. Mariani Marcello M ranks third with 7 related studies. Tied for fourth place in terms of contributions are Buhalis Dimitrios and Bigne Enrique, both of whom have contributed 6 related studies since 2015. From the collaboration network, there are few fixed collaboration relationships within the field, with researchers predominantly conducting independent research. However, there is a continuous influx of new researchers joining the field.
Ta le 4. Author contri ution ranking list
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Ranking Frequency Year Author
1 18 2014 Filieri Raffaele
2 9 2020 Dwivedi Yogesh K
3 7 2019 Mariani Marcello M
4 6 2015 Buhalis Dimitrios
5 6 2015 Bigne Enrique
6 5 2020 Ismagilova Elvira
7 5 2019 Jiang Cuiqing
8 5 2016 Buzova Daniela
9 5 2019 Borghi Matteo
10 5 2016 Ilkan Mustafa
Hotspot analysis
Keywords are highly condensed representations of the main topics of an article. Through keyword co-occurrence, the relationships between research topics on eWOM and movie revenue can be intuitively observed, and through frequency and centrality ranking analysis, research focuses and hotspots can be identified. Keyword clustering further deepens co-occurrence relationships, calculating a set of keywords with tighter relationships, thus forming thematic clusters containing multiple groups of vocabulary (Shi & Li, 2019).
Due to the generation of numerous keyword nodes, in order to ensure clearer analysis, the Pathfinder algorithm was selected in Citespace software with a g-index (K) of 10. The time frame was set from January 2006 to December 2024, with a time slice interval of 1 year. A total of 277 nodes and 443 edges were generated (N—277, N=443) (Figure 7).
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Typically, when a keyword appears more frequently in the literature, it indicates greater importance and influence within the research domain. This suggests that the keyword is closely related to core concepts or hot topics in the field, attracting widespread attention and discussion among researchers. Therefore, an increase in the frequency of keyword appearances can be considered one of the indicators of the keyword's importance and influence within the research domain.
From the table 5, after excluding the keyword "word of mouth," the most frequently appearing keywords are "online reviews" (505), "Impact" (505), "social media" (341), "Information" (300), "Trust" (245), "Consumer" (217), "Behavior" (196), "moderating role" (190), "Communication" (186), and "Model" (183).
In Citespace, the centrality of keywords refers to a metric that measures the importance and influence of a keyword within the literature network. Specifically, the centrality of a keyword can be measured by its connections within the literature network, including the number of times the keyword is cited by other keywords and the degree of association with other keywords. In general, keywords with high centrality have greater influence and importance within the literature network, better representing the core concepts and hot topics in the research field, thus providing important references and guidance for academic research and disciplinary development.
From the table 5, we can observe that the top ten keywords ranked by centrality are "information" (0.51), "model" (0.38), "Consumer" (0.27), "loyalty" (0.25), "brand" (0.24), "Acceptance" (0.22), "Behavior" (0.21), "persuasion" (0.21), "Antecedents" (0.20), and "online" (0.19). Through keyword analysis, researchers focus more on the information related to eWOM itself, while there is also significant research on the relationship between eWOM and consumers, such as its impact on consumer behavior, loyalty, satisfaction, and so on.
In Citespace software, keyword clustering refers to grouping keywords that appear in the literature based on their co-occurrence patterns and semantic relevance. These keyword clusters can reflect the relationships between different themes and concepts within the research field, helping researchers better understand the structure and dynamics of the field. Keyword clustering analysis can reveal hot topics, themes, and research directions within the research domain, providing important clues and guidance for further research. By generating keyword cluster diagrams, researchers can visually observe the relationships between different keywords, providing a beneficial visualization tool for further exploration of the research domain.
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Та le 5. Top 10 Keyword distri ution
Keywords
Count Centrality Year
online reviews 505 0.02 2008
impact 505 0.14 2009
social media 341 0.09 2013
information 300 0.51 2006
trust 245 0.07 2009
consumer 217 0.27 2006
behavior 196 0.21 2008
moderating role 190 0.06 2012
communication 186 0.03 2009
model 183 0.38 2006
Keywords
information
model
consumer
loyalty
brand
acceptance
behavior
persuasion
antecedents online
Count Centrality Year
300
183 217
41
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24
196
30
108 96
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Figure 7. Analysis of keywords
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Cluster ID
Size Silhouette
Ta le 6. Cluster distri ution Top Terms ( LLR log—likelihood ratio)
#0
word-of-mouth content
#1
spokesman credibility
#2
opinion platform
#3
publication pattern
#4
korean consumer
#5
positive emotion
#6
online
recommendation behavior
#7
brand page engagement
#8
movie review
#9
price service
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22
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television advertising (445.02, 1.0E-4); word-of-mouth content (285.63, 1.0E-4); offline performance (285.63, 1.0E-4); sequential rollout (280.93, 1.0E-4); movie box office performance (280.93, 1.0E-4)
ewom characteristics (402.24, 1.0E-4); of-mouth communication (331.47, 1.0E-4); online repurchase intention (326.59, 1.0E-4); herd behavior (306.8, 1.0E-4); spokesman credibility source fit (306.8, 1.0E-4)
consumer opinion platform (353.17, 1.0E-4); attitude contagion (353.17, 1.0E-4); hotel rating scheme (346.98, 1.0E-4); emerging realm (334.57, 1.0E-4); 360-degree technology (334.57, 1.0E-4)
web analytics (440.82, 1.0E-4); publication pattern (437.14, 1.0E-4); intellectual structure (437.14, 1.0E-4); international journal (437.14, 1.0E-4); purchase involvement (433.47, 1.0E-4)
korean consumer (461.85, 1.0E-4); awe experience (399.69, 1.0E-4); voice assistant (344.49, 1.0E-4); fashion shopping (344.49, 1.0E-4); online customer communities (337.58, 1.0E-4)
product quality signal (355.26, 1.0E-4); positive emotion (354.09, 1.0E-4); consumer brand sensitivity (352.24, 1.0E-4); hotel online (352.24, 1.0E-4); guest satisfaction (349.23, 1.0E-4)
destination marketing tool (358, 1.0E-4); sustainable heritage festival (358, 1.0E-4); mediation study (358,
1.0E-4); to-peer accommodation (351.24, 1.0E-4); influencing explicit online recommendation behavior (351.24, 1.0E-4)
social media brand page engagement (319.56, 1.0E-4);
scale development (311.15, 1.0E-4); digital content marketing (302.71, 1.0E-4); value-laden social media (302.71, 1.0E-4); boosting ewom (298.21, 1.0E-4)
determining consumer (287.24, 1.0E-4); fuzzy analytic hierarchy process approach (287.24, 1.0E-4); movie review (287.24, 1.0E-4); preferred ewom platform (287.24, 1.0E-4); movie box office revenue (278.79, 1.0E-4)
behavioural intention (336.42, 1.0E-4); modeling consumer distrust (279.55, 1.0E-4); batavia jakarta heritage (267.77, 1.0E-4); adaptive reuse building (267.77, 1.0E-4); price service convenience (263.57, 1.0E-4)
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In the cluster analysis conducted using Citespace software, a total of 14 clusters were generated. Typically, researchers focus on analyzing the top 10 clusters as they represent the most significant and influential clusters within the dataset. Below are the descriptions of the top 10 clusters (Table 6):
Cluster #0: Word-of-mouth content - Keywords: "television advertising", "word-of-mouth content", "offline performance", "sequential rollout", "movie box office performance"
Cluster #1: Spokesman credibility - Keywords: "ewom characteristics", "word-of-mouth communication", "online repurchase intention", "herd behavior", "spokesman credibility source fit"
Cluster #2: Opinion platform - Keywords: "consumer opinion platform", "attitude contagion", "hotel rating scheme", "emerging realm", "360-degree technology"
Cluster #3: Publication pattern - Keywords: "web analytics", "publication pattern", "intellectual structure", "international journal", "purchase involvement"
Cluster #4: Korean consumer - Keywords: "Korean consumer", "awe experience", "voice assistant", "fashion shopping", "online customer communities"
Cluster #5: Positive emotion - Keywords: "product quality signal", "positive emotion", "consumer brand sensitivity", "hotel online", "guest satisfaction"
Cluster #6: Online recommendation behavior - Keywords: "destination marketing tool", "sustainable heritage festival", "mediation study", "to-peer accommodation", "influencing explicit online recommendation behavior"
Cluster #7: Brand page engagement - Keywords: "social media brand page engagement", "scale development", "digital content marketing", "value-laden social media", "boosting ewom"
Cluster #8: Movie review - Keywords: "determining consumer", "fuzzy analytic hierarchy process approach", "movie review", "preferred ewom platform", "movie box office revenue"
Cluster #9: Price service - Keywords: "behavioural intention", "modeling consumer distrust", "Batavia Jakarta heritage", "adaptive reuse building", "price service convenience".
These clusters provide insights into different themes and concepts within the research domain, helping researchers understand the structure and dynamics of the field and guiding further research endeavors.
Through cluster analysis, we can gain a more intuitive understanding of research in the field of electronic word-of-mouth (eWOM) concerning movie box office and revenue. This study summarizes three main areas of core research in the field.
The first area encompasses clusters #0, #1, and #2, focusing on research related to eWOM information, including quality, quantity, credibility, and platforms. With the rapid rise of text-based social media and online word-of-mouth (WOM) activities, millions of people express their thoughts and opinions on various topics.
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Consider that word-of-mouth is now the most influential source of information guiding consumer choices and purchasing decisions (Vujic & Zhang, 2018).
Chakravarty conducted three stimulus experiments and found that eWOM has a significant influence on moviegoers, especially those who do not frequently watch movies, who are more susceptible to eWOM influence, even surpassing the influence of movie reviews on moviegoers (Chakravarty et al., 2010). Huang et al. found through their research on eWOM and consumers that the credibility of reviews moderates their adoption, as highly credible reviews are more likely to be adopted by consumers to help them make judgments (Huang & Liang, 2021). In the discernment of credibility by consumers, opinion leaders play a significant role. The electronic word-of-mouth of opinion leaders drives sales due to their experience and knowledge background (Bao & Chang, 2014). Lee's research on 2090 films in the Korean market found that in cases where reviews are helpful, the quantity and length of reviews have a greater impact on box office performance (Lee & Choeh, 2018). Aghakhani et al. developed and measured "comment consistency" (the level of consistency between comment text and its accompanying comment rating) and rating inconsistency (the degree of inconsistency between comment rating and the average rating) based on the Elaboration Likelihood Model (ELM) in 2020. They found that rating inconsistency negatively moderates the effect of comment consistency on comment usefulness in electronic reviews (Aghakhani et al., 2020). In other words, taking movie reviews as an example, if the average score for a film is 8 points, but the scores given in the comments are 2 and 3 points, significantly deviating from the average score, consumers who have not yet watched the film may perceive the comments below the film as insincere and unhelpful. Additionally, Hsu et al. found that consumers perceive electronic word-of-mouth as more credible when receiving negative news (Hsu & Yang, 2021).
The second area includes clusters #3, #6, and #7, focusing on the topic of brand marketing strategies in movies. Kim et al. analyzed 169 films released in 2008 as samples and found that the frequency of online word-of-mouth and the efficacy of expert reviews are important factors influencing domestic box office performance (Kim et al., 2013). Similarly, findings from Rao's study in 2017 indicated that external validation (recommendations from top reviewers) is more important for revenue than the content of advertisements (Rao et al., 2017). They analyzed the content of print advertisements in the film industry (such as the number of reviews quoted in the advertisement, the presence of top reviewers, the size of the advertisement, stars, directors, etc.).
Moreover, previous studies have found that different publishing platforms also influence eWOM and box office results. Given the large number of movies released weekly, consumers seek online reviews to help them decide which movies to watch. It was found that source credibility is more important than information quality, so review websites have become the most popular electronic word-of-mouth platform (Yeap et al., 2014). Additionally, due to the significant uncertainty in choice,
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consumers are more likely to actively search during the initial release period of a movie and passively engage later. Expert reviews and peer reviews have a significant impact in the early stages of movie release, and the influence diminishes over time. In contrast, the volume of comments on Weibo, a microblogging platform, has a greater impact on later box office revenue (Huang et al., 2017). Lee collected data on the 45-day sales and eWOM of 63 films released in South Korea in 2017 and conducted panel regression analysis on a total of 2,835 data items. They found a nonlinear relationship between eWOM performance and sales, which varied depending on the type of eWOM platform involved (Lee et al., 2021).
The third area encompasses clusters #4, #5, #8, and #9, focusing on the theme of emotional sentiment in reviews. The sentiment of online word-of-mouth (WOM) often sparks controversy as different individuals have varying preferences for the same product. Similarly, with movies, different viewers elicit diverse reactions after watching, leading to the emergence of reviews with varied emotional sentiments online. The frequency, sentiment, and timing of movie tweets are correlated to varying extents with the movie's box office revenue, with negative tweets particularly detrimental to revenue (Vujic & Zhang, 2018). Analyzing the impact of critical reviews on box office revenue, it was found that review efficacy not only directly affects box office revenue but also increases community active posts and user comment volume, thereby indirectly boosting box office revenue (Chiu et al., 2022). Lee et al. investigated in 2017 how the entropy of movie review text sentiment affects the relationship between online word-of-mouth and product sales. The experiment found that the entropy level in review text positively moderates the relationship between word-of-mouth (e.g., efficacy and quantity) and movie box office sales. In other words, in terms of review sentiment, a balanced distribution of positive and negative reviews positively impacts movie box office sales. Unilaterally deleting negative text does not substantially improve box office revenue and may even reduce consumer trust in online word-of-mouth (Lee et al., 2017). Moon found that over time, viewers' movie experiences make them more critical of viewership ratings. Therefore, sequels often generate more revenue but have lower viewership ratings compared to the original works (Moon et al., 2010).
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Evolution trend analysis
Timezone anal sis
The timezone map analysis in CiteSpace is a functionality used to visualize the trends and evolution of keywords over time in the literature. Through the timezone map, researchers can clearly observe the frequency and correlation changes of keywords in different time periods, thereby revealing the development trends and evolutionary history of the research field. With the development of technology and changes in people's habits of using social media, the research focus on eWOM in the context of movie box office has also evolved over different time periods.
New Media and Human Communication | https://doi.org/10.46539/gmd.v6i4.500
From the Figure 8 and Figure 9, we can clearly see that over the 17-year period from 2006 to 2023, researchers initially focused on studying eWOM itself, such as comments and information on social media. Subsequently, there was a gradual shift towards deeper exploration of the relationship between eWOM and consumers. This includes aspects such as the quality, quantity, and credibility of eWOM sources, the influence of eWOM on consumer behavior across different social media platforms, and the impact of ewom with different emotions on consumers. In recent years, researchers have been dedicated to integrating theories to explain observed phenomena. For example, the application of information processing theories, including the exploration and analysis of intermediary elements in the relationship between eWOM and consumers.
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#5 positive emotion
#6 online recommendation behavior
#7 brand page engagement
#8 movie review
#9 price service
Figure 9. Timeline view in the field of eWOM/Movie Box Office
Based on the publication trends and timezone analysis, the field can be divided into three stages: the inception stage, exploration stage, and empirical stage. The period from 2006 to 2010 represents the inception stage. The timezone analysis indicates that during this period, high-frequency keywords mainly focused on the analysis of eWOM-related content, such as online reviews, information, and the impact of eWOM. From 2011 to 2021, which spans a decade, represents the explosive growth period of eWOM research in the context of movie box office. During this period, researchers began to concentrate on studying the impact of eWOM on consumers (i.e., moviegoers) and their relationships, including satisfaction, loyalty, and consumer perceptions. Keywords related to these aspects were frequent during this period, indicating a focus on exploring the influence of eWOM with different emotions and across different platforms on consumer behavior. From 2022 to the present, while there has been a slight decrease in the total number of publications, recent years have seen a greater emphasis on integrating phenomena with theories. For example, there is a trend towards utilizing the information adoption model to explain the impact of eWOM quantity, quality, and credibility on consumers. Moreover, there is a noticeable inclusion of more scientific research methods, such as the application of the partial least squares structural equation modeling (PLS-SEM) for explanation and analysis.
B rst ordanal sis o fee ords
The burst word analysis in CiteSpace software is a method used to identify keywords that experience a significant increase in frequency within a specific time period. These keywords, known as "burst words," typically indicate a sudden surge in attention within the research field during a certain period. Burst word analysis helps researchers discover emerging hotspots and trends within the research domain, identifying keywords with prominent influence and providing timely and valuable information to researchers. This analytical method is essential for understanding the dynamic changes in the research field and capturing research hotspots.
Through burst word analysis of keywords (Table 7), we can observe the top five burst words with the longest duration: "information" (12 years), "film critics" (10 years), "search" (10 years), "electronic commerce" (9 years), and "dynamics" (8 years). The top five burst words with the strongest intensity are "purchase intention" (10.25), "dynamics" (9.39), "sales" (7.33), "persuasion" (6.11), and "community" (5.63). Additionally, we can identify the newest keywords such as "consumer engagement," "purchase intention," "information quality," "values," and "continuance intention." From this, we can observe that recent research trends have focused on the study of the quality of electronic word-of-mouth (eWOM) information, as well as the analysis and research on the impact of eWOM on consumer behavior, particularly in enhancing consumer engagement, increasing purchase intentions, and fostering awareness of continued purchasing.
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Keywords Strength Begin End
information 4.32 2006 2017
consumer 4.27 2006 2011
model 3.92 2006 2009
film critics 5.61 2008 2017
electronic commerce 4.9 2008 2016
communicatio n 4.7 2009 2016
dynamics 9.39 2010 2017
community 5.63 2010 2016
search 4.7 2010 2019
consumer behaviour 3.9 2010 2013
information search 3.93 2011 2015
sales 7.33 2012 2015
persuasion 6.11 2012 2017
choice 4.41 2013 2017
consequences 4.46 2015 2018
internet 4.68 2016 2018
self 4.11 2018 2019
models 3.8 2019 2020
consumer engagement 4.44 2020 2024
co creation 4.32 2020 2021
destination image 4.65 2021 2022
purchase intention 10.25 2022 2024
information quality 4.32 2022 2024
values 4.27 2022 2024
continuance intention 4.27 2022 2024
Ta le 7. Burst words analysis 2006 - 2024
Analysis of co-cited documents
Citation analysis reflects the flow and reorganization of knowledge, enabling researchers to trace influential foundational literature and explore future research trends, thereby clarifying the evolutionary context of eWOM research in film revenue (Shi & Li, 2019). In co-citation analysis, the software identifies articles that are cited together in the literature, considering them as related documents. This analysis helps researchers discover collections of literature with similar research topics or interconnected themes within the same research domain. By analyzing these co-cited documents, researchers can identify important papers, core concepts, and collaboration relationships among researchers within the research field. Co-citation analysis also aids researchers in understanding the structure and evolution of the research field, thereby providing valuable clues and guidance for further research.
Ta le 8. Analysis of co-cited documents
References
Year Strength Begin End
2006 - 2024
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Sen S, 2007, J INTERACT MARK, V21, P76, DOI 10.1002/dir.20090
Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001
Litvin SW, 2008, TOURISM MANAGE, V29, P458, DOI 10.1016/j.tourman.2007.05.011
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Park C, 2009, J BUS RES, V62, P61, DOI
10.1016/j.jbusres.2007.11.017
Lee M, 2009, INT J ADVERT, V28, P473, DOI
10.2501/S0265048709200709
Mudambi SM, 2010, MIS QUART, V34, P185
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
Cheung MY, 2009, INT J ELECTRON COMM, V13, P9, DOI 10.2753/JEC1086-4415130402
2006
2007
2008
2008
2008
2009
2009
2010
2010
2009
12.85
10.88
15.54
12.06
10.91
14.26
12.05
17.7
15.6
12.54
2008 2011
2009 2012
2010
2010
2010
2011
2011
2013
2013
2013
2014
2014
2012 2015
2012 2015
2012 2014
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Chu SC, 2011, INT J ADVERT,
V30, P47, DOI 10.2501/IJA-30- 2011 19.95 2013 2016 1-047-075
Ye QA, 2011, COMPUT HUM
BEHAV, V27, P634, DOI 2011 11.63 2013 2016 10.1016/j.chb.2010.04.014
Sparks BA, 2011, TOURISM
MANAGE, V32, P1310, DOI 2011 11.12 2013 2016
10.1016/j.tourman.2010.12.011
Cheung CMK, 2012, DECIS
SUPPORT SYST, V54, P461, 2012 22.21 2014 2017
DOI 10.1016/j.dss.2012.06.008
Cheung CMK, 2012, DECIS
SUPPORT SYST, V53, P218, 2012 14.74 2014 2017
DOI 10.1016/j.dss.2012.01.015
Cantallops AS, 2014, INT J
HOSP MANAG, V36, P41, DOI 2014 21.26 2016 2019 10.1016/j.ijhm.2013.08.007
Berger J, 2014, J CONSUM
PSYCHOL, V24, P586, DOI 2014 13.18 2016 2019
10.1016/j.jcps.2014.05.002
You Y, 2015, J MARKETING,
V79, P19, DOI 2015 12.79 2016 2020
10.1509/jm.14.0169
Filieri R, 2014, J TRAVEL RES,
V53, P44, DOI 2014 12.35 2016 2019
10.1177/0047287513481274
Tsao WC, 2015, INT J HOSP
MANAG, V46, P99, DOI 2015 11.58 2016 2020
10.1016/j.ijhm.2015.01.008
Ladhari R, 2015, INT J HOSP
MANAG, V46, P36, DOI 2015 18.66 2017 2020
10.1016/j.ijhm.2015.01.010
Rosario AB, 2016, J
MARKETING RES, V53, P297, 2016 17.18 2017 2021 DOI 10.1509/jmr.14.0380
Liu ZW, 2015, TOURISM
MANAGE, V47, P140, DOI 2015 13.58 2017 2020
10.1016/j.tourman.2014.09.020
Erkan I, 2016, COMPUT HUM
BEHAV, V61, P47, DOI 2016 14.72 2019 2021
10.1016/j.chb.2016.03.003
Filieri R, 2018, INFORM
MANAGE-AMSTER, V55, P956, 2018 16.83 2021 2024 DOI 10.1016/j.im.2018.04.010
From Table 8, we can observe the citation status of literature in this field over different periods. Through the analysis of co-cited articles, we can accurately
understand the focus and hot topics of research during that period. Filieri et al.'s study is the most cited article in the past three years. They found that consumers perceive signals of popularity, bilateral reviews (referring to discussions of both positive and negative aspects of services), and expert sources (but not source credibility) to be helpful in evaluating service quality and performance (Filieri et al., 2018). The quality, credibility, usefulness, and adoptability of information, as well as information needs and attitudes toward information, are key factors influencing consumers' purchase intentions regarding social media word-of-mouth (Erkan & Evans, 2016). Liu et al. also found in their study that displaying information about the identity and professional reputation of commentators in reviews has a positive effect on the perceived usefulness of the reviews (Liu & Park, 2015). In the analysis of the impact of electronic word-of-mouth (eWOM) on consumer purchase decisions, it was found that the quantity of online word-of-mouth has a greater impact on sales than the valence of online word-of-mouth. Additionally, negative eWOM does not always threaten sales, but high variability does indeed jeopardize sales (Babic Rosario et al., 2016). Comments from Facebook1 "friends" influence consumers' purchase intentions. Internet users who have read positive comments show significantly higher consumption intentions than those who have read negative comments (Ladhari & Michaud, 2015).
Discussion
This research aimed to provide a detailed overview of the studies on electronic word-of-mouth (eWOM) in the context of movie revenue, exploring the complex interrelationship between these domains. By employing a bibliometric approach, we analyzed author collaborations, geographic distributions, keyword usage patterns, and temporal trends, using a dataset of scholarly works from 2006 to 2024 indexed in major citation indices. Our findings offer several insights:
Firstly, the publication trend in the field of movie revenues and eWOM showed a significant increase post-2011, peaking in 2021 before a slight decline, yet maintaining an overall upward trajectory. This indicates that research on this dual topic has gained substantial attention in the academic sphere.
Secondly, author's analysis revealed top contributors such as Filieri Raffaele, Dwivedi Yogesh K, and Mariani Marcello M. Despite these key contributors, the field exhibits limited collaboration, with a preference for independent research. The most prolific institutions were identified as the State University System of Florida, City University of Hong Kong, and University System of Georgia. Country-wise, England, the USA, and China had the highest centrality, highlighting their significant influence in this field.
Thirdly, the co-occurrence analysis of keywords in movie revenues and eWOM identified "online reviews," "impact," "social media," "information," and "trust" as the most frequent terms. Keywords like "information," "model," "consumer
- A social network owned by "Meta", which is recognized as extremist in Russia
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loyalty," and "brand" were central to the research discourse. Hotspot analysis clustered keywords into ten themes, such as word-of-mouth content, spokesman credibility, opinion platform, publication pattern, Korean consumer, positive emotion, online recommendation behavior, brand page engagement, movie review, and price service. These clusters underscore the primary research interests and focus areas in the field.
This research highlights the significant role of electronic word-of-mouth (eWOM) in movie revenue, emphasizing its importance for movie production and distribution companies. The study identified a notable increase in both the quantity and quality control of eWOM on social media, stressing its critical influence during the screening process(Kim et al., 2018). Effective management of negative reviews is essential for ensuring strong box office performance (Kim & Yoon, 2016). Addressing and responding to negative eWOM constructively can mitigate potential damage and turn it into an opportunity for positive engagement.
The analysis suggests that distributors and filmmakers should focus on brand marketing strategies, not only starting from the choice of platform, but also using the effects of experts and celebrities to obtain higher box office by increasing electronic word-of-mouth (Fan et al., 2021). Professional movie review websites and social media platforms like Weibo, and Twitter have been identified as effective channels (Baek et al., 2017; Huang et al., 2017). Additionally, leveraging professional reviews for promotional purposes can generate greater interest from potential viewers, enhancing the reach and impact of promotional efforts (Rao et al., 2017).
The sentiment of reviews, particularly negative sentiment, is a critical aspect that distributors and marketing teams must address. While negative reviews tend to have a greater impact on film decisions and often reduce box office receipts (Gunter, 2018), the impact is usually limited to the first week of release (Basuroy et al., 2003). Instead of deleting negative comments, a proactive and positive response strategy is recommended. Allowing negative comments to exist and responding constructively increases the credibility of reviews and encourages more viewers to engage in discussions and comments. This approach not only enhances the authenticity of feedback but also fosters a more engaged and interactive audience, ultimately benefiting the film's overall reception and revenue (Lee et al., 2017).
The analysis combines insights from the most cited articles over the past decade, including works by Ladhari R (2015), Rosario AB (2016), Liu ZW (2015), Erkan I (2016), and Filieri R (2018). Additionally, through the analysis of burst keywords in the literature, the top five keywords with the longest burst duration were identified as "information," "film critics," "search," "electronic commerce," and "dynamics." Recently, new burst keywords such as "consumer engagement," "purchase intention," "information quality," "values," and "continuance intention" have emerged. This indicates a recent research trend focusing on integrating various review platforms or social media platforms. Distributors should enhance consumer engagement, improve the quality of reviews, and encourage consumers
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to increase their willingness to watch movies for the first time or make repeat purchases, thereby boosting box office revenue.
Conclusion
This research provides a comprehensive overview of studies on electronic word-of-mouth (eWOM) in the context of movie revenue, highlighting the intricate relationship between these domains. Utilizing a bibliometric approach, this study analyzed author collaborations, geographic distributions, keyword usage patterns, and temporal trends through a dataset comprising scholarly works indexed in major citation indices from 2006 to 2024. Quantitative analysis and visualization using CiteSpace software identified significant trends, research focal points, and emerging themes.
The practical implications of these findings for movie distributors and marketing professionals are substantial. Understanding the impact of eWOM allows for more strategic marketing decisions, especially in the digital age where consumer opinions on social media can significantly influence movie success. By focusing on platforms where target audiences are most active, distributors can maximize their promotional efforts. Improving review quality and managing consumer engagement effectively can enhance the credibility and attractiveness of movies. Additionally, addressing negative reviews constructively rather than deleting them can build trust and foster a loyal audience base. This strategic approach not only improves immediate box office performance but also cultivates long-term consumer relationships, leading to sustained success in the competitive film industry.
Limitations and future research
While this study offers valuable insights, several limitations must be acknowledged. The findings are derived from a bibliometric analysis, which, although comprehensive, may not capture the full breadth of cultural nuances and genre-specific differences. The generalizability of the results across different cultural backgrounds and movie types remains uncertain. Future research should consider these variables to understand better how eWOM influences movie revenues in diverse contexts. Additionally, the reliance on specific citation indices may have excluded relevant studies not indexed within these databases, potentially limiting the scope of the analysis. Addressing these limitations in future studies will enhance the applicability and robustness of the findings across various cultural and cinematic landscapes.
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