International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 5, Issue 3, March 2016
ISSN 2319 - 4847
Does Online Rating Matter in China?An
Empirical Test Based on Data from 177 Chinese
Movies
Zhengshan Liu1, Guangmin Hou2 , Peng Peng3
12
3
Beijing Film Academy, Beijing, China
Research Institute for Fiscal Science,Ministry of Finance,China
ABSTRACT
Whether public commentary has significant influence on box-office, and how the influence takes place, are long argued
propositions. This paper utilizes data of 177 Chinese movies to investigate the relationship between online rating and box-office
of movies.It shows that paid posters influence the rating of movies. Including control variables such as director, movie stars,
scheduling, online rating and rating numbers in the model, the study shows that popularity is a more significant variable than
ratings, and one of the three largest Chinese movie websites has more reliable ratings than the other two competitors. The
results show that online rating has a significantly positive effect on the box-office of movies by fully considering the
heterogeneity of movies.
Keywords: Online Rating, Paid Posters, Heterogenity of Movies
1.Introduction
Rational consumers will search for information from all kinds of channels when making their decisions. Public
commentary, or Word of Mouth (WOM) is considered as one of the most important resources of information for the
box-office of movies[1]-[2]. However, there is no consensus about the relationship between the WOM and the boxoffice of movies.
This study is focused on Chinese movie market that has long been neglected. The academic focuses on Chinese movie
market lag behind those produced by the big studios, i.e. in Hollywood, who have already enthusiastically barged into
Chinese movie market. Let alone the studies on the effects of WOM.
This paper collected data from 177 movies released in China to study whether the commentaries online has significant
influence on box-office, as well as to compare it with oversea results. It contributes to the literature showing that paid
posters influence the rating of movies, popularity is a more significant variable than ratings, and online rating has a
significantly positive effect on the box-office of movies in the Chinese movie market.
2.Literature Review
Recent researches show that professional suggestions and comments play key roles in many aspects of consumption
decisions[3]-[8].
Researchers regard many functions of comments. Cameron (Prime Minister of the United Kingdom of Great Britain
and Northern Ireland) believes that comments have the functions of advertisements and information spreading (such as
the information given by comments on new movies, new books and new songs), reputation construction (such as critics
often pay close attention to promising young stars), consumption experience accumulation (such as comments posted as
impressions of commentaries), and influencing preferences (such as consumers will consider the comments of other
people as references for self differentiation and or resources of attraction). In the field of movies, Austin[10] suggests
that critics would help the masses to differentiate movies, to understand the main idea, as well as to strengthen social
contacts (people can have conversations with friends when they read new critics of a movie).
Another strand of research papers focuses on the relationship between comments and box-office[11]-[19], and find that
every "star" added (5 star refers to a masterpiece and 1 star refers to worst movie) positively influences the box-office of
a movie. Later studies such as Litman and Kohl[12], Wallace, Seigerman, and Holbrook[13], Sochay[15], Prag and
Casavant [22], and Litman and Ahn [17], also come to the same result. However, Ravid[19] and Reinstein and
Snyder[33] suggest that there is no significant relationship between comments and box-office.
In recent years, as Internet spreads, online comments or "Word of Mouth" (WOM), have been considered playing more
notable roles. There is increasing literature on this topic[24]-[29]. Kim et al.[31]insist that online comments has high
capacity on influencing box-office, while traditional comments by experts can only influence local American movie
Volume 5, Issue 3, March 2016
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Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 5, Issue 3, March 2016
ISSN 2319 - 4847
market, and cannot significantly influence international box-office. Nevertheless, exponentially growing online
comments are considered to reflect the taste of masses well, even as the declaration of professional comments[32].
Loads of researches have demonstrated that, movie industry has become the soil where studies on WOM root, especially
for economists and marketing researcher [4]-[6]. It largely counts on: firstly, the price of movie ticket is fixed, which
makes it easier to study than other industries (such as the studies from Chevalier and Mayzlin's[24] on publishing
industry, or Zhu and Zhang's [28] study on video games), thus the effect on box-office is easier to measure[32];
secondly, consumers collect information, watch movies and post comments online are common phenomena in new
media age, therefore the data is fruitful, which has facilitated relating studies[32].
However, how the online comments influence the box-office is still an arguable proposition. The first question is: do
online comments really influence the box-office?
Although Ravid[6], Reinstein and Snyder[33] have already denied the relationship between the ratings of online
comments and box-office, more and more researchers tend to suggest that online comments do take significant role in
determining the success and failure of box-office. Eliashberg and Shugan[4] devide online comments into two
categories: the influencer and predictor. The authors show when influencers make decision based on online comments,
the ratings will significantly influence the box-office. Eliashberg and Shugan's study insist that comments do influence
the box-office. Einav [34]argue that conventional wisdom has demonstrated the WOM formed by online platforms, is
necessary for the success of box-office. Lee Yoong Hon[35]suggest that the WOM effect is ever growing, especially
when the technology advances, for instance, using the comments on Twitter and Facebook.
The second question is, the valence or the volume of online comments, which plays the bigger role?
Liu [36], Duan, Gu and Whinston [37] suggest that the volume of comments has significant effect on the box-office of a
movie. Some people argue that the comments only have effect on box-office when it has accumulated to a certain
volume [32]. However, Chintagunta, Gopinath and Venkatraman [26] argue that volume is not the determinant, and
when counting in the valence, volume does not matter anymore. As Chintagunta, Gopinath and Venkatraman[26] use
cross-sectional data, Suman Basuroy and Richard T. Gretz[32] utilize cross sectional and time series data to obtain the
similar results.
The third question is about the asymmetric effect of positive and negative comments on the box-office of a movie.
Skowronski and Carlston[38] insist that negative comments harm the box-office. Suman Basuroy, Subimal Chatterjee
and S. Abraham Ravid [6] focus on how comments influence the box-office, and find that negative comments have
greater influence than positive comments by analyzing data from 200 movies in the period between 1991 and 1993.
Nevertheless, some studies considered the influence of movie stars would balance the influence from negative
comments[12].
From these researches mentioned above, there are three summaries:
First, although lots of researches insist that online comments have significant influence over box-office, there are still a
few opponents. Thus empirical study is necessary to test this proposition with new data.
Second, how does online comments influence box-office, and whether the influence of positive and negative comments
is different, empirical tests are needed.
Third, existing studies mainly focus on data from European countries, America, Japan and Korea in Asia. Few studies
focus on China’s movie market. In fact, China is one of the most important market in the movie industry. In 2012, the
box-office of China bumped into the second largest in the world, and the growth rate was much higher than its GDP
growth (around 7.7% in 2013). In 2015, China box-office was 44.069 billion RMB, year on year growth rate was
48.7%. Hence, it is meaningful to study China data both for the theoretical and practical purpose.
What follows in this paper, are theoretical analysis, and the data used with economics methods, as well as the
discussions from different angles, which all comes to the final result.
3.Theoretical Analysis
Let the supply function of movie as . From the characteristics of a movie, as soon as the movie has completed its
production, all costs become suck cost, and the marginal cost of copies can be seen as zero, which refers to
. Of
course, to promote the box-office, a lot of fees need to be paid for advertisement, and sometimes it may cost about 50%
of total. However, the promotion cost is hard to be observed, thus in this paper, it will be set as a fixed variable .
The demand function of movie, , is determined by the quality of movie , and the advertisement fee . Since movie is
non-reusable consumed, before buying the ticket consumers cannot have direct understanding about the quality of a
movie. Then consumers will collect all reachable information to reduce transaction expenses, and to make indirect
inference about the quality of the movie. Set the quality function of the movie as
.
Just as the analysis by Barzel[40], consumers will adopt all kinds of methods to examine the quality of the product, to
reduce the transaction fee. When the product is reachable, for instance, apples, consumers will foretaste to examine the
quality of apples; however, when the product is non-reachable, such as human capital, consumers need to use proxy,
such as diploma. Thus the demand function of a consumer is:
, that is the utility function from watch
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 5, Issue 3, March 2016
ISSN 2319 - 4847
a movie, is determined by the advertising fee and the extent of examination of the consumer. The market equilibrium
implies that
Obviously, early buyers can only understand the information through prevue or advertisement, such as the title,
director, star performers, and synopsis of the story as criteria.
Suppose the quality of a movie follows the probability of p to be a good movie, and follows the probability of 1-p to be
bad. Since the information is scarce before watching, the quality function for early movie audiences is:
Before watching the movie, the utility cannot be measured, thus the p may follow an even probability of 50%. Early
consumers cannot collect enough materials to fully understand the quality of the movie before watching, and some of
them may post their impressions (especially ratings) on professional movie online platforms (such as IMDb, Douban of
China, among others).
Later consumers may get more information, especially the comments from audiences of opening weekend. Those
comments may determine the following decisions. Suppose there are n audiences whose average ratings on a movie is
. Meanwhile, for later audiences, the effect of advertising may be replaced by the online comments, in the demand
function for later buyers where can be neglected. Thus,
where
Since the comments online are anonymous, which seems to be more reliable, comparing to the professional critics who
may be easier influenced by other pressure. The comments online are free of pressure and more diversified. Many later
consumers may consult these comments before going to the cinema. This argument is arguable, which will be tested by
the data from China in the following paragraphs. Audiences may also have bias since they may be diversified by their
taste on different type of movies. Thus the heterogeneity of movies should also be examined.
Besides, online comments may also encounter signal failure, when movie producers hire "paid commentaries" to give
good ratings to their movie and low marks to components. Therefore, the influence of "paid commentaries" should also
be considered in later analysis.
4.Data Sources and Descriptive Statistics
This paper collected data from three largest online movie platforms in China, which are M1905 (www.1905.com),
Mtime (www.smtime.com), and Douban Movie (movie.douban.com). The data covers:
(1) Movie title, director, Stars, box-office, released data, movie genre, from M1905.
(2) Valence of movies, from M1905, Douban and Mtime. The data from M1905 and Mtime includes average ratings,
poster number. Douban provides even more detailed information about numbers for each level of ratings.
From 2014 to 2015, there were 745 movies released in China. After eliminate missing data, there are 177 movies. The
collected data has been formatted as the following:
(1) For the data from Douban Movie, the study normalizes the two contrary indicators, "Very bad" and "Worse". Other
data such as the valence, box-office and poster numbers, the study just keeps the original value.
(2) Evaluation for dummy variables.
Evaluation for released date. In recent years, audience who aged from 19 to 40 has formed 87% of total audiences.
Meanwhile, audiences aged in this range mainly watch movies on holidays. Thus movies released on vacations are
evaluated as 2, and those released on weekends as 1, others as 0.
Evaluation for directors. If the director won Oscar Prize, evaluated as 2, Famous Chinese directors such as Yimou
Zhang, Anhua Xu, Ye Lou, Zhaohui Mai, etc., evaluated as 2, and Han Han, Jingming Guo who are famous writers and
popular in young people, also evaluated as 2. For those less famous directors will be evaluated as 1. And the other
directors will be evaluated as 0.
Evaluation for star. The criterion is for the leading two stars, if the actors won Oscar Prize or rank top 10 in China or
oversea, evaluated as 2, for actors ranking top 10 to 30, evaluated as 1, and others will be evaluated as 0.
Evaluation for sequel works. Yes as 1, and no as 0.
All data from the 177 movies are described in Table 1 and Table 2.
Table 1 Description Statistics: Box-office, online ratings and posted numbers
N
Box office
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Minimum
Maximum
22
243952
Mean
31846.66
Std. Deviation
42437.564
Page 49
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 5, Issue 3, March 2016
ISSN 2319 - 4847
Valence-M1905
177
6.70
9.20
7.88
7
Volume-M1905
177
208
41692
3873.84
6237.53
Valence-Douban
177
3.20
9.10
6.16
1.36
Volume-Douban
177
360
284030
51424.14
54368.24
Valence- Mtime
177
1.60
8.80
6.43
1.23
Volume- Mtime
177
279
30879
6730.33
6893.69
Notes: Unit notation: Box-office, million RMB.
Table 2 Description Statistics: Classified Douban Ratings
N
Maximum
39.10%
Mean
15.43%
Std.
Deviation
10.96%
Variance
120.16
grade V
177
Minimum
0.60%
grade IV
177
1.60%
53.30%
24.978
15.61%
243.75
grade III
177
6.20%
56.30%
34.17%
12.18%
148.32
grade II
177
0.60%
39.10%
15.43%
10.96%
120.16
grade I
177
0.30%
75.60%
12.93%
16.62%
276.35
Notes: grade V, Most appreciated; grade IV, Appreciated; grade III, Not too bad; grade II, Worse; grade I, Very bad.
5.Empirical Results
Next in this paper, binary or multiple regressions will be adopted to test the relationship between online ratings/posted
numbers and box-office from different angers.
Firstly, this paper is going to test the relationship between online ratings from the three websites and the box-office.
Here the OLS analysis will be adopted. The testing model is:
Where refers to M1905, Douban, and Mtime. The results are demonstrated in the Table 3.
Table 3 Relationship between Box-office and Valence
Notes: *** refers to P<0.01, and the values in the parenthesis are t-statistics of the coefficient estimates.
From Table 3, the results show that box-office has significant positive relationship with valences for all three websites.
Although the R-squared values seem not to be "high enough", this paper does not aim at forecasting, thus low Rsquared value won't matter. In a paper discussing the importance of movie comments, published on PNAS on Jan 20th,
2015, most of the R-squared values are even lower than 0.1 [41].
Secondly, the correlation between hierarchy ratings and Douban and box-office is tested. Some researchers argued that
online comments could have been manipulated[29], thus paid commentaries will bias the comments data. However,
there also exists studies that suggest both good and bad comments do not influence the box-offce during the releasing
week [30].
Normally, paid commentaries tend to give high ratings to employer's movie, and low ratings to the competitors. The
study uses the following model to test it:
where i refers to the ranking of ratings the grade (there are five rankings from Douban). The results are showed in
Table 4.
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Table 4 Regression of Rankings from Douban and Box-office
From Table 4, only the regression on Grade III does not pass the significant test. Normally, if there does not exist any
paid poster, all five regressions should have passed the significant test. The results from Table 4 show that the p-values
of grade V and Grade I are higher than other Grades. It may suggest that paid commentaries who tend to give higher
ratings to their employer. However here the result does not provide us with the solid evidence.
In Chinese culture, people tend to give "middle" opinion, which may refers to conservative or vague ideas. Thus Grade
III is a ranking reflecting the neutral but also vague opinion. Therefore, this paper will neglect the result of Grade III.
Thirdly, this study considers the effect of directors, stars, and releasing date in the model. Here in the econometric
model, new variables such as director, star, and releasing date are added, and the model is:
The results are showed in Table 5.
Table 5 Regression on Extended Variables
Notes: * P<0.1; ** P<0.05; *** P<0.01
From the results in Table 5, it is obvious that only volume of comments and sequel, the two variables passed the
significant test. This may imply that directors and stars are not that important. Market attention is more important,
even when the attention was drawn by negative or arguable comments. From this point of view, awareness and
attention focused are necessary for high box-office. In fact, some previous studies also suggested that no matter the
comments are negative or positive, the volume of comments is more important for box-office, such as Duan et al.
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(2008), and Liu(2006). Thus, this paper tests the relationship between volume and box-office, one by one using the
following model:
where i refers to the volume of each websites, which are Volume-M1905, Volume-Douban, and Volume-Mtime. The
results are presented in Table 6.
Table 6 Regression on Volume and Box-office
Notes: *** P<0.01.
From the results, there are two phenomena. One is for all three websites, volume does have positive relationship with
box-office, and the other is the R-squared values are significantly raised. Then we need to test another model, where
valence variable is added:
where i refers to each website, Volume-M1905, Volume-Douban, and Volume-Mtime. The results are in Table 7.
Table 7 Regression on Volume, Valence with Box-office
Notes: * P<0.1; ** P<0.05; *** P<0.01.
From Table 7, p-values show that only the coefficient for Valence-M1905 has passed the significant test. It is obvious
that for Douban and Mtime, the volume variable has replaced the influence of valence variable. Comparing the results
with Table 3 and Table 4, the R-squared values are also enhanced. Thus, this paper has reached the conclusion that for
Douban and Mtime, the volume of comments is the most important variable to influence box-office. The positive
relationship between valence and box-office has been replaced by volume, and it may also imply the existence of paid
commentaries, besides the attention the movie has drawn.
However for M1905, its valence still has significant positive relationship with box-office. It may suggest that the
valence of this website is more objective and reflecting the taste of consumers. Otherwise it may also reflect the
influence of this website's valence on box-office.
Lastly, this paper is going to consider the genre of movies, from which perspective to differentiate the relationship
between valence and box-office. Earlier analyses in this paper embed a presumption: each movie was abstracted as
homogeneous product. In fact, each movie has double attributes, both artistry and economic [15]. Movies should not be
treated as standardized product. Thus following analysis, the study takes genre into account. The testing model is:
where j refers a specific type of movie, one of which from Fantasy, Romance, Action, Animation, Sci-Fi, Drama, and
Comedy. Each type of movie will be regressed by the comments from the three websites. The results are showed in
Table 8 to Table 14.
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From the results listed above, for each type of movies, the valence has significant positive relationship with Box-office.
Although the R-squared is not high, it is obviously higher than previous research such as Wasserman et al.[41].
6.Conclusion
This paper has collected data from 177 movies released in China from 2014 to 2015. According to the economic
analysis, valence online does have significant relationship with box-office. Meanwhile, there are a few special points to
make:
Firstly, to be differ from the research of Mayzlin, Dover and Chevalier [29]and Liu [30], this paper find that paid
commentaries may impact the objectivity of websites and make the valence not to reflect the box-office. However, in
total the online valence does not deviate too much.
Secondly, Volume of comments has larger influence than valence, and the comments from M1905 best reflects the boxoffice of movies.
Thirdly, to take the genre of movies into account, this paper find that, for each single type of movies, valences from all
three websites reflect positive relationship with box-office.
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