Recipe for Hindi Cinema Blockbuster: Research for Marketing Decisions

Group No. 9 (PGP 2012)
Reechal Vardhan – 1211050
Anubhav Tiwary – 1211171
Manish Dev - 1211274
Vishrut Shukla – 1211314
Varalakshmi M – 121139

Final Project Report
Submitted to: Prof. Ashis Mishra
in partial fulfillment of the course
Research in Marketing Decision (Term III)
( 20th March 2013)

Project Report: Recipe for Hindi Cinema Blockbuster | 1

Executive Summary
The objective of the research project was to identify factors determining the success of Hindi movies for
four different genres i.e. action, thriller, drama and romance and deliver genre-wise statistical models to
predict whether a newly released Bollywood movie will be a blockbuster, superhit/hit or a flop at the box
office (basically to predict the extent of success of the movie).
The researchers found it prudent to study the context in which the industry is based before taking up the
marketing research project in detail. The rationale behind the activity was to understand the industry in
detail, identify emerging trends and justify the objectives defined for the project. The financial state and
historical evolution of the Hindi movie industry was studied in detail during the exploratory phase of the
project. Recent emerging trends in the industry were also identified as they bear relevance to the scope
of the project.
During the exploratory research phase of the project, the researchers deeply understood the classification
paramenters for movie successes and methodologies adopted for classification. An exhaustive list of Hindi
movies and associated information was created right from the period 1980s onwards till present day.
Genre-wise audience preferences for various paramenters while deciding to watch a movie in the
threaters were also collected and then primary research was conducted to gauge the importance of
various factors in the viewer’s decision. The data collected via online questionnaires were factor analysed
to obtain a reduced set of parameters which are important to determine a movie’s success at the box
office.
Finally, as part of the conclusive research phasethe researchers devised a plan to test the set of
hypotheses derived from the elaborate exploratory research carried out untl that point. With the aid of
analysis tools such as the Multiple Discriminant Analysis, the researchers achieved the intended
deliverables of the project - genre-wise statistical models which were capable of predicting the success
category (blockbuster, superhit/hit or flop) for any new Bollywood movie by processing collected
audience ratings for the movie on the genre’s correspondingly identified “decision factors”.


Contents
1. Introduction ............................................................................................................ 4
a) The Indian Media and Entertainment Industry .......................................................................... 4
b) An Overview of the Hindi Cinema Industry ................................................................................ 4
c) Objective of the Research Project ................................................................................................ 4
d) A Contextual Study of the Industry and Its Trends .................................................................. 5
e) Need for the Research Project ...................................................................................................... 8
2. Methodology ........................................................................................................... 8
a) Contextual Study............................................................................................................................. 8
b) Exploratory Research ..................................................................................................................... 8
c) Conclusive Research ....................................................................................................................... 9
3. Exploratory Research ............................................................................................10
a) Understanding the Classification of Movies .............................................................................. 10
b) Insights Derived From the Referred Journal ............................................................................ 10
c) Exhaustive List of Movies and Related Information since 1980s .......................................... 11
d) Sum Total of Parameters Affecting Movie-Watching Decision in Theater............................ 11
e) Online Questionnaire: Audience Preferences Towards Success Parameters ....................... 11
f) Factor Analysis .............................................................................................................................. 12
4. Conclusive Research .............................................................................................16
a) Online Questionnaire: Collecting Audience Ratings for Representative Set of Movies ...... 16
b) Discriminant Analysis ................................................................................................................... 17
5. Conclusion ............................................................................................................22
a) Conclusive Remarks ..................................................................................................................... 22
b) Scope for Further Research ........................................................................................................ 23
c) Limitations of the Research Carried Out ................................................................................... 23
6. Appendix ...............................................................................................................24
7. References ............................................................................................................95


1. Introduction

a) The Indian Media and Entertainment Industry
Comprising of more than 600 television channels, over 100 million pay cable-TV households, close to
70,000 newspapers and 1,000 movies produced annually, India’s vibrant media and entertainment
industry is going through a never before golden phase of rapid and attractive growth. Enticed by
economic liberalization, high volumes of climbing consumption trends, a near double-digit annual
growth and fast-growing middle class, there has been a renewed surge in investments into the
industry by both domestic and global players. According to the estimates of a recent KPMG-FICCI
2012 report, the industry is slated to touch Rs. 1,457 billion by the year 2016. The growth trajectory
is also backed by strong consumption in Tier II and III cities across India, continued growth of
regional media, and fast increasing new media business.

b) An Overview of the Hindi Cinema Industry
The country’s filmed entertainment industry, which
combines movies produced in all Indian languages, is the
largest in the world in terms of the number of films it
produces (around 1000 annually) and its theatrical
admissions (around 3 billion). Stiil, it continues to be small
in size in terms of the revenue generated, mainly due to low
ticket realization and occupancy levels owing to the diversity
of cinema produced. Moreover, lack of quality content and
rising competition from the US film industry cinema,
commonly known as Hollywood, continue to affect it.
Bollywood, as the Hindi film industry is popularly known, on
the other hand is the largest contributor to the Media and
Entertainment industry’s revenues in India, followed by the
South Indian movie industry and other language cinema
industries such as Begali, Bhojpuri, Marathi and Gujarati. The Hindi film industry produces more
movies and sells more tickets than any other movie industry, with revenues second only to those in
Hollywood.

c) Objective of the Research Project
The following objectives were established to be achieved during the course of the term-wide project:


Identify factors determining the success of Hindi movies for different genres;



Formulate genre-wise statistical models to predict whether a Bollywood movie will be a
blockbuster at the box office after its release (basically to predict the success of a movie by
categorizing it as a blockbuster, superhit/hit or flop).


d) A Contextual Study of the Industry and Its Trends
The researchers found it prudent to study the context in which the industry is based before taking up
the marketing research project. The rationale behind the activity was to understand the industry in
detail, identify emerging trends and justify the objectives defined for the project.
i. Financial Performance: The main underlying aim of the cinema business is to generate greater
profits for the production house and the distributors. With several high budget Hindi releases
throughout the year, 2012 was expected to sustain the growth momentum witnessed in 2011.
The Indian film industry is presently projected to grow at a CAGR of 10.1% to touch Rs. 150
Billion in 2016. The industry is estimated to have grosses Rs. 93 billion in 2011 indicating a
growth of 11.5% vis-à-vis 2010.
ii. Evolution of the Indian Cinema: The way Bollywood movies were produced in the 1950s is
very different from the landscape in the 21st century. The researchers have identified the broad
changes that the industry has been through over the decades and some probable explanations for
them. It is important to understand the journey of the Hindi cinema to justify the research
objectives and identify parameters for movie success.
The first phase of Hindi cinema may be considered to
be in existense from the 1940s till the early 1960s.
Often referred to as the “Golden Era of the India
cinema” where critically acclaimed movies like Awaara
(1951), Shree 420 (1955) and Mother India (1957)
were produced. These movies revolved around the
theme of the “common man”, depicting the various
struggles faced by him in life, which the audience
could relate to, before finally coming out victorious
owing to his strong moral values. The stories usually
showed the protagonist to be from the poor, deprived
and weaker section of the society who might have
wavered from his path thanks to greed and success,
but would never fail to realize his mistakes and
overcome his troubles in a just manner at the end.
The second phase is classified from the late 1960s and
uptil the early 1980s, when the movies saw a
distinctive shift in their general storyline. Movies like
Aradhana (1969), Anand (1970), Bobby (1973) and
Sholay (1975) denote this period of Hindi cinema. The
themes were more action-based and romantic in
nature and it signified the birth of these two popular
genres. Violent scenes became common in these
movies and a lot of emphasis was given to villains and underworld mafias. The “angry young
man” was introduced in this period, along with an action hero who was admired for delivering
effective punches and kicks and of course winning the lady’s heart in the end. A majority of
movies during this period loosely followed this basic theme.
The third phase runs from the late 1980s till the early 2000s. This period signified opening up of
the industry to diverse themes and represented remarkable shifts in the movie-making procedures
adopted in Bollywood. Advanced graphics and special effects technology was introduced and used
for the first time in Indian cinema. The first Indian sci-fi movie Mr. India, which went on to
become one of the most popular all time hits, was released in 1987. Event though filmamers
started experimenting with different themes, romance still dominated the most mindshare among


all the themes. A few comedy movies were also runaway hits during this phase. This period
brought forward the obsession of Indian production houses with shooting movies at really exotic
locations abroad with dance sequences on foreign roads and hill tops alike.
The last and currently ongoing phase of Hindi cinema started in the late 2000s. This period has
introduced a number of technical advancements in movie production styles. From Koi Mil Gaya
(2003) to superhero action movies Ra.One (2011), producers put a greater emphasis on the
visual effects rather than the storyline and content of the movies. A lot of movies have been shot
abroad, with only a mere symbolic connection to India. What really is interesting is the whole Rs.
100-crore film phenomenon (also a hype to some extent) that seems to have become the new
benchmark in Bollywood these days, and which gets producers and distributors to leave no stone
unturned in their attempt to break the barrier. In essence, a Rs. 100-crore film has become an
essential element in an actor’s profile in order to establish him/her as a “bankable” star’. Besides
content and cast, effective marketing and public relations have played a big role to play in
transforming the economics of the film trade in this phase.
iii. Key Emerging Trends: Some key emerging trends in the current ongoing period of the Hindi
movie industry identified based on the contextual study performed by the researchers were as
follows:
a. Emergence of new sources of revenue: Although revenues from the theater segment
constitute around 60% of the overall revenue generated for a movie, other revenue streams
have begun to make a meaningful contribution. The trend of pre-selling satellite and homevideo rights gained momentum in 2010, and has enabled producers to de-risk their business
models. Even films that are due for release in 2013 are witnessing negotiation for satellite
and new media rights. Revenue from new media, including mobile and online rights, is
expected to increase after the recent introduction of 3G services by mobile operators. In
addition, film production houses have the opportunity to monetize their content through
gaming on mobile and online platforms. New sources of revenue will reduce a movie’s
dependence on its theatrical performance for it to achieve success and is expected to enable
fuller exploitation of content.
b. Collaboration with international studios: International film studios such as Warner Bros.,
Disney, Fox and Dreamworks have entered collaborations with local film production houses to
develop Hindi and regional movies. Walt Disney, who earlier held a 50% stake in UTV, has
now acquired a controlling stake in UTV Software Communications. Viacom18 has also
entered a deal with global movie company Paramount Pictures to market and distribute the
latter’s movies in India, Bangladesh and Sri Lanka. It has already ventured into production of
Hindi language movies, and the new deal is expected to help it create a distribution network.
Local film production can leverage the experience of these international studios to expand
their international reach and incorporate enhanced project planning and cost controls. A case
in point is My Name is Khan, which was distributed in unexplored markets, with innovations
such as taking the lead actors to the NASDAQ stock exchange. The success of the movie
demonstrates the potential of Indian films abroad.
c.

Rise of 3D cinema and advent of special effects: 3D was a prominent theme in 2010 and has
amply demonstrated its significant potential with benefits such as enhanced audience
engagement, increased ticket prices and the exclusivity of the medium, i.e, the theaters. The
success of Avatar has taken 3D movie-making to new heights. Multiplexes could look at the
feasibility of investing larger amounts on 3D screens to meet the growing demand to view 3D.
Last year, the Bollywood film Ramayana, was also released in 3D. Therefore, a new window
of opportunity could open if Bollywood is able to produce high quality 3D content. Releasing
movies with spectacular special effects, such as in Avatar, could be the answer to bringing


people back to the theater. Rajnikanth’s forthcoming movie ‘Kochadiyaan’ may set the stage
for motion capture technique based movies in India.
d. Rationalizing the movie slate: In line with the global trend, Indian movie production houses
have cut down on the number of movies they release every year, mainly due to rising movie
production costs, which is leading to difficulties in securing funding for projects. The year
2010 witnessed the release of around 150 Bollywood films, as compared to the earlier
average of 300 films per year. The movie business has been hard hit and production houses
would do well to reduce volatility in their revenues by producing varied budget films within
different genres. A shift toward a portfolio approach for movies with small, medium and large
budgets is a positive development in the sector.
e. Content-driven films giving big-budget films run for their money: The last year and a half has
witnessed strong performance by films that have relied on strong and differentiated content.
As against a few years ago, when the success of a film critically hinged on its star-cast, recent
times bear witness to the fact that Indian audiences have matured and are appreciating films
driven by strong content and not necessarily star power. These include The Dirty Picture,
Kahaani, Paan Singh Tomar and Vicky Donor, films that were able to rewrite rules and do
good business at the box office.
f.

Focus on niche movies: The recent success of small budget, niche movies such as No One
Killed Jessica, Peepli Live, Well Done Abba and Dhobi Ghat has re-emphasized the importance
of content-driven films. While these movies are produced on tight budgets, strong content
and word-of-mouth marketing can bring high returns to studios. The success of such movies
has at best been patchy over recent years, but a few failures should not deter industry
players from backing good scripts with requisite funding. In addition, refined audience tastes
and the advent of miniplexes to cater to the tastes of targeted audiences is likely to drive the
production of more such movies, which is in sync with the portfolio approach adopted of late
by the production houses.

g. Advent of digital cinema and the growth of multiplexes: The growth of multiplexes has
improved the movie-going experience for Indian audiences and has led to increased per-ticket
realization. Rising urbanization and growing disposable incomes are also driving increased
investments in multiplexes. In addition, theaters with low seating capacities allow costeffective screening of movies that are targeted at niche audiences. Companies such as Real
Image and UFO Moviez have facilitated digitization of movies, which curbs piracy and enables
increased release of films across the country — a game-changing phenomenon whereby 60%
of box-office collections are realized in the first week of release of a movie. Thereby, a bigbudget Hindi movie, which would have been released earlier with 400–500 prints, now enjoys
a wider release with almost 1,000–1,500 prints being distributed. However, there is still
further ground to be covered. The average number of screens per million in India is 12, as
compared to the global average of 54. The number of multiplex screens in India is expected
to increase from 1,000 in 2010 to around 1,405 by 2013.
h. Ancillary revenues spiral upwards: The year 2011 saw an increase in the viewership of both
Hindi and English film channels. This has led to positive responses from film broadcasters and
advertisers. An increasing number of films with bankable stars are able to sell broadcasting
rights even before their release. With top film channels such as Star Gold, Set Max, Zee and
Colors constantly competing to secure the satellite rights of large releases, film producers
have an increased bargaining power leading to higher costs being witnessed. Production
houses are capitalising on this in order to recover a significant proportion of the costs before
their films hit theatres. Agneepath was able to recover close to 60% of its production costs
through the sale of its satellite rights. However, in some cases, airing of films on television
soon after their theatrical release has a negative impact on box office revenues.


e) Need for the Research Project
The underlying reason why there has been a shift in the content and the way movies are made in the
last few decades is because the target audience has changed. In the early phases of the Indian
cinema, the target audience were the common masses living in towns as well as villages and that
affected the choice of storyline, issues, cast and protagonists in the movie.
As time shifted, the urban class expanded and the multiplex culture became prominent, the target
audience for present day Bollywood movies have evolved as well. The industryalso has been getting
increasingly more corporatized with every passing year. Several ﬁlm production, distribution and
exhibition companies have been listed on stock markets and have issued shares to public. The
production studios now target the upper middle class or upper class, those with high purchasing
power and primarily living in cities. Most movies today are targeted at young audiences generally
with the content and elements desired by such a target segment. Though it would not be fair to
write-off all modern day Hindi movies as production goods targeted at extracting maximum profits as
an increasing number of main stream cinema productions also focus on eminent political, social and
cultural issues of our societies and in the process, have
seen hugely successful runs at the box office
alongwith huge profits, yet the common view is that
Bollywood has become more professional and profitcentered in the last few decades. Thus, box office
earnings have acquired a centerstage in terms of
speculations, importance and success benchmarkers,
thereby making it essential for the film makers to work
on movie elements that they know would sell, and sell
big, not only in the domestic market, but overseas as
well, since the overseas market brings in significant
returns for the movies.
Movies are considered as experience goods in which the quality, as perceived by the consumer, is
only fully revealed after the good is consumed. Filmgoers have an expectation and an image of the
film’s likely quality, but this expectation can be either exceeded or unfulfilled based on the content of
the movie. The present need is to be able to reasonably predict the success of a Bollywood movie
based on audience feedback on a reduced set of parameters after they have watched a movie in the
theatre, which is precisely what the researchers set out to do.

2. Methodology
Keeping the objectives of the research project in mind, the following activities were performed by the
researchers during the course of the project work:

a) Contextual Study
The researchers studied the path dependence and historical evolution of the Hindi cinema over the
years and identified the current emerging trends for the industry. The findings have been detailed in
the previous section and were used to justify the objective of the research project.

b) Exploratory Research
i.

The researchers carried out secondary research first to understand and identify the
parameters to classify Hindi movies of recent times in one of the three categories namely:
blockbuster, superhit/hit or a flop movie. For example, previously, a blockbuster, superhit/hit or


flop movie was determined based on the audience responses and evaluation by critics. However,
the parameters of classifying movies have undergone major changes in the recent times. These
days, a blockbuster is defined as a high-budget production aimed at mass markets on which the
financial fortunes of the film studio or distributors depended and which generates massive
amounts of profits for the stakeholders.
ii.

Further, the researchers also referred to a published journal paper on the same topic “The
Main Determinants of Bollywood Movie Box Ofﬁce Sales” authored by Marc Fetscherin which
appeared in the Journal of Global Marketing (23:461–476) in the year 2010.

iii.

Further as part of the secondary research, an exhaustive detailed list of Bollywood movies
starting right from the 1980s era uptil the present day was painstakingly compiled with relevant
details about the movies alongwith their classification in one of the three buckets: blockbusters,
superhits/hits and flops.

iv.

Based on the insights obtained from the secondary research and analysing the content of the
primary data of movies, the researchers deduced a sum total list of parameters that influence
movie-goers while taking the decision to watch a Bollywood movie in the theatre.

v.

The researchers then undertook primary research to identify the broad factors derived /
grouped from the sum total list of parameters obtained from the secondary study for each of the
four genres of Hindi movies being considered for the project i.e. Action, Thriller, Romance
and Drama. The methodology adopted to fulfill this requirement was an online questionnaire
floated to a diverse set of respondents with varying demographics.

vi.

The preference data obtained from the online survey was factor-analysed to identify the
reduced factors used as a basis of movie-watching by the audience. The activity was performed
independently for each of the four genres. Based on the results of the factor analysis, the
researchers proceeded to the next step of hypothesis formation. The hypotheses formed were
to be tested in the next phase of the research.

c) Conclusive Research
i.

Aimed with the hypotheses to be verified, the researchers carried out another round of primary
data collection via multiple online questionnaires to collect the audience’s ratings for a
diverse set of recent Hindi movies on each of the identified set of parameters for the respective
genre. The movies were selected subjectively based on the criteria of commanding a high recall
in the minds of the respondents and appropriateness in terms of representing one of the three
success categories (blockbuster, superhit/hit or flop) for the corresponding genre.

ii.

Multiple Discriminant Analysis was thereafter used on the collected data to establish
procedures for classifying movies (individual test units) from the set into three distinct groups
based on their classification as blockbusters, superhits/hits or flop productions. The activity was
performed independently for all four genres to understand the relative importance of each of the
factors identified during the exploratory research phase in segregating the movie types. The use
of this technique enabled the researchers to assign group membership (blockbusters,
superhits/hits or flops) from among the set of movies in every genre using the corresponding
factor ratings (multiple predictor variables). Four different linear discriminant equations were
obtained in the following form: Di  a  b1 X 1  b2 X 2    bp X p .

iii.

The final outputs of the conclusive research were genre-wise statistical models (basically
the linear discriminant equations) which can be used for predicting the category (blockbuster,
superhit/hit or flop) of any new movie when primary survey data i.e. ratings collected across the


corresponding factors for that genre obtained from actual movie-goers within the first 2-3 days
after movie release is entered into the model.

3. Exploratory Research
a) Understanding the Classification of Movies
As outlined in the methodology section above, the first task taken up by the researchers during this
phase of the project was to clearly understand the crtieria for classifying movies in the three
categories i.e. blockbusters, superhits/hits and flops.
The basic mindset prevailing in the industry and
obtained via available literature on the topic is that
when the term 'blockbuster' is mentioned for a movie,
the measure for any movie was the profit generated
and not just the revenue. This made sense since the
cost of producing movies in the Hindi film industry
varied greatly over the years and within each year as
well. It is noteworthy that collections of Rs. 100 crore
are by no means a new phenomenon, though it is fairly
recent. According to figures available with online
commuity BoxOfficeIndia.com, there have been 37
films that have hit the prized Rs. 100 crore limit in
terms of worldwide gross, adjusted for inflation, in the
history of Indian cinema. According to Siddharth Roy
Kapur of UTV Motion Pictures, if a film makes 10 to 15
percent of ROI (return on investment) then it is usually
considered a success. Hence, the criteria for classifying
movies is somewhat hazy.
According to some estimates, three times the production budget in box office revenues is the
accepted norms in the industry but different trade analysts follow slightly dofferent practices. The
marketing budget usually does not get counted in the budget taken for this calculation. There is also
a stress on the fact that only theater revenues and corresponding profits should be considered for
classifying the movie correctly. Hindi movies like Rocket Singh and Paathshaala, which had costs of
Rs. 17 crore and Rs. 10 crore respectively and released by the producers so even when they did not
find a audience, were still profitable due to low costs and revenues coming in from streams like
satellite TV and DVD sales. Films like Kites on the other end require to achieve “All Time Blockbuster”
status business in India or huge business overseas to get near the premium prices they were
purchased at by the distributors. However the argument that box office has nothing to do with
satellite, music, DVD, etc. makes perfect sense according to the researchers since box office is about
the public watching a movie at a theatre, be it in India or abroad.

b) Insights Derived From the Referred Journal
The journal paper groups the success factors determining Bollywood movie sales in the following four
categories: a) Product Related; b) Brand Related; c) Distribution Related and d) Consumer Related.
This insight helped the researchers in identifying the sum total list of factors that may be important
to the audience to consider while making decision to watch a movie in a theatre.


c) Exhaustive List of Movies and Related Information since 1980s
The exhaustive list of movies classified is provided in the Appendix. The following details about the
movies were collected by the researchers as part of this activity: movie name, year of release, star
cast, director, production house, genre, IMDB rating, movie category (blockbuster, superhit/hit, flop),
period of release. A sample screenshot of the collected data is as given below:
Movie Name
Khuda Gawah
Shola aur Shabnam
Tahalka
Vishwatma
Jigar
Bol Radha Bol
Khiladi
Angaar
Aankhen
Khalnayak
Darr
Baazigar
Tirangaa
Damini
Anari
Hum Aapke Hain Kaun
Mohra
Krantiveer
Raja Babu
Main Khiladi Tu Anari

Year
08/05/1992
23/01/1992
26/06/1992
24/01/1992
23/10/1992
03/07/1992
05/06/1992
01/01/1992
09/04/1993
15/061/993
24/12/1993
12/11/1993
29/01/1993
30/04/1993
26/05/1993
05/08/1994
01/07/1994
22/07/1994
10/01/1994
23/09/1994

Starcast
Amitabh Bachan, SriDevi
Govinda,Divya Bharati
Dharmendra, Naseeruddin Shah
Naseeruddin Shah,Sunny Deol,Divya Bharati
Ajay Devgan,Karishma Kapoor
Rishi Kapoor,Juhi Chawla
Akshay Kumar,Ayesha Jhulka
Jackie Shroff,Dimple Kapadi,Nana Patekar
Govinda,Chunkey Pandey
Jackie Shroff,Dimple Kapadi,Nana Patekar
Sunny Deol,Juhi Chawla,Shahrukh Khan
Shahrukh Khan,Kajol
Raaj Kumar,Nana Patekar
Rishi Kapoor,Sunny Deol,Meenakshi Sheshadri
Venkatesh Daggubati,Karishma Kapoor
Salman Khan,Madhuri Dixit
Akshay Kumar,Raveena Tandon,Sunil Shetty
Nana Patekary,Dimple Kapadia
Govinda,Karishma Kapoor
Akshay Kumar,Saif Ali Khan,Shilpa Shetty

Revenue
11.75
10.75
10.25
9.5
9
8.5
4
7.9
25.25
21.5
19.25
14
12.25
11.75
10.75
135
21.5
16.5
13.5
11.75

Director
Mukul Anand
David Dhawan
Anil Sharma
Rajiv Rai
Farogue Siddique
David Dhawan
Abbas Mastan
Shashilal Nair
David Dhawan
Subhash Ghai
Yash Chopra
Abbas Mustan
Mehul Kumar
Rajkumar Santoshi
K. Muralimohana Rao
Sooraj Barjatya
Rajiv Rai
Mehul Kumar
David Dhawan
Sameer Malkan

Prodution House
Glamour Films
NA
Shantketan
Trimurti Films
Aftab Pictures
Neha Arts
Venus Movies
Aarishaa International
Chiragdeep International
Mukta Arts Ltd
Yash Raj Films
Eros Labs
NA
NA
NA
Rajshri Productions
Trimurti Films Pvt Ltd.
Mehul Movies Pvt Ltd.
Sapna Arts
United Seven Creation

Genre
Action/Adventure/Drama
Action/Romance
Action
Action/Thriller
Action/Romance
Comedy/Romance/Thriller
Action/Thriller
Action/Crime/Drama
Comedy
Action
Romance/Thriller
Crime/Thriller
Action
Drama
Comedy/Drama/Romance
Action/Thriller
Drama
Action/Comedy

Category
Below Average
Hit
Semi Hit
Above Average
Hit
Hit
Commercial Sucess
Hit
All time blockbuster
SuperHit
SuperHit
Hit
Hit
Hit
Hit
All Time blockbuster
SuperHit
SuperHit
Hit
Semi Hit

d) Sum Total of Parameters Affecting Movie-Watching Decision in Theater
Music
Star Cast
Storyline
Dialogue
Special Effects
Remake
Director

Production House
Advertising & Promotion
Release Date
Item Song
Sequel
(Total: 12 Factors)

e) Online Questionnaire: Audience Preferences Towards Success Parameters
A single online questionnaire was floated to collect audience’s responses about the importance
associated with each of the twelve factors listed above for movies belonging to the following four
genres identified for the project - Action, Thriller, Romance and Drama. A semantic differential
scale (1 to 7) was used to allow the respondents to indicate the importance they associate with
every factor. For example, the instructions in the questionnaire looked like this:
Genre: ROMANCE
What importance do you attribute to the following factors while deciding to watch a Bollywood movie of this
genre in theatre? (Please note the rating convention here: 1 = Least Important and 7 = Most Important)

Scale Employed: A 7-point scale was chosen to ensure increased granularity in the responses since
estimating the importance of parameters was crucial to defining broad factors for different genres of
cinema.
Sampling Plan: The survey was floated across respondents differing on age, gender, marital status
and occupation. While a majority of respondents were students within and outside IIM Bangalore
campus, the researchers managed to cover a significantly diverse pool of people through personal
and professional contacts.
Responses: A total of 152 responses were collected against the questionnaire with a brief
snapshot of the varying demographics of the respondents being as follows:


A detailed snapshot of the questionnaire and the data collected for every genre is provided in the
Appendix for reference.

f) Factor Analysis
The technique of factor analysis was used for reducing the twelve different parameters that moviegoers were known to consider while deciding whether to watch a movie in the theatre or not. Derived
out of secondary exploratory study, these parameters were summarized into a smaller number of
factors specific to each genre. Total variance in the data was considered for carrying out the analysis
using the Principal Component Analysis method since the concern was to find out the minimum
number of factors that account for the maximum variance in the data. The detailed outputs of factor
analysis performed independently for each of the genres are available in the Appendix and
interpretations below are based on these data sets only.
i.

Action: Comparing the variables in the correlation matrix, correlations between the variables
selected for factor analysis is significant for most of the pairs. Some pairs like [Music, Release
Date] have low correlations (logical and seen very well in practice when we watch Hindi movies
as well), however it cannot be concluded that all variables have low correlations from among the
set. Since not all variables have a low degree of correlation among themsleves, the researchers
concluded that factor analysis can be further proceeded with. This is further confirmed by the
Bartlett's Test of Sphericity result from the analysis which is significant at the given degrees of
freedom level and has a large value of the test statistic, hence the null hypothesis “Ho: The
variables are uncorrelated in the population” gets rejected. At 0.787, The KMO measure of
sampling adequacy is significantly high as well (greater than 0.5) and hence, factor analysis is
appropriate. Communality values for all the variables were significantly high and hence, every
variable shared variance with the rest. The eigenalues of three variables were greater than 1 and
two others have values tending to 1. Hence, these two variables could end up as single strong
variables accounting for significant variance in the data. This however, needed to be verified
using the percentage of variance explained criterion. Considering the Extraction Sums of Squared
Loadings and then the Rotation Sums of Squared Loadings in the Total Variance Explained table,
it can be seen that taking 5 factors into consideration accounted for more than 60% of the


variance in the data. Hence, post-rotation using the varimax procedure, a total of 5 components
were extracted for this genre. The Component Score Covariance Matrix post-rotation confirmed
that the 5 factors extracted were loaded on significantly different set of variables and no overlap
was observed. Correlation between the factors and the extracted components as mentioned in
the Rotated Component Matrix table were used to interpret the factors and name them
appropriately. The named list of 5 factors for the action genre are mentioned in the factor
analysis summary section below.
ii.

Drama: For obtaining better reduction of factors, the researchers removed a variable out of the
twelve originally put into the survey based on the low importance average score (less than 2)
given to it on the scale of 1 to 7 by the respondents. Comparing the remaining 11 variables in the
correlation matrix, correlations between the variables selected for factor analysis is significant for
most of the pairs. Some pairs like [Item Song, Storyline] have low correlations (as seen in
practice by the abrupt switch to item songs in Hindi movies of current times bearing no relation
or necessity to the storyline whatsoever in most cases), however it cannot be concluded that all
variables have low correlations from among the set. Since not all variables have a low degree of
correlation among themsleves, the researchers concluded that factor analysis can be further
proceeded with. This is further confirmed by the Bartlett's Test of Sphericity result from the
analysis which is significant at the given degrees of freedom level and has a large value of the
test statistic, hence the null hypothesis “H o: The variables are uncorrelated in the population”
gets rejected. At 0.782, The KMO measure of sampling adequacy is significantly high as well
(greater than 0.5) and hence, factor analysis is appropriate. Communality values for all the
variables were significantly high and hence, every variable shared variance with the rest. The
eigenalues of three variables were greater than 1 and another one was tending to 0.8 closer to 1.
Hence, this variable (Dialogue) could end up as a single strong variable accounting for significant
variance in the data. This is as per the logical expectations as movies in this genre hold a lot of
significance on the dialogue and script. This however, needed to be verified using the percentage
of variance explained criterion. Considering the Extraction Sums of Squared Loadings and then
the Rotation Sums of Squared Loadings in the Total Variance Explained table, it can be seen that
taking 4 factors into consideration accounted for more than 60% of the variance in the data. The
kink in the scree plot confirmed this observation. Hence, post-rotation using the varimax
procedure, a total of 4 components were extracted for this genre. The Component Score
Covariance Matrix post-rotation confirmed that the 4 factors extracted were loaded on
significantly different set of variables and no overlap was observed. Correlation between the
factors and the extracted components as mentioned in the Rotated Component Matrix table were
used to interpret the factors and name them appropriately. The named list of 4 factors for the
drama genre are mentioned in the factor analysis summary section below.

iii.

Romance: For obtaining better reduction of factors, the researchers removed two variables out
of the twelve originally put into the survey based on the low importance average scores (less
than 2) given to them on the scale of 1 to 7 by the respondents. Comparing the remaining 10
variables in the correlation matrix, correlations between the variables selected for factor analysis
is significant for most of the pairs. Some pairs like [Item Song, Storyline] have low correlations
(as seen in practice by the abrupt switch to item songs in Hindi movies of current times bearing
no relation or necessity to the storyline whatsoever in most cases), however it cannot be
concluded that all variables have low correlations from among the set. Since not all variables
have a low degree of correlation among themsleves, the researchers concluded that factor
analysis can be further proceeded with. This is further confirmed by the Bartlett's Test of
Sphericity result from the analysis which is significant at the given degrees of freedom level and
has a large value of the test statistic, hence the null hypothesis “Ho: The variables are
uncorrelated in the population” gets rejected. At 0.762, The KMO measure of sampling adequacy
is significantly high as well (greater than 0.5) and hence, factor analysis is appropriate.
Communality values for all the variables were significantly high and hence, every variable shared


variance with the rest. The eigenalues of three variables were greater than 1 and another one
was tending to 0.9 closer to 1. Hence, this variable (Dialogue) could end up as a single strong
variable accounting for significant variance in the data. This is as per the logical expectations as
movies in this genre hold a lot of significance on the dialogues as an expression of romance. This
however, needed to be verified using the percentage of variance explained criterion. Considering
the Extraction Sums of Squared Loadings and then the Rotation Sums of Squared Loadings in the
Total Variance Explained table, it can be seen that taking 5 factors into consideration accounted
for clsoe to 78.5% of the variance in the data. The flattening of the scree plot confirmed this
observation. Hence, post-rotation using the varimax procedure, a total of 5 components were
extracted for this genre. The Component Score Covariance Matrix post-rotation confirmed that
the 5 factors extracted were loaded on significantly different set of variables and no overlap was
observed. Correlation between the factors and the extracted components as mentioned in the
Rotated Component Matrix table were used to interpret the factors and name them appropriately.
The named list of 5 factors for the romance genre are mentioned in the factor analysis summary
section below.
iv.

Thriller: For obtaining better reduction of factors, the researchers removed one variable out of
the twelve originally put into the survey based on the low importance average score (less than 2)
given to it on the scale of 1 to 7 by the respondents. Comparing the remaining 11 variables in the
correlation matrix, correlations between the variables selected for factor analysis is significant for
most of the pairs. Some pairs like [Music, Dialogue] have low correlations (as seen in practice
with lots of Hindi movies in this genre very well), however it cannot be concluded that all
variables have low correlations from among the set. Since not all variables have a low degree of
correlation among themsleves, the researchers concluded that factor analysis can be further
proceeded with. This is further confirmed by the Bartlett's Test of Sphericity result from the
analysis which is significant at the given degrees of freedom level and has a large value of the
test statistic, hence the null hypothesis “H o: The variables are uncorrelated in the population”
gets rejected. At 0.749, The KMO measure of sampling adequacy is significantly high as well
(greater than 0.5) and hence, factor analysis is appropriate. Communality values for all the
variables were significantly high and hence, every variable shared variance with the rest. The
eigenalues of four variables were greater than 1 and another one was tending to 0.8 closer to 1.
Hence, this variable (Special Effects) could end up as a single strong variable accounting for
significant variance in the data. This is as per expectations as movies in this genre use a lot of
special graphics and effects as means to create the thrill and horror factor. This however, needed
to be verified using the percentage of variance explained criterion. Considering the Extraction
Sums of Squared Loadings and then the Rotation Sums of Squared Loadings in the Total Variance
Explained table, it can be seen that taking 5 factors into consideration accounted for clsoe to
72.4% of the variance in the data. The flattening of the scree plot confirmed this observation.
Hence, post-rotation using the varimax procedure, a total of 5 components were extracted for
this genre. The Component Score Covariance Matrix post-rotation confirmed that the 5 factors
extracted were loaded on significantly different set of variables and no overlap was observed.
Correlation between the factors and the extracted components as mentioned in the Rotated
Component Matrix table were used to interpret the factors and name them appropriately. The
named list of 5 factors for the thriller genre are mentioned in the factor analysis summary section
below.

Summary of Factor Analysis Results
The following factors were obtained for different genres. Henceforth, these will be referred to as the
“decision factors” for movies in the particular genre.


Hypotheses Formation
The following hypotheses were formed once the factor analysis outputs were obtained. The
hypotheses formed were to be tested in the next phase of the research.


Genre: Action








H1:
H2:
H3:
H4:
H5:

Release Date of a movie influences total box office sales
Quality of content influences total box office sales
Pre-release buzz created by a movie influences total box office sales
Power of director & production banner influence total box office sales
The greater the star power, the higher total box office sales

Genre: Thriller








Promotion Strategy influences total box office sales
Remake/Sequel of a successful movie influences total box office sales
Star power & quality of dialogues influence total box office sales
Quality of music & screenplay influences total box office sales

Genre: Romance








H1:
H2:
H3:
H4:
H5:

H1:
H2:
H3:
H4:
H5:

Pre-release buzz influences total box office sales
Presence of star and fit with script influence total box office sales
Presence of special effects influences total box office sales
Quality of music influences total box office sales

Genre: Drama





H1:
H2:
H3:
H4:

Pre-release buzz influences total box office sales
Star power & quality of content influences total box office sales
Quality of music influences total box office sales


4. Conclusive Research
The primary aim of the researchers in this phase of the project was to devise a plan to test the set of
hypotheses derived from the elaborate exploratory research carried out. With the aid of analysis tools
such as the Multiple Discriminant Analysis, the researchers aimed to achieve the intended deliverables of
the project - genre-wise statistical models which were capable of predicting the success category
(blockbuster, superhit/hit or flop) for any new Bollywood movie by processing collected audience ratings
for the movie on the genre’s correspondingly identified “decision factors”.
For the purpose of the research, it was essential for the researchers to find out how Hindi movies
belonging to different genres get classified into one of the three buckets i.e. blockbusters, superhits/hits
or flops based on the audience’s perception about the movie. A clear objective and understanding of the
process was critical to proceed further.
The exploratory research phase identified the factors (the same that were named as “success factors” by
the researchers) on which movie-goers based their decision to watch a production in theater or not. An
affirmative decision taken by the audience would translate into revenues for the movie in question and
will decide which one out of the three buckets, it will land into. The researchers found it prudent to base
their predictive model by training it on the data available for existing set of Hindi movies from recent
times (post-2000 period). The idea was to seek ratings from movie-goers for a diverse set of movies
belonging to each of the three buckets (blockbusters, superhits/hits or flops) and spread across genres.
The data so obtained would then be used to prepare a classifying method to best segregate movies into
one of the three buckets. The same model (specific to a particular genre) can then be extended for
classifying new movies as well to fulfill the objective of the research project.

a) Online Questionnaire: Collecting Audience Ratings for Representative Set of Movies
The researchers carried out another round of primary data collection via two online
questionnaires (each one containing movies for two different genres) to collect the audience’s
ratings for a diverse set of recent Hindi movies on each of the identified set of “success factors” for
the respective genre. A sample screenshot of the online survey used for the above activity is provided
in the Appendix.
Methodology: A set number of movies (usually 13, 14 or 15) were selected (usually 4 or 5 movies
belonging to each of the buckets i.e. blockbuster, superhit/hit or flop) to be placed into the online
quesntionnaire for that genre. The movies were selected subjectively based on the criteria of
commanding a high recall in the minds of the respondents and appropriateness in terms of
representing one of the three buckets for the corresponding genre. These movies acted as a
representative set for their corresponding genres while seeking rating inputs from the respondents.
Scale Employed: A 5-point scale labelled as (Very Bas – Bad – Neutral – Good- Very Good) was
employed to seek the respondent’s attitude against every movie on each of the identified success
factor. The 5-point scale though compromised on granularity on the part of the responses obtained
(a 7-point scale would have enabled the respondent greater discretion while marking their
responses), it was adopted keeping in mind the sufficiently large number of input points the
respondednt was required to fill in while taking the questionnaire. Cognitive capabilities and
motivation put in by the respondents was kept in mind while restricting the scale to only 5 items,
basically to ensure that the respondents don’t feel overwhelmed and leave the questions unanswered
in between while taking the survey.
Sampling Plan: The demographic targeted was similar to the one for the survey conducted during
the exploratory research phase.


Responses: A total number of 52 responses were obtained against each of the surveys. The
responses when properly put into tabular form for subsequent multi-variate analysis provided the
researchers with 600+ data records of audience’s attitude ratings for 15 movies belonging to one
single genre which was sufficient to carry out the required quantitative analysis. It is to be noted that
similar 600+ records of data was available for each of the four movie genres.

b) Discriminant Analysis
The data obtained from the online questionnaire could be visualised in the following form:
Independent variables i.e. the x-variables (ratings obtained on each of the success factors identified
for the genre). For the purpose of Discriminant Analysis, the dependent variable (y-variable) is also
usually collected as part of the survey from the respondents and then used to classify the records
into different groups by the researchers to carry out the analysis. In the present use case, the
dependent variable (y-variable) was already known to the researchers since the success categories of
already released Bollywood movies placed in the questionnaires were well-known from the exhaustive
data collection on movies right from 1980s onwards carried out at part of the exploratory research.
Hence, each of movies, which were the individual test units in this case, and therefore all the various
corresponding attitude responses (approximately 52, but may vary slightly due to cleaning up of
incomplete or biased/unserious entries) for that movie were paired with the dependent variable for
that movie, i.e. group number 1, 2 or 3 based on the following uniform convention:
Return of Investment Status

Bucket Classification

If the movie was a Blockbuster

Dependent Variable (Group No.) = 1

If the movie was a Flop


If the movie was a Superhit/Hit


The activity was performed independently for all four genres with their corresponding representative
set of movies to understand the relative importance of each of the success factors identified during
the exploratory research phase in segregating blockbusters, superhits/hits and flops with enough
confidence. As mentioned before, the use of this technique enabled the researchers to assign group
membership (blockbusters, superhits/hits or flops) from among the set of movies in every genre
using the corresponding factor ratings (which are the multiple predictor variables or x-variables in the
discriminant analysis method). Four different linear discriminant equations were obtained in the
following form: Di  a  b1 X 1  b2 X 2    bp X p .
A genre-wise interpretation of the discriminant analysis carried out is outlined below. The detailed
outputs of discriminant analysis performed independently for each of the genres are available in the
Appendix and interpretations below are based on these data sets only.

i.

Action: In this genre, the number of 5 movies belonging to the blockbuster category were
placed in the questionnaire and 250 responses were obtained, 4 superhit/hit movies were placed
in the questionnaire and 200 responses were obtained and lastly, 4 flop movies were placed in
the survey while 200 responses were obtained for them. From the group standard deviation
statistics, it was inferred that 'Music & Screenplay', 'Promotion & Publicity' & 'Star cast & Quality
of Dialogues’ played major roles in differentiating a block buster movie from the rest of the films.
‘Promotion & Publicity’ and ‘Music & Screenplay’ differentiated the most among the three buckets
of movies for this genre. The values of correlation in the 'Pooled Within-Groups Matrices' depicted
the presence of correlation between the following factors: a) 'Music & Screen Play' & 'Direction
& Production Banner'; b) ‘Music & Screen Play' & ‘Remake & Sequel Effect’ and c) ‘Music &


Screen Play' & ‘Promotion & Publicity'. Since the value was not highly significant, the multi-col
linearity factor was not considered high but caution was needed. Based on the Canonical
Discriminant Functions output, it could be inferred that the three groups translate into two major
functions. The eigenvalue for function 1 was high, also function 1 alone accounted for 95.3 % of
the total explained variance, thus it was more significant relative to the function 2. The value of
Wilk's Lambda was 0.459 for the combined function, which corresponds to a chi-square value of
502.173 with 10 degrees of freedom
– enough to achieve significance
beyond the 0.05 level. From the
Standardized Canonical Discriminant
Function Coefficients table, all five
factors seemed to be associated
primarily with function 1. But the
insights from the Structure Matrix
differed. This may be due to the
presence of slight multi-collinearity
in the predictor variables as
discussed above. In the absence of
any unambiguous measure of the
relative importance of the predictors
in discriminating between the
groups, the researchers decided to
rely on the value of the factor
coefficients to judge the contributed
importance. Hence, ‘Promotion &
Publicity’, ‘Remake & Sequel Effects’
and ‘Music & Screen Play’ were concluded to be associated with function 2 and ‘Star Cast &
Quality of Dialogues’ with function 1. Coefficients from both functions for ‘Director & Production
Banner’ variables were comparatively very low and hence, were not considered important in
segregating movies in this genre. This was inline with the general perception of action movies
where the screen play, star power and promotion/publicity shaped the audience’s perception
about the movie rather than production houses or directors. The scatter plot portrayed the
following relationship between the three groups and the functions:
Group

Movie Category

Related Function

1

Blockbuster

Highest value for function 1 & function 2 .Proves that blockbuster
movie has a high quotient of all the factors.

2

Flop

Lowest value of both the functions, thus explaining the poor
performance.

Superhit/Hit

Moderate to high value on the functions. The centroid of this
category is plotted between the blockbuster and flop movies
projecting that sufficient factor quotients were not to aid the
movies to perform at blockbuster level.

3

Hit Ratio: The classification results conveyed that 77.6% of blockbusters, 77% of flops and a
relatively low 52.5% of superhits/hits were classified correctly via this model. A point of parity
and point of difference analysis for this genre was also conducted and listed in the Appendix after
the discriminant analysis output.


ii.

Drama: In this genre, the number of 5 movies belonging to the blockbuster category were
statistics, it was inferred that ‘Star Power & Content Quality’ played a major role in differentiating
a block buster movie from the rest of the films. ‘Director & Production Banner’ and ‘Quality of
Music’ also differentiated the most among the three buckets of movies for this genre. The values
of correlation in the 'Pooled Within-Groups Matrices' depicted the presence of correlation
between the following factors: a) 'Pre-Release Buzz' & 'Direction & Production Banner' and b)
'Pre-Release Buzz' & 'Star Power & Content Quality'. Since the value was not highly significant,
the multi-collinearity factor was not considered high but caution was needed to be observed.
Based on the Canonical Discriminant Functions output, it could be inferred that the three groups
translate into two major functions. The eigenvalue for function 1 was high, also function 1 alone
accounted for 88.3 % of the total explained variance, thus it was more significant relative to the
function 2. The value of Wilk's Lambda was 0.463 for the combined function, which corresponds
to a chi-square value of 525.172 with 8 degrees of freedom – significant beyond the 0.05 level.
From the Standardized Canonical
Discriminant Function Coefficients
table, all four factors seemed to be
associated primarily with function 1.
But the insights from the Structure
Matrix differed. This may be due to
the presence of slight multicollinearity in the predictor variables
as discussed above. In the absence
of any unambiguous measure of the
relative importance of the predictors
in discriminating between the
groups, the researchers decided to
rely on the value of the factor
coefficients to judge the contributed
importance. Hence, ‘Pre-Release
Buzz’ and ‘Direction & Production
Banner’ were concluded to be
associated
with
function
2
(considering absolute value ignoring
the sign of the coefficients) while ‘Quality of Music’ and ‘Star Power & Content Quality’ with
function 1. This was inline with the general perception where all factors for a drama movie
played a role in decision-making by audiences. The scatter plot portrayed the following
relationship between the three groups and the functions:
Group

Movie Category

Related Function

1

Blockbuster


2

Flop

3

Superhit/Hit

performance.


Hit Ratio: The classification results conveyed that 78.8% of blockbusters, 83.2% of flops and
58% of superhits/hits were classified correctly via this model. A point of parity and point of
difference analysis for this genre was also conducted and listed in the Appendix after the
discriminant analysis output.

iii.

Romance: In this genre, the number of 6 movies belonging to the blockbuster category were
statistics, it was inferred that the factors ‘Time of Release’ and ‘Star Cast’ played a major role in
differentiating a block buster movie from the rest of the films. ‘Quality of Movie Content’ also
differentiated the most among
the three buckets of movies for
this genre. The values of
correlation in the 'Pooled WithinGroups Matrices' depicted the
presence of correlation between
the following factors: a) 'PreRelease Buzz' & 'Direction &
Production Banner' and b) 'PreRelease Buzz' & 'Star Cast’. Since
the value was not highly
significant, the multi-collinearity
factor was not considered high
but caution was required to be
observed.
Based
on
the
Canonical Discriminant Functions
output, it could be inferred that
the three groups translate into
two
major
functions.
The
eigenvalue for function 1 was
high, also function 1 alone accounted for 99.7% of the total explained variance, thus it was more
significant relative to the function 2. The value of Wilk's Lambda was 0.343 for the combined
function, which corresponds to a chi-square value of 727.963 with 10 degrees of freedom –
significant beyond the 0.05 level. From the Standardized Canonical Discriminant Function
Coefficients table, all five factors seemed to be associated primarily with function 1. But the
insights from the Structure Matrix differed. This may be due to the presence of slight multicollinearity in the predictor variables as discussed above. In the absence of any unambiguous
measure of the relative importance of the predictors in discriminating between the groups, the
researchers decided to rely on the value of the factor coefficients to judge the contributed
importance. Hence, ‘Time of Release’, ‘Quality of Movie Content’ and ‘Pre-Release Marketing
Buzz’ were concluded to be associated with function 2 (considering absolute value ignoring the
sign of the coefficients) while ‘Star Cast’ and ‘Direction & Production Banner’ with function 1. This
was inline with the general perception where all these factors for a romance movie played a role
in decision-making by audiences. The ‘Pre-Release Marketing Buzz’ played a reduced role in
comparison to the other factors. The scatter plot portrayed the following relationship between
the three groups and the functions:
Group

Movie Category

Related Function



1

Blockbuster

2

Flop

performance.

Superhit/Hit


3

Hit Ratio: The classification results conveyed that 71.4% of blockbusters, 95.5% of flops and
42.2% of superhits/hits were classified correctly via this model. A point of parity and point of

iv.

Thriller: In this genre, the number of 4 movies belonging to the blockbuster category were
the survey while 250 responses were obtained for them. From the group mean value statistics, it
was inferred that the factors ‘Star Cast Fit with Script’ and ‘Direction & Prodcution Banner’ and
‘Quality of Music’ values were closer to each other. From the values of standard deviation, it
could be concluded that 'Star Cast Fit with Script’ & ‘Quality of Music’ played a major role in
differentiating a block buster movie from the rest of the films. ‘Pre-Release Buzz' was the next
major factor that played a significant
role in categorizing movies, followed
by ‘Direction & Production Banner'
and ‘Special Effects’. The values of
correlation in the 'Pooled WithinGroups
Matrices'
depicted
the
presence of correlation between the
following
factors:
a)
‘Direction
Production Banner’ & ‘Star Cast Fit
with Script’ and b) ‘Direction
Production Banner’ & ‘Quality of
Music’. Since the value was not highly
significant,
the
multi-collinearity
factor was not considered high but
caution was required to be observed.
Based on the Canonical Discriminant
Functions output, it could be inferred
that the three groups translate into
two major functions. The eigenvalue
for function 1 was high, also function
1 alone accounted for 99.7% of the
total explained variance, thus it was
more significant relative to the function 2. The value of Wilk's Lambda was 0.397 for the
combined function, which corresponds to a chi-square value of 596.384 with 10 degrees of
freedom – significant beyond the 0.05 level. From the Standardized Canonical Discriminant
Function Coefficients table, all five factors seemed to be associated primarily with function 1. But
the insights from the Structure Matrix differed. This may be due to the presence of slight multicollinearity in the predictor variables as discussed above. In the absence of any unambiguous


measure of the relative importance of the predictors in discriminating between the groups, the
researchers decided to rely on the value of the factor coefficients to judge the contributed
importance. Hence, ‘Pre-Release Buzz’, ‘Quality of Music’, ‘Star cast fit with Script’ and ‘Direction
& Production Banner’ were concluded to be associated with function 2 (considering absolute
value ignoring the sign of the coefficients) while ‘Special Effects’ with function 1. The scatter plot
portrayed the following relationship between the three groups and the functions:
Group

Movie Category

Related Function

1

Blockbuster

Highest value for function 1 & function 2. Proves that blockbuster

2

Flop

performance.

Superhit/Hit

projecting that sufficient factor quotient were not to aid the

3

Hit Ratio: The classification results conveyed that 76.5% of blockbusters, 72% of flops and
68.5% of superhits/hits were classified correctly via this model. A point of parity and point of

5. Conclusion
a) Conclusive Remarks
The following four statistical models (one for each movie genre) were obtained out of the research
process which can be used to classify new movies in one of the three success categories:

DAction =
(0.538* Promotion & Publicity) – (0.674 * Remake & Sequel Effects) + (0.476 * StarCast &
Quality of Dialogues) + (1.021 * Music & Screen Play)

DThriller =
(0.562 * Pre-Release Buzz) + (0.652 * Quality of Music) + (0.718 * Star cast fit with Script) –
(1.031 * Direction & Production Banner) + (0.382 * Special Effects)

DRomance =
- (0.918 * Time of Release) + (0.4 * Quality of Movie Content) + (0.112 * Pre-Release Marketing
Buzz) + (0.148 * Direction & Production Banner) + (0.613 * Star Cast)

DDrama =
(1.214 * Pre-Release Buzz) – (0.558 * Direction & Production Banner) + (0.371 * Quality of
Music) + (0.659 * Star Power & Content Quality)


b) Scope for Further Research
In order to have an exhaustive model we can extend this model to the following:
i.

Different kind of earning models depending of media: The current research was focused
on Bollywood movies which will be displayed on big screens, this research can be extended to
Satellite TV as producers are searching for alternative source of revenue earning for them and
Satellite TV display is a potential medium because of its reach and penetration in Indian homes.

ii.

Regional movies: As regional movies are not much researched hence we can extend this model
to analyze the success of different regional movies like Bhojpuri, Tamil ,Telegu, kannad etc and
can make it suited for different genre in regional movie segment which can be of greater help for
the regional producers.

c) Limitations of the Research Carried Out
i.

Low rate of classification for super hit movies: In the survey analysis we found that the
subjects were not able to differentiate much between super hit and Blockbuster movies out that
there was lesser difference in people perception of these two categories.

ii.

Sample set: Since movies are watched by billions over people of different demography with
different buying behavior, the sample set might not be sufficient as it majorly covers: urban
population; mostly youth respondents; unmarried people (as they have different movie watching
preferences) and private company employees.

iii.

Movie set while sampling: The movie set shown to the subject for analyzing and asking them
to rate on the identified factors might not be sufficient since the data is collected on the basis of
limited number of movies. Hence more exhaustive movie list would bring more granularities to
the model.


6. Appendix
Exhibit 1: Exhaustive Calssification of Hindi Films since 1980s (Attached alongwith Report)
Exhibit 2: Survey Data for Exploratory Phase - Factor Analysis (Attached alongwith Report)
Exhibit 3: Survey Data for Conclusive Phase - Discriminant Analysis (Attached with Report)
Exhibit 4: Survey form for Exploratory Research (Success Factors of Bollywood Movies)


(Survey hosted at the following URL: https://qtrial.qualtrics.com/SE/?SID=SV_bJYjLphOuK6CdF3)

Exhibit 5: Factor Analysis Report for Different Genres of Movies
Action:
FACTOR
/VARIABLES Music Starcast StoryLine Dialogue SpecialEffects Remake Director
ProductionHouse AdvPromotion ReleaseDate ItemSong Sequel
/MISSING LISTWISE
/ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Remake Director
/PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE
/PLOT EIGEN
/CRITERIA FACTORS(5) ITERATE(25)
/EXTRACTION PC
/CRITERIA ITERATE(25)
/ROTATION VARIMAX
/METHOD=CORRELATION.


Factor Analysis
Notes
Output Created

27-Feb-2013 23:21:29

Comments
Input

Active Dataset

DataSet1

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data

153

File
Missing Value Handling

Definition of Missing

MISSING=EXCLUDE: User-defined
missing values are treated as missing.

Cases Used

LISTWISE: Statistics are based on
cases with no missing values for any
variable used.

Syntax

FACTOR
/VARIABLES Music Starcast
StoryLine Dialogue SpecialEffects
Remake Director ProductionHouse
AdvPromotion ReleaseDate ItemSong
Sequel
/MISSING LISTWISE
/ANALYSIS Music Starcast StoryLine
Dialogue SpecialEffects Remake
Director ProductionHouse
Sequel
/PRINT INITIAL CORRELATION
KMO REPR EXTRACTION
ROTATION FSCORE
/PLOT EIGEN
/CRITERIA FACTORS(5)
ITERATE(25)
/EXTRACTION PC
/ROTATION VARIMAX

Resources

Processor Time

00 00:00:00.281


Elapsed Time

00 00:00:00.246

Maximum Memory Required

18744 (18.305K) bytes

Correlation Matrix
Music
Correlation

Music

Starcast

StoryLine

Dialogue

Special Effects

1.000

.068

.285

.222

.165

Starcast

.068

1.000

.124

.196

.256

StoryLine

.285

.124

1.000

.305

.131

Dialogue

.222

.196

.305

1.000

.361

Special Effects

.165

.256

.131

.361

1.000

Remake

.029

.189

-.041

.050

.311

Director

-.038

.218

.150

.128

.213

Production House

.068

.272

.171

.218

.349

Adv & Promotion

.179

.276

.181

.242

.345

-.006

.196

.098

.150

.226

Item Song

.079

.160

-.150

.130

.214

Sequel

.070

.225

.089

.024

.309

Release Date

Correlation Matrix
Production
Remake
Correlation

Adv &

House

Promotion

Director

Music

.029

-.038

.068

.179

Starcast

.189

.218

.272

.276

StoryLine

-.041

.150

.171

.181

Dialogue

.050

.128

.218

.242

Special Effects

.311

.213

.349

.345

Remake

1.000

.174

.368

.326

Director

.174

1.000

.487

.145

Production House

.368

.487

1.000

.398

Adv & Promotion

.326

.145

.398

1.000

Release Date

.338

.200

.433

.510

Item Song

.329

.050

.265

.505

Sequel

.499

.151

.305

.397

Correlation Matrix
Release Date
Correlation

Music

-.006

Item Song
.079

Sequel
.070


Starcast

.196

.160

.225

StoryLine

.098

-.150

.089

Dialogue

.150

.130

.024

Special Effects

.226

.214

.309

Remake

.338

.329

.499

Director

.200

.050

.151

Production House

.433

.265

.305

Adv & Promotion

.510

.505

.397

1.000

.468

.420

Item Song

.468

1.000

.350

Sequel

.420

.350

1.000

Release Date

KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity

Approx. Chi-Square
df

.787
423.874
66

Sig.

.000

Communalities
Initial

Extraction

Music

1.000

.664

Starcast

1.000

.434

StoryLine

1.000

.779

Dialogue

1.000

.717

Special Effects

1.000

.661

Remake

1.000

.698

Director

1.000

.741

Production House

1.000

.672

Adv & Promotion

1.000

.683

Release Date

1.000

.720

Item Song

1.000

.749

Sequel

1.000

.713

Extraction Method: Principal Component
Analysis.


Total Variance Explained
Extraction
Sums of
Squared
Initial Eigenvalues
Component

Total

% of Variance

Loadings

Cumulative %

Total

1

3.651

30.424

30.424

3.651

2

1.602

13.350

43.774

1.602

3

1.211

10.088

53.862

1.211

4

.889

7.411

61.274

.889

5

.880

7.330

68.604

.880

6

.797

6.638

75.242

7

.717

5.971

81.213

8

.546

4.549

85.763

9

.489

4.071

89.834

10

.454

3.785

93.619

11

.390

3.250

96.869

12

.376

3.131

100.000

Extraction Sums of Squared
Loadings
Component

% of Variance

Rotation Sums of Squared Loadings

Cumulative %

Total

% of Variance

Cumulative %

1

30.424

30.424

2.099

17.492

17.492

2

13.350

43.774

1.591

13.261

30.753

3

10.088

53.862

1.578

13.152

43.905

4

7.411

61.274

1.539

12.822

56.726

5

7.330

68.604

1.425

11.878

68.604

6
7
8
9
10
11
12


Extraction Method: Principal Component Analysis.

Component Matrix

a

Component
1

2

3

4

5

Music

.209

.511

.510

.311

.057

Starcast

.471

.155

-.182

-.201

.339

StoryLine

.243

.701

.007

.356

-.319

Dialogue

.392

.579

.141

-.450

.076

Special Effects

.598

.221

.014

-.127

.488

Remake

.607

-.339

-.029

.327

.327

Director

.425

.157

-.721

.023

-.125


Production House

.699

.062

-.394

.011

-.155

Adv & Promotion

.737

-.040

.276

-.103

-.227

Release Date

.688

-.246

.052

-.071

-.422

Item Song

.588

-.397

.365

-.303

-.143

Sequel

.643

-.282

.092

.439

.139

Starcast

StoryLine

a. 5 components extracted.

Reproduced Correlations
Music
Reproduced Correlation

Music
Starcast

.664

a

.042

Dialogue

.042

.505

.314

a

.042

.365

a

.318

.434

StoryLine

.505

.042

Dialogue

.314

.365

.318

Special Effects

.234

.504

.099

.459

Remake

.060

.284

-.078

-.084

Director

-.199

.309

.256

.136

Production House

-.029

.356

.263

.238

Adv & Promotion

.230

.234

.189

.334

-.001

.148

.105

.135

Item Song

.005

.162

-.195

.178

Sequel

.182

.202

.071

-.085

.026

-.219

-.092

.082

-.168

Release Date

Residual

b

Music

.779

.717

a

Starcast

.026

StoryLine

-.219

.082

Dialogue

-.092

-.168

-.013

Special Effects

-.069

-.248

.032

-.098

Remake

-.031

-.095

.037

.135

Director

.161

-.090

-.105

-.008

Production House

.097

-.084

-.093

-.020

Adv & Promotion

-.051

.042

-.008

-.092

Release Date

-.005

.048

-.007

.015

.074

-.002

.045

-.048

-.112

.023

.018

.109

Item Song
Sequel

-.013


Special Effects

Remake

Director

Music

.234

.060

-.199

Starcast

.504

.284

.309

StoryLine

.099

-.078

.256

Dialogue

.459

-.084

.136

a

.406

.215

Remake

.406

a

.192

Director

.215

.192

Production House

.349

.368

.611

Adv & Promotion

.338

.345

.134

Release Date

.161

.338

.267

Item Song

.238

.336

-.064

Sequel

.336

.672

.156

Music

-.069

-.031

.161

Starcast

-.248

-.095

-.090

StoryLine

.032

.037

-.105

Dialogue

-.098

.135

-.008

-.095

-.002

Special Effects

Residual

b

.661

Special Effects

.698

.741

a

Remake

-.095

-.018

Director

-.002

-.018

Production House

-.001

.001

-.123

Adv & Promotion

.006

-.019

.011

Release Date

.065

-3.670E-5

-.067

Item Song

-.024

-.007

.115

Sequel

-.027

-.174

-.005

Production
House

Music

Adv &
Promotion

Release Date

-.029

.230

-.001

Starcast

.356

.234

.148

StoryLine

.263

.189

.105

Dialogue

.238

.334

.135

Special Effects

.349

.338

.161


Remake

.368

.345

.338

Director

.611

.134

.267

a

.438

.509

a

.635

Production House

.672

Adv & Promotion
Release Date

.635

.262

.614

.603

Sequel

.379

.434

.427

Music

.097

-.051

-.005

Starcast

-.084

.042

.048

StoryLine

-.093

-.008

-.007

Dialogue

-.020

-.092

.015

Special Effects

-.001

.006

.065

Remake

.001

-.019

-3.670E-5

Director

b

.509

Item Song

Residual

.438

.683

-.123

.011

-.067

-.040

-.076

Production House

.720

a

Adv & Promotion

-.040

Release Date

-.076

-.125

.004

-.109

-.136

-.074

-.037

-.006

Item Song
Sequel

-.125

Item Song

Sequel

Music

.005

.182

Starcast

.162

.202

StoryLine

-.195

.071

Dialogue

.178

-.085

Special Effects

.238

.336

Remake

.336

.672

Director

-.064

.156

Production House

.262

.379

Adv & Promotion

.614

.434

Release Date

.603

.427

a

.371

Item Song

.749

a

Sequel
Residual

b

.371

.713

Music

.074

-.112

-.002

.023

Starcast


StoryLine

.045

.018

Dialogue

-.048

.109

Special Effects

-.024

-.027

Remake

-.007

-.174

Director

.115

-.005

Production House

.004

-.074

Adv & Promotion

-.109

-.037

Release Date

-.136

-.006

Item Song

-.021

Sequel

-.021

a. Reproduced communalities
b. Residuals are computed between observed and reproduced correlations. There are 31 (46.0%)
nonredundant residuals with absolute values greater than 0.05.
Rotated Component Matrix

a

Component
1

2

3

4

5

Music

.045

.130

.187

-.276

.731

Starcast

.071

.160

.588

.238

-.036

StoryLine

-.011

-.078

.017

.315

.820

Dialogue

.229

-.319

.675

.060

.323

Special Effects

.108

.309

.732

.087

.104

Remake

.206

.773

.193

.125

-.071

Director

-.004

.071

.165

.842

-.009

Production House

.344

.225

.241

.661

.088

Adv & Promotion

.731

.198

.228

.103

.214

Release Date

.766

.191

-.015

.308

.038

Item Song

.803

.183

.168

-.123

-.165

Sequel

.317

.763

.060

.111

.120

Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.

Component Transformation Matrix


Component

1

2

3

4

5

1

.630

.459

.458

.380

.192

2

-.307

-.412

.398

.149

.746

3

.387

.029

.003

-.854

.345

4

-.318

.660

-.481

.069

.476

5

-.507

.428

.633

-.313

-.247


Component Score Coefficient Matrix
Component
1

2

3

4

5

Music

-.042

.166

.027

-.287

.544

Starcast

-.130

.031

.449

.056

-.158

StoryLine

-.033

-.040

-.218

.227

.622

Dialogue

.119

-.393

.491

-.067

.068

Special Effects

-.170

.162

.549

-.111

-.067

Remake

-.145

.564

.050

-.039

-.051

Director

-.124

-.048

-.011

.614

-.063

Production House

.068

-.005

-.015

.413

.002

Adv & Promotion

.391

-.077

-.025

-.048

.108

Release Date

.452

-.107

-.240

.157

.017

Item Song

.485

-.109

.037

-.206

-.171

-.043

.549

-.126

-.039

.125

Sequel


Component Score Covariance Matrix
Component

1

2

3

4

5

1

1.000

.000

.000

.000

.000

2

.000

1.000

.000

.000

.000

3

.000

.000

1.000

.000

.000

4

.000

.000

.000

1.000

.000

5

.000

.000

.000

.000

1.000


Component

1

2

3

4

5

1

1.000

.000

.000

.000

.000

2

.000

1.000

.000

.000

.000

3

.000

.000

1.000

.000

.000

4

.000

.000

.000

1.000

.000

5

.000

.000

.000

.000

1.000


Drama:
FACTOR
/VARIABLES Music Starcast StoryLine Dialogue SpecialEffects Director
/MISSING LISTWISE
/ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Director
/PLOT EIGEN
/EXTRACTION PC
/ROTATION VARIMAX

Factor Analysis
Notes
Output Created

27-Feb-2013 23:38:18

Comments
Input

Data

E:IIMBTerm 3RMDProjectFinal
DataDrama Data File.sav

Active Dataset

DataSet2

Filter

<none>

Weight

<none>

Split File

<none>


153

File



Cases Used

variable used.


Syntax

FACTOR
Sequel
/MISSING LISTWISE
Dialogue SpecialEffects Director
ProductionHouse AdvPromotion
ReleaseDate ItemSong Sequel
/PRINT INITIAL CORRELATION KMO
REPR EXTRACTION ROTATION
FSCORE
/PLOT EIGEN
ITERATE(25)
/EXTRACTION PC
/ROTATION VARIMAX

Resources

Processor Time

00 00:00:00.265

Elapsed Time

00 00:00:00.240


16004 (15.629K) bytes

[DataSet2] E:IIMBTerm 3RMDProjectFinal DataDrama Data File.sav

Correlation Matrix
Music
Correlation

Music

Starcast

StoryLine

Dialogue

Special Effects

1.000

.345

.402

.216

.265

Starcast

.345

1.000

.410

.439

.138

StoryLine

.402

.410

1.000

.512

.092

Dialogue

.216

.439

.512

1.000

.170

Special Effects

.265

.138

.092

.170

1.000


Director

.166

.196

.142

.273

.108

Production House

.161

.183

-.029

.132

.277

Adv & Promotion

.269

.267

.257

.239

.248

Release Date

.220

.274

.064

.260

.364

Item Song

.172

.133

-.003

.072

.454

Sequel

.216

.167

.034

.133

.382

Correlation Matrix
Production
Director
Correlation

House

Adv & Promotion

Release Date

Music

.166

.161

.269

.220

Starcast

.196

.183

.267

.274

StoryLine

.142

-.029

.257

.064

Dialogue

.273

.132

.239

.260

Special Effects

.108

.277

.248

.364

1.000

.460

.327

.197

Production House

.460

1.000

.463

.502

Adv & Promotion

.327

.463

1.000

.464

Release Date

.197

.502

.464

1.000

Item Song

.159

.377

.423

.503

Sequel

.232

.310

.341

.542

Director

Correlation Matrix
Item Song
Correlation

Sequel

Music

.172

.216

Starcast

.133

.167

StoryLine

-.003

.034

Dialogue

.072

.133

Special Effects

.454

.382

Director

.159

.232

Production House

.377

.310

Adv & Promotion

.423

.341

Release Date

.503

.542

1.000

.460

.460

1.000

Item Song
Sequel



.782

Approx. Chi-Square

473.679

df

55

Sig.

.000

Communalities
Initial

Extraction

Music

1.000

.865

Starcast

1.000

.565

StoryLine

1.000

.725

Dialogue

1.000

.771

Special Effects

1.000

.570

Director

1.000

.747

Production House

1.000

.725

Adv & Promotion

1.000

.536

Release Date

1.000

.703

Item Song

1.000

.643

Sequel

1.000

.567

Analysis.

Extraction Sums
of Squared
Initial Eigenvalues
Component

Total

% of Variance

Loadings

Cumulative %

Total

1

3.724

33.853

33.853

3.724

2

1.806

16.423

50.276

1.806

3

1.103

10.023

60.299

1.103

4

.786

7.142

67.441

.786

5

.708

6.437

73.878

6

.649

5.902

79.780

7

.612

5.560

85.340


8

.510

4.637

89.977

9

.432

3.923

93.900

10

.364

3.308

97.208

11

.307

2.792

100.000

Loadings
Component

% of Variance


Cumulative %

Total

% of Variance

Cumulative %

1

33.853

33.853

2.592

23.560

23.560

2

16.423

50.276

1.964

17.854

41.413

3

10.023

60.299

1.735

15.772

57.186

4

7.142

67.441

1.128

10.255

67.441

5
6
7
8
9
10
11



Component Matrix

a

Component
1

2

3

4

Music

.502

.357

.292

.633

Starcast

.520

.510

.029

-.184

StoryLine

.373

.753

.129

.045

Dialogue

.490

.579

-.075

-.436

Special Effects

.561

-.220

.447

.085

Director

.496

.056

-.677

.201

Production House

.641

-.311

-.452

.112

Adv & Promotion

.703

-.044

-.179

.090

Release Date

.741

-.274

.070

-.272

Item Song

.634

-.429

.235

-.041


Sequel

.637

-.327

.200

-.125

a. 4 components extracted.

Music

Music

.865

Starcast

Starcast
a

StoryLine

Dialogue

.335

.335

.522

.155

a

.573

.628

a

.590

.565

StoryLine

.155

.628

.590

Special Effects

.388

.177

.105

.077

Director

.198

.230

.149

.239

Production House

.150

.141

-.048

.119

Adv & Promotion

.342

.321

.210

.293

Release Date

.122

.297

.067

.317

Item Song

.208

.125

-.058

.062

Sequel
b

.573

Dialogue

Residual

.522

.182

.193

.012

.162

.010

-.120

.060

-.164

-.189

Music

.725

.771

a

Starcast

.010

StoryLine

-.120

-.164

Dialogue

.060

-.189

-.078

Special Effects

-.122

-.039

-.013

.093

Director

-.033

-.034

-.007

.034

Production House

.011

.042

.020

.013

Adv & Promotion

-.072

-.054

.047

-.054

.098

-.023

-.003

-.058

-.036

.008

.055

.010

.033

-.026

.022

-.029

Release Date
Item Song
Sequel

-.078

Production
Special Effects

Director

House

Music

.388

.198

.150

Starcast

.177

.230

.141


StoryLine

.105

.149

-.048

Dialogue

.077

.239

.119

a

-.019

.235

a

.629

Special Effects

.570

Director

-.019

.747

.629

.332

.485

.555

Release Date

.484

.251

.498

Item Song

.552

.124

.429

Sequel

.508

.137

.405

Music

-.122

-.033

.011

Starcast

-.039

-.034

.042

StoryLine

-.013

-.007

.020

Dialogue

b

.235

Adv & Promotion

Residual

.093

.034

.013

.127

.042

Special Effects

.725

a

Production House

Director

.127

-.169

Production House

.042

-.169

Adv & Promotion

-.084

-.158

-.093

Release Date

-.120

-.054

.003

Item Song

-.098

.035

-.052

Sequel

-.126

.094

-.095

Adv & Promotion

Release Date

Music

.342

.122

Starcast

.321

.297

StoryLine

.210

.067

Dialogue

.293

.317

Special Effects

.332

.484

Director

.485

.251

Production House

.555

.498

a

.496

Adv & Promotion

.536

.419

.615

Sequel
Residual

.496

Item Song

b

.703

a

Release Date

.415

.609

Music

-.072

.098

Starcast

-.054

-.023


StoryLine

.047

-.003

Dialogue

-.054

-.058

Special Effects

-.084

-.120

Director

-.158

-.054

Production House

-.093

.003

Adv & Promotion

-.032

Release Date

-.032

Item Song

.004
-.074

Sequel

-.112
-.067

Item Song

Sequel

Music

.208

.182

Starcast

.125

.193

StoryLine

-.058

.012

Dialogue

.062

.162

Special Effects

.552

.508

Director

.124

.137

Production House

.429

.405

Adv & Promotion

.419

.415

Release Date

.615

.609

a

.596

Item Song

.643

Residual

b

.596

Music

-.036

.033

Starcast

.008

-.026

StoryLine

.055

.022

Dialogue

.010

-.029

-.098

-.126

.035

.094

Production House

-.052

-.095

Adv & Promotion

.004

-.074

-.112

-.067

Special Effects
Director

Release Date
Item Song
Sequel

.567

a

Sequel

-.136
-.136


a. Reproduced communalities
b. Residuals are computed between observed and reproduced correlations. There are 29 (52.0%) nonredundant
residuals with absolute values greater than 0.05.

Rotated Component Matrix

a

Component
1

2

3

4

Music

.172

.219

.124

.879

Starcast

.155

.707

.128

.157

StoryLine

-.077

.733

.001

.427

Dialogue

.103

.860

.127

-.076

Special Effects

.689

.040

-.043

.302

-.014

.160

.847

.069

Production House

.394

-.021

.755

-.008

Adv & Promotion

.424

.228

.523

.175

Release Date

.752

.230

.274

-.104

Item Song

.782

-.039

.161

.071

Sequel

.732

.079

.157

.022

Director

a. Rotation converged in 5 iterations.

Component Transformation Matrix
Component

1

2

3

4

1

.708

.434

.495

.255

2

-.516

.782

-.118

.329

3

.438

.014

-.824

.360

4

-.201

-.447

.250

.835

Component Score Coefficient Matrix
Component
1
Music

2

3

4

-.053

-.143

.027

.867

Starcast

.012

.386

-.044

-.058

StoryLine

-.104

.346

-.082

.252

Dialogue

.010

.555

-.055

-.349


Special Effects

.325

-.072

-.218

.234

-.242

-.040

.632

.036

Production House

.002

-.129

.479

-.041

Adv & Promotion

.052

.010

.259

.077

Release Date

.317

.123

-.022

-.266

Item Song

.347

-.086

-.076

-.002

Sequel

.326

.006

-.083

-.083

Director

Component

1

2

3

4

1

1.000

.000

.000

.000

2

.000

1.000

.000

.000

3

.000

.000

1.000

.000

4

.000

.000

.000

1.000


Romance:
FACTOR
/VARIABLES Music Starcast StoryLine Dialogue SpecialEffects Director
ProductionHouse AdvPromotion ReleaseDate ItemSong
/MISSING LISTWISE
/ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Director
ProductionHouse AdvPromotion ReleaseDate ItemSong
/PLOT EIGEN
/EXTRACTION PC
/ROTATION VARIMAX

Factor Analysis
Notes
Output Created

27-Feb-2013 23:43:58

Comments
Input

Data

E:IIMBTerm 3RMDProjectFinal
DataRomance Data File.sav

Active Dataset

DataSet3

Filter

<none>

Weight

<none>


Split File

<none>


153

File



Cases Used

variable used.

Syntax

FACTOR
/MISSING LISTWISE
Dialogue SpecialEffects Director
ProductionHouse AdvPromotion
ReleaseDate ItemSong
/PRINT INITIAL CORRELATION
KMO REPR EXTRACTION
ROTATION FSCORE
/PLOT EIGEN
ITERATE(25)
/EXTRACTION PC
/ROTATION VARIMAX

Resources

Processor Time

00 00:00:00.218

Elapsed Time

00 00:00:00.225


13480 (13.164K) bytes

Correlation Matrix
Music
Correlation

Music

Starcast

StoryLine

Dialogue

Special Effects

1.000

.483

.493

.415

.168

Starcast

.483

1.000

.374

.385

.141

StoryLine

.493

.374

1.000

.604

.103

Dialogue

.415

.385

.604

1.000

.261


Special Effects

.168

.141

.103

.261

1.000

Director

.280

.236

.146

.219

.287

Production House

.246

.346

.080

.258

.344

Adv & Promotion

.324

.391

.182

.191

.241

Release Date

.235

.283

.068

.249

.399

Item Song

.100

.218

-.025

.115

.452

Correlation Matrix
Production

Adv &

House

Promotion

Director
Correlation

Music

.280

.246

.324

Starcast

.236

.346

.391

StoryLine

.146

.080

.182

Dialogue

.219

.258

.191

Special Effects

.287

.344

.241

1.000

.619

.275

Production House

.619

1.000

.377

Adv & Promotion

.275

.377

1.000

Release Date

.286

.480

.400

Item Song

.075

.267

.389

Director

Correlation Matrix
Release Date
Correlation

Item Song

Music

.235

.100

Starcast

.283

.218

StoryLine

.068

-.025

Dialogue

.249

.115

Special Effects

.399

.452

Director

.286

.075

Production House

.480

.267

Adv & Promotion

.400

.389

1.000

.323

.323

1.000

Release Date
Item Song


Approx. Chi-Square

.762
458.262


df

45

Sig.

.000

Communalities
Initial

Extraction

Music

1.000

.638

Starcast

1.000

.669

StoryLine

1.000

.789

Dialogue

1.000

.779

Special Effects

1.000

.818

Director

1.000

.889

Production House

1.000

.786

Adv & Promotion

1.000

.706

Release Date

1.000

.940

Item Song

1.000

.841

Analysis.

Extraction
Sums of
Squared
Initial Eigenvalues
Component

Total

% of Variance

Loadings

Cumulative %

Total

1

3.597

35.968

35.968

3.597

2

1.665

16.651

52.619

1.665

3

1.074

10.738

63.357

1.074

4

.894

8.935

72.292

.894

5

.625

6.252

78.544

.625

6

.553

5.527

84.071

7

.543

5.427

89.499

8

.414

4.139

93.637

9

.332

3.318

96.956

10

.304

3.044

100.000

Component

Loadings



% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

35.968

35.968

2.050

20.497

20.497

2

16.651

52.619

1.646

16.459

36.956

3

10.738

63.357

1.598

15.980

52.936

4

8.935

72.292

1.479

14.789

67.725

5

6.252

78.544

1.082

10.820

78.544

6
7
8
9
10


Component Matrix

a

Component
1

2

3

4

5


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