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Recipe for Hindi Cinema Blockbuster: Research for Marketing Decisions

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Term Paper as part of the Research for Marketing Decisions course at IIM Bangalore.

Term Paper as part of the Research for Marketing Decisions course at IIM Bangalore.

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  • 1. 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
  • 2. 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”. Project Report: Recipe for Hindi Cinema Blockbuster | 2
  • 3. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 3
  • 4. 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). Project Report: Recipe for Hindi Cinema Blockbuster | 4
  • 5. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 5
  • 6. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 6
  • 7. 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. Project Report: Recipe for Hindi Cinema Blockbuster | 7
  • 8. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 8
  • 9. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 9
  • 10. 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. Project Report: Recipe for Hindi Cinema Blockbuster | 10
  • 11. 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 Comedy/Drama/Romance Action/Thriller Drama Comedy/Drama/Romance 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: Project Report: Recipe for Hindi Cinema Blockbuster | 11
  • 12. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 12
  • 13. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 13
  • 14. 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. Project Report: Recipe for Hindi Cinema Blockbuster | 14
  • 15. 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 Power of director & production banner 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 Power of director & production banner influence 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 Power of director & production banner influence total box office sales Quality of music influences total box office sales Project Report: Recipe for Hindi Cinema Blockbuster | 15
  • 16. 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. Project Report: Recipe for Hindi Cinema Blockbuster | 16
  • 17. 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 Dependent Variable (Group No.) = 2 If the movie was a Superhit/Hit Dependent Variable (Group No.) = 3 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 & Project Report: Recipe for Hindi Cinema Blockbuster | 17
  • 18. 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. Project Report: Recipe for Hindi Cinema Blockbuster | 18
  • 19. ii. Drama: In this genre, the number of 5 movies belonging to the blockbuster category were placed in the questionnaire and 245 responses were obtained, 5 superhit/hit movies were placed in the questionnaire and 245 responses were obtained and lastly, 4 flop movies were placed in the survey while 196 responses were obtained for them. From the group standard deviation 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 Highest value for function 1 & function 2 .Proves that blockbuster movie has a high quotient of all the factors. 2 Flop 3 Superhit/Hit Lowest value of both the functions, thus explaining the poor performance. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 19
  • 20. movies to perform at blockbuster level. 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 placed in the questionnaire and 284 responses were obtained, 4 superhit/hit movies were placed in the questionnaire and 196 responses were obtained and lastly, 4 flop movies were placed in the survey while 196 responses were obtained for them. From the group standard deviation 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 Project Report: Recipe for Hindi Cinema Blockbuster | 20
  • 21. Highest value for function 1 & function 2 .Proves that blockbuster movie has a high quotient of all the factors. 1 Blockbuster 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 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 difference analysis for this genre was also conducted and listed in the Appendix after the discriminant analysis output. iv. Thriller: In this genre, the number of 4 movies belonging to the blockbuster category were placed in the questionnaire and 200 responses were obtained, 4 superhit/hit movies were placed in the questionnaire and 200 responses were obtained and lastly, 5 flop movies were placed in 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 Project Report: Recipe for Hindi Cinema Blockbuster | 21
  • 22. 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 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 quotient were not to aid the movies to perform at blockbuster level. 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 difference analysis for this genre was also conducted and listed in the Appendix after the discriminant analysis output. 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) Project Report: Recipe for Hindi Cinema Blockbuster | 22
  • 23. 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. Project Report: Recipe for Hindi Cinema Blockbuster | 23
  • 24. 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) Project Report: Recipe for Hindi Cinema Blockbuster | 24
  • 25. (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 ProductionHouse AdvPromotion ReleaseDate ItemSong Sequel /PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE /PLOT EIGEN /CRITERIA FACTORS(5) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. Project Report: Recipe for Hindi Cinema Blockbuster | 25
  • 26. 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 AdvPromotion ReleaseDate ItemSong Sequel /PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE /PLOT EIGEN /CRITERIA FACTORS(5) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. Resources Processor Time 00 00:00:00.281 Project Report: Recipe for Hindi Cinema Blockbuster | 26
  • 27. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 27
  • 28. 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. Project Report: Recipe for Hindi Cinema Blockbuster | 28
  • 29. 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 Total Variance Explained 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 Project Report: Recipe for Hindi Cinema Blockbuster | 29
  • 30. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 30
  • 31. 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 Extraction Method: Principal Component Analysis. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 31
  • 32. Reproduced Correlations Special Effects Reproduced Correlation 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 Reproduced Correlations Production House Reproduced Correlation 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 Project Report: Recipe for Hindi Cinema Blockbuster | 32
  • 33. 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 Reproduced Correlations Item Song Reproduced Correlation 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 Project Report: Recipe for Hindi Cinema Blockbuster | 33
  • 34. 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 Extraction Method: Principal Component Analysis. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. Component Transformation Matrix Project Report: Recipe for Hindi Cinema Blockbuster | 34
  • 35. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 35
  • 36. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Drama: FACTOR /VARIABLES Music Starcast StoryLine Dialogue SpecialEffects Director ProductionHouse AdvPromotion ReleaseDate ItemSong Sequel /MISSING LISTWISE /ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Director ProductionHouse AdvPromotion ReleaseDate ItemSong Sequel /PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE /PLOT EIGEN /CRITERIA FACTORS(4) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. 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> 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. Project Report: Recipe for Hindi Cinema Blockbuster | 36
  • 37. Syntax FACTOR /VARIABLES Music Starcast StoryLine Dialogue SpecialEffects Director ProductionHouse AdvPromotion ReleaseDate ItemSong Sequel /MISSING LISTWISE /ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Director ProductionHouse AdvPromotion ReleaseDate ItemSong Sequel /PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE /PLOT EIGEN /CRITERIA FACTORS(4) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. Resources Processor Time 00 00:00:00.265 Elapsed Time 00 00:00:00.240 Maximum Memory Required 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 Project Report: Recipe for Hindi Cinema Blockbuster | 37
  • 38. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 38
  • 39. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity .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 Extraction Method: Principal Component Analysis. Total Variance Explained 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 Project Report: Recipe for Hindi Cinema Blockbuster | 39
  • 40. 8 .510 4.637 89.977 9 .432 3.923 93.900 10 .364 3.308 97.208 11 .307 2.792 100.000 Total Variance Explained Extraction Sums of Squared Loadings Component % of Variance Rotation Sums of Squared Loadings 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 Extraction Method: Principal Component Analysis. Project Report: Recipe for Hindi Cinema Blockbuster | 40
  • 41. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 41
  • 42. Sequel .637 -.327 .200 -.125 Extraction Method: Principal Component Analysis. a. 4 components extracted. Reproduced Correlations Music Reproduced Correlation 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 Reproduced Correlations Production Special Effects Reproduced Correlation Director House Music .388 .198 .150 Starcast .177 .230 .141 Project Report: Recipe for Hindi Cinema Blockbuster | 42
  • 43. 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 Reproduced Correlations Adv & Promotion Reproduced Correlation 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 Project Report: Recipe for Hindi Cinema Blockbuster | 43
  • 44. 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 Reproduced Correlations Item Song Reproduced Correlation 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 Project Report: Recipe for Hindi Cinema Blockbuster | 44
  • 45. Extraction Method: Principal Component Analysis. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 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 Project Report: Recipe for Hindi Cinema Blockbuster | 45
  • 46. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Score Covariance Matrix 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 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 /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 23:43:58 Comments Input Data E:IIMBTerm 3RMDProjectFinal DataRomance Data File.sav Active Dataset DataSet3 Filter <none> Weight <none> Project Report: Recipe for Hindi Cinema Blockbuster | 46
  • 47. 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 Director ProductionHouse AdvPromotion ReleaseDate ItemSong /MISSING LISTWISE /ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Director ProductionHouse AdvPromotion ReleaseDate ItemSong /PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE /PLOT EIGEN /CRITERIA FACTORS(5) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. Resources Processor Time 00 00:00:00.218 Elapsed Time 00 00:00:00.225 Maximum Memory Required 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 Project Report: Recipe for Hindi Cinema Blockbuster | 47
  • 48. 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 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square .762 458.262 Project Report: Recipe for Hindi Cinema Blockbuster | 48
  • 49. 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 Extraction Method: Principal Component Analysis. Total Variance Explained 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 Total Variance Explained Extraction Sums of Squared Component Loadings Rotation Sums of Squared Loadings Project Report: Recipe for Hindi Cinema Blockbuster | 49
  • 50. % 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 Extraction Method: Principal Component Analysis. Component Matrix a Component 1 2 3 4 5 Project Report: Recipe for Hindi Cinema Blockbuster | 50
  • 51. Music .633 .443 .008 -.167 .119 Starcast .659 .268 .082 -.395 .008 StoryLine .491 .708 .109 .184 .041 Dialogue .615 .475 .103 .377 -.151 Special Effects .551 -.381 .236 .539 .152 Director .584 -.161 -.663 .098 .271 Production House .693 -.329 -.444 -.006 -.010 Adv & Promotion .638 -.183 .173 -.483 .056 Release Date .633 -.358 .028 .010 -.641 Item Song .460 -.480 .567 -.012 .276 Starcast StoryLine Extraction Method: Principal Component Analysis. a. 5 components extracted. Reproduced Correlations Music Dialogue .599 .519 .603 a .450 .390 .599 .450 a .712 Dialogue .519 .390 .712 Special Effects .110 .068 .132 .362 Director .309 .251 .130 .211 Production House .289 .334 .057 .223 Adv & Promotion .411 .576 .116 .133 Release Date .165 .314 .036 .323 Item Song b .603 StoryLine Residual a Starcast Reproduced Correlation .118 .228 -.043 .067 -.120 -.106 -.104 -.076 -.005 Music .638 Music .669 .789 .779 a Starcast -.120 StoryLine -.106 -.076 Dialogue -.104 -.005 -.108 .058 .073 -.028 -.101 Director -.029 -.014 .016 .008 Production House -.044 .012 .023 .035 Adv & Promotion -.087 -.185 .066 .058 .070 -.031 .031 -.074 -.018 -.010 .018 .048 Special Effects Release Date Item Song -.108 Reproduced Correlations Project Report: Recipe for Hindi Cinema Blockbuster | 51
  • 52. Production Special Effects Reproduced Correlation Director House Music .110 .309 .289 Starcast .068 .251 .334 StoryLine .132 .130 .057 Dialogue .362 .211 .223 a .321 .398 Director .321 a .749 Production House .398 .749 Adv & Promotion .210 .255 .428 Release Date .399 .236 .550 Item Song .606 .044 .222 Music .058 -.029 -.044 Starcast .073 -.014 .012 StoryLine -.028 .016 .023 Dialogue -.101 .008 .035 -.034 -.054 Special Effects Residual b .818 Special Effects .889 .786 a Director -.034 -.130 Production House -.054 -.130 Adv & Promotion .031 .021 -.051 Release Date -.001 .050 -.071 Item Song -.154 .031 .045 Reproduced Correlations Adv & Promotion Reproduced Correlation Release Date Item Song Music .411 .165 .118 Starcast .576 .314 .228 StoryLine .116 .036 -.043 Dialogue .133 .323 .067 Special Effects .210 .399 .606 Director .255 .236 .044 Production House .428 .550 .222 a .433 .501 Release Date .433 a .302 Item Song .501 Adv & Promotion .706 .940 .302 .841 a Project Report: Recipe for Hindi Cinema Blockbuster | 52
  • 53. Residual b Music -.087 .070 -.018 Starcast -.185 -.031 -.010 StoryLine .066 .031 .018 Dialogue .058 -.074 .048 Special Effects .031 -.001 -.154 Director .021 .050 .031 -.051 -.071 .045 -.034 -.112 Production House Adv & Promotion Release Date -.034 Item Song .021 -.112 .021 Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 20 (44.0%) nonredundant residuals with absolute values greater than 0.05. Rotated Component Matrix a Component 1 2 3 4 5 Music .596 .199 .492 -.011 -.032 Starcast .404 .137 .688 -.006 .116 StoryLine .872 .007 .152 -.022 -.072 Dialogue .834 .100 .021 .148 .224 Special Effects .205 .276 -.105 .804 .208 Director .125 .927 .105 .061 .011 Production House .049 .758 .249 .165 .345 Adv & Promotion .022 .168 .760 .245 .202 Release Date .080 .195 .221 .206 .897 -.089 -.039 .387 .823 .069 Item Song Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 8 iterations. Component Transformation Matrix Component 1 2 3 4 5 1 .508 .476 .509 .369 .347 2 .770 -.245 .034 -.502 -.308 Project Report: Recipe for Hindi Cinema Blockbuster | 53
  • 54. 3 .110 -.791 .216 .562 .012 4 .369 .121 -.822 .413 .059 5 -.042 .270 .135 .355 -.884 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Score Coefficient Matrix Component 1 2 3 4 5 Music .218 .042 .279 -.074 -.200 Starcast .062 -.063 .480 -.149 -.023 StoryLine .481 -.077 -.054 .002 -.128 Dialogue .483 -.078 -.262 .062 .211 Special Effects .138 .094 -.346 .631 -.054 Director -.037 .719 -.086 -.039 -.297 Production House -.101 .462 .005 -.071 .136 Adv & Promotion -.180 -.058 .577 .020 -.014 Release Date -.025 -.159 -.060 -.171 1.034 Item Song -.122 -.169 .239 .640 -.252 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Thriller: FACTOR /VARIABLES Music Starcast StoryLine Dialogue SpecialEffects Remake Director ProductionHouse Controversy AdvPromotion Sequel /MISSING LISTWISE /ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Remake Director ProductionHouse Controversy AdvPromotion Sequel /PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE /PLOT EIGEN /CRITERIA FACTORS(5) ITERATE(25) /EXTRACTION PC Project Report: Recipe for Hindi Cinema Blockbuster | 54
  • 55. /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. Factor Analysis Notes Output Created 27-Feb-2013 23:48:45 Comments Input Data E:IIMBTerm 3RMDProjectFinal DataThriller Data FIle.sav Active Dataset DataSet4 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. Project Report: Recipe for Hindi Cinema Blockbuster | 55
  • 56. Syntax FACTOR /VARIABLES Music Starcast StoryLine Dialogue SpecialEffects Remake Director ProductionHouse Controversy AdvPromotion Sequel /MISSING LISTWISE /ANALYSIS Music Starcast StoryLine Dialogue SpecialEffects Remake Director ProductionHouse Controversy AdvPromotion Sequel /PRINT INITIAL CORRELATION KMO REPR EXTRACTION ROTATION FSCORE /PLOT EIGEN /CRITERIA FACTORS(5) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. Resources Processor Time 00 00:00:00.250 Elapsed Time 00 00:00:00.227 Maximum Memory Required 16004 (15.629K) bytes Correlation Matrix Music Correlation Music Starcast StoryLine Dialogue Special Effects 1.000 .257 .108 .017 .170 Starcast .257 1.000 .244 .237 .143 StoryLine .108 .244 1.000 .284 .122 Dialogue .017 .237 .284 1.000 .278 Special Effects .170 .143 .122 .278 1.000 Remake .193 .241 -.095 .192 .329 Director .079 .198 .110 .168 .116 Production House .096 .259 -.167 .106 .190 Controversy .252 .215 -.045 .092 .284 Adv & Promotion .259 .328 .098 .195 .226 Sequel .196 .269 .020 .050 .202 Project Report: Recipe for Hindi Cinema Blockbuster | 56
  • 57. Correlation Matrix Production Remake Correlation Director House Controversy Music .193 .079 .096 .252 Starcast .241 .198 .259 .215 StoryLine -.095 .110 -.167 -.045 Dialogue .192 .168 .106 .092 Special Effects .329 .116 .190 .284 Remake 1.000 .128 .402 .555 Director .128 1.000 .514 .118 Production House .402 .514 1.000 .380 Controversy .555 .118 .380 1.000 Adv & Promotion .383 .248 .311 .434 Sequel .571 .036 .252 .461 Correlation Matrix Adv & Promotion Correlation Sequel Music .259 .196 Starcast .328 .269 StoryLine .098 .020 Dialogue .195 .050 Special Effects .226 .202 Remake .383 .571 Director .248 .036 Production House .311 .252 Controversy .434 .461 1.000 .431 .431 1.000 Adv & Promotion Sequel KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square df Sig. .749 384.482 55 .000 Communalities Project Report: Recipe for Hindi Cinema Blockbuster | 57
  • 58. Initial Extraction Music 1.000 .893 Starcast 1.000 .617 StoryLine 1.000 .711 Dialogue 1.000 .710 Special Effects 1.000 .813 Remake 1.000 .726 Director 1.000 .811 Production House 1.000 .794 Controversy 1.000 .625 Adv & Promotion 1.000 .537 Sequel 1.000 .726 Extraction Method: Principal Component Analysis. Total Variance Explained Extraction Sums of Squared Initial Eigenvalues Component Total % of Variance Loadings Cumulative % Total 1 3.362 30.562 30.562 3.362 2 1.494 13.579 44.140 1.494 3 1.254 11.399 55.540 1.254 4 1.016 9.233 64.773 1.016 5 .836 7.600 72.373 .836 6 .672 6.109 78.482 7 .615 5.594 84.076 8 .557 5.067 89.143 9 .506 4.604 93.747 10 .351 3.189 96.936 11 .337 3.064 100.000 Total Variance Explained Extraction Sums of Squared Loadings Component % of Variance Rotation Sums of Squared Loadings Cumulative % Total % of Variance Cumulative % 1 30.562 30.562 2.588 23.523 23.523 2 13.579 44.140 1.614 14.669 38.193 3 11.399 55.540 1.496 13.602 51.794 Project Report: Recipe for Hindi Cinema Blockbuster | 58
  • 59. 4 9.233 64.773 1.196 10.876 62.671 5 7.600 72.373 1.067 9.702 72.373 6 7 8 9 10 11 Extraction Method: Principal Component Analysis. Component Matrix a Component 1 2 3 4 5 Music .408 .094 -.308 .545 .570 Starcast .535 .370 -.029 .354 -.260 Project Report: Recipe for Hindi Cinema Blockbuster | 59
  • 60. StoryLine .107 .790 -.225 .098 -.126 Dialogue .344 .592 .044 -.477 -.106 Special Effects .491 .185 -.134 -.500 .520 Remake .748 -.297 -.131 -.232 -.085 Director .405 .227 .750 .154 .093 Production House .615 -.201 .610 .026 .037 Controversy .715 -.295 -.148 -.056 .034 Adv & Promotion .696 .037 -.057 .156 -.154 Sequel .664 -.266 -.333 .021 -.321 Extraction Method: Principal Component Analysis. a. 5 components extracted. Reproduced Correlations Music Reproduced Correlation Music Starcast .893 Starcast a .307 StoryLine Dialogue .307 .168 -.138 a .423 .261 a .461 .617 StoryLine -.138 .261 .461 Special Effects .283 .023 .114 .456 Remake .143 .234 -.137 .196 Director .093 .309 .057 .223 Production House .079 .237 -.233 .103 Controversy .298 .249 -.133 .088 Adv & Promotion .303 .483 .151 .201 Sequel b .423 Dialogue Residual .168 .176 .357 -.022 .081 -.050 -.061 .155 -.179 -.023 Music .711 .710 a Starcast -.050 StoryLine -.061 -.179 Dialogue .155 -.023 -.177 -.113 .120 .008 -.177 Remake .051 .007 .042 -.004 Director -.014 -.111 .053 -.055 .017 .022 .065 .003 Controversy -.047 -.034 .088 .005 Adv & Promotion -.044 -.155 -.053 -.005 .020 -.088 .042 -.031 Special Effects Production House Sequel -.177 Project Report: Recipe for Hindi Cinema Blockbuster | 60
  • 61. Reproduced Correlations Production Special Effects Reproduced Correlation Remake Director House Music .283 .143 .093 .079 Starcast .023 .234 .309 .237 StoryLine .114 -.137 .057 -.233 Dialogue .456 .196 .223 .103 a .402 .112 .189 Remake .402 a .094 .431 Director .112 .094 a .669 Production House .189 .431 .669 Controversy .362 .652 .107 .409 Adv & Promotion .198 .494 .258 .385 Sequel .144 .641 -.068 .248 Music -.113 .051 -.014 .017 Starcast .120 .007 -.111 .022 StoryLine .008 .042 .053 .065 Dialogue -.177 -.004 -.055 .003 -.072 .004 .001 .034 -.029 Special Effects Residual b .813 Special Effects .726 .811 .794 a Remake -.072 Director .004 .034 Production House .001 -.029 -.155 -.078 -.096 .011 -.029 Adv & Promotion .028 -.111 -.010 -.074 Sequel .058 -.070 .103 .004 Controversy -.155 Reproduced Correlations Adv & Controversy Reproduced Correlation Promotion Sequel Music .298 .303 .176 Starcast .249 .483 .357 StoryLine -.133 .151 -.022 Dialogue .088 .201 .081 Special Effects .362 .198 .144 Remake .652 .494 .641 Director .107 .258 -.068 Project Report: Recipe for Hindi Cinema Blockbuster | 61
  • 62. Production House .409 .385 .248 a .481 .590 Adv & Promotion .481 a .524 Sequel .590 .524 Music -.047 -.044 .020 Starcast -.034 -.155 -.088 StoryLine .088 -.053 .042 Dialogue .005 -.005 -.031 Special Effects -.078 .028 .058 Remake -.096 -.111 -.070 Director .011 -.010 .103 -.029 -.074 .004 -.048 -.129 Controversy Residual b .625 Production House .537 Controversy Adv & Promotion -.048 Sequel -.129 .726 a -.093 -.093 Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 27 (49.0%) nonredundant residuals with absolute values greater than 0.05. Rotated Component Matrix a Component 1 2 3 4 5 Music .170 .039 .119 .097 .916 Starcast .361 .254 .603 -.144 .194 StoryLine -.119 -.103 .815 .117 .092 Dialogue .084 .134 .552 .538 -.301 Special Effects .208 .063 .036 .857 .172 Remake .792 .134 -.073 .272 -.017 Director -.035 .882 .163 .059 .043 Production House .369 .794 -.142 .080 -.006 Controversy .724 .144 -.089 .204 .175 Adv & Promotion .603 .254 .283 .020 .169 Sequel .843 -.059 .091 -.043 .031 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Project Report: Recipe for Hindi Cinema Blockbuster | 62
  • 63. Extraction Method: Principal Component Analysis. a. Rotation converged in 9 iterations. Component Transformation Matrix Component 1 2 3 4 5 1 .798 .418 .233 .294 .218 2 -.394 .061 .878 .264 .031 3 -.308 .888 -.165 -.082 -.287 4 -.043 .159 .163 -.722 .652 5 -.333 .087 -.348 .562 .667 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Score Coefficient Matrix Component 1 Music 2 3 4 5 -.103 -.019 -.025 .068 .904 Starcast .125 .090 .424 -.312 .069 StoryLine -.081 -.112 .569 .009 .037 Dialogue -.023 .012 .334 .399 -.366 Special Effects -.085 -.051 -.136 .789 .160 Remake .331 -.057 -.107 .130 -.144 Director -.192 .625 .049 -.020 .033 Production House .033 .508 -.167 -.015 -.058 Controversy .273 -.033 -.128 .083 .065 Adv & Promotion .224 .056 .167 -.143 .037 Sequel .437 -.194 .071 -.198 -.130 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 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 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Project Report: Recipe for Hindi Cinema Blockbuster | 63
  • 64. Exhibit 6: Survey form for Conclusive Research (Genre-Wise Movie Ratings) (Genre-Wise surveys hosted at the following URLs: Romance + Drama: https://qtrial.qualtrics.com/SE/?SID=SV_b4thOuIisqxuFmZ Action + Thriller: https://qtrial.qualtrics.com/SE/?SID=SV_bq45iAzskCvwDKB) Project Report: Recipe for Hindi Cinema Blockbuster | 64
  • 65. Exhibit 7: Discriminant Analysis Outputs for Different Movie Genres Action: GET DATA /TYPE=XLSX /FILE='E:IIMBTerm 3RMDProjectDiscriminant AnalysisAction Data-1.xlsx' /SHEET=name 'Sheet1' /CELLRANGE=full /READNAMES=on /ASSUMEDSTRWIDTH=32767. EXECUTE. DATASET NAME DataSet1 WINDOW=FRONT. DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=PromotionampPublicity RemakeampSequelEffect StarCastampQualityofDialogues DirectionampProductionBanner MusicampScreenPlay /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /CLASSIFY=NONMISSING POOLED. Discriminant Notes Output Created 19-Mar-2013 17:34:00 Comments Input Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data 651 File Missing Value Handling Definition of Missing User-defined missing values are treated as missing in the analysis phase. Cases Used In the analysis phase, cases with no user- or system-missing values for any predictor variable are used. Cases with user-, system-missing, or out-of-range values for the grouping variable are always excluded. Project Report: Recipe for Hindi Cinema Blockbuster | 65
  • 66. Syntax DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=PromotionampPublicity RemakeampSequelEffect StarCastampQualityofDialogues DirectionampProductionBanner MusicampScreenPlay /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /CLASSIFY=NONMISSING POOLED. Resources Processor Time 00 00:00:00.015 Elapsed Time 00 00:00:00.014 Analysis Case Processing Summary Unweighted Cases N Valid Percent 650 0 0 .0 1 .2 1 Missing or out-of-range .0 .2 651 Excluded 99.8 100.0 group codes At least one missing discriminating variable Both missing or out-of-range group codes and at least one missing discriminating variable Total Total Group Statistics Valid N (listwise) Temp 1 Mean Std. Deviation Unweighted Weighted Promotion &amp; Publicity 4.34 .701 250 250.000 Remake&amp; Sequel 4.09 .728 250 250.000 4.24 .712 250 250.000 Effect StarCast &amp; Quality of Dialogues Project Report: Recipe for Hindi Cinema Blockbuster | 66
  • 67. Direction &amp; Production 3.90 .772 250 250.000 Music &amp; Screen Play 3.96 .788 250 250.000 Promotion &amp; Publicity 2.82 1.085 200 200.000 Remake&amp; Sequel 2.49 .908 200 200.000 2.63 .904 200 200.000 2.65 1.001 200 200.000 Music &amp; Screen Play 2.50 1.125 200 200.000 Promotion &amp; Publicity 3.44 .793 200 200.000 Remake&amp; Sequel 3.14 .702 200 200.000 3.53 .896 200 200.000 3.33 .857 200 200.000 Music &amp; Screen Play 3.44 .843 200 200.000 Promotion &amp; Publicity 3.60 1.074 650 650.000 Remake&amp; Sequel 3.30 1.028 650 650.000 3.53 1.066 650 650.000 3.34 1.014 650 650.000 3.35 1.101 650 650.000 Banner 2 Effect StarCast &amp; Quality of Dialogues Direction &amp; Production Banner 3 Effect StarCast &amp; Quality of Dialogues Direction &amp; Production Banner Total Effect StarCast &amp; Quality of Dialogues Direction &amp; Production Banner Music &amp; Screen Play Tests of Equality of Group Means Wilks' Lambda F df1 df2 Sig. Promotion &amp; Publicity .644 179.078 2 647 .000 Remake&amp; Sequel .575 239.316 2 647 .000 .608 208.749 2 647 .000 .740 113.659 2 647 .000 .696 141.515 2 647 .000 Effect StarCast &amp; Quality of Dialogues Direction &amp; Production Banner Music &amp; Screen Play Project Report: Recipe for Hindi Cinema Blockbuster | 67
  • 68. Pooled Within-Groups Matrices StarCast &amp; Promotion Quality of &amp; Publicity Correlation Remake&amp; Sequel Effect Dialogues Promotion &amp; Publicity 1.000 .473 .339 .473 1.000 .407 .339 .407 1.000 .501 .481 .442 .511 .525 .461 Remake&amp; Sequel Effect StarCast &amp; Quality of Dialogues Direction &amp; Production Banner Music &amp; Screen Play Pooled Within-Groups Matrices Direction &amp; Production Banner Correlation Music &amp; Screen Play Promotion &amp; Publicity .501 .511 Remake&amp; Sequel .481 .525 .442 .461 1.000 .587 .587 1.000 Effect StarCast &amp; Quality of Dialogues Direction &amp; Production Banner Music &amp; Screen Play Analysis 1 Summary of Canonical Discriminant Functions Eigenvalues Canonical Function Eigenvalue 1 2 % of Variance Cumulative % Correlation 1.069 a 95.3 95.3 .719 .053 a 4.7 100.0 .224 a. First 2 canonical discriminant functions were used in the analysis. Wilks' Lambda Test of Function(s) 1 through 2 Wilks' Lambda .459 Chi-square 502.173 df Sig. 10 .000 Project Report: Recipe for Hindi Cinema Blockbuster | 68
  • 69. Eigenvalues Canonical Function Eigenvalue % of Variance Cumulative % Correlation 1.069 2 a 95.3 95.3 .719 .053 1 a 4.7 100.0 .224 2 .950 33.079 4 .000 Standardized Canonical Discriminant Function Coefficients Function 1 2 Promotion &amp; Publicity .349 -.538 Remake&amp; Sequel .499 -.674 .476 .329 -.053 .093 -.003 1.021 Effect StarCast &amp; Quality of Dialogues Direction &amp; Production Banner Music &amp; Screen Play Structure Matrix Function 1 Remake&amp; Sequel 2 .830 * -.213 .772 * .385 .718 * -.177 .626 * .599 .571 * .245 Effect StarCast &amp; Quality of Dialogues Promotion &amp; Publicity Music &amp; Screen Play Direction &amp; Production Banner Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function Functions at Group Centroids Project Report: Recipe for Hindi Cinema Blockbuster | 69
  • 70. Function Temp 1 2 1 1.177 -.125 2 -1.304 -.185 3 -.166 .341 Unstandardized canonical discriminant functions evaluated at group means Classification Statistics Classification Processing Summary Processed Excluded 651 Missing or out-of-range 0 group codes At least one missing 1 discriminating variable Used in Output 650 Prior Probabilities for Groups Cases Used in Analysis Temp Prior Unweighted Weighted 1 .333 250 250.000 2 .333 200 200.000 3 .333 200 200.000 1.000 650 650.000 Total Classification Results b,c Predicted Group Membership Temp Original Count 1 2 3 Total 12 154 34 200 37 58 105 200 1 77.6 2.0 20.4 100.0 6.0 77.0 17.0 100.0 18.5 29.0 52.5 100.0 1 191 5 54 250 2 12 150 38 200 3 % 250 3 Count 51 2 a 5 3 Cross-validated 194 2 % 1 40 58 102 200 1 76.4 2.0 21.6 100.0 Project Report: Recipe for Hindi Cinema Blockbuster | 70
  • 71. 2 6.0 75.0 19.0 100.0 3 20.0 29.0 51.0 100.0 a. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. b. 69.7% of original grouped cases correctly classified. c. 68.2% of cross-validated grouped cases correctly classified. Further Analysis of Data by the Researchers: Category Comparison Analysis Group % of Data classified Category Dominant Factors (decreasing order of dominance) Comments Promotion & Publicity (4.34), 1 Blockbuster 77.6 Star cast & Quality Dialogue (4.24) Promotion & Publicity (2.81), 2 Flop 77 Direction & production banner (2.65) Star cast & Quality Dialogue (3.53), 3 Superhit/hit 52.5 Promotion & Publicity (3.44), Music & screen play (3.44) The mean values of all the factors more or less lie in the range of 3.90 to 4.34, which justifies that all the factors are equally established in a blockbuster movie of the action genre. The mean values of all the factors are low leading to the poor box-office play. The range of mean values of all the factors 3.143.53, thus showing an average rating. Note: The above dominant factors play a major role in the classification of a movie in the particular category. Points of Parity: Assumption is that the mean values of the factors of the two category can differ by a maximum of 0.30 to establish a point of parity. Group 1 2 3 1 All None None All None 2 3 All Inference : The factors above form the distinguishing element which categorizes a movie into either of the groups as per the survey data collected. The stress on these factors, according to movie-goers makes Project Report: Recipe for Hindi Cinema Blockbuster | 71
  • 72. a striking difference between the values rated for each category, thus the Action genre movies are well segmented in terms of these factors. Points of Difference: Assumption is that the mean values of the factors of the two categories should differ by a minimum of 0.50 to establish a point of difference. Group 1 2 3 1 None Promotion & Publicity. Promotion & Publicity. Remake & Sequel Effect. Remake & Sequel Effect. Star Cast & Quality of Dialogues. Star Cast & Quality of Dialogues. Direction & Production Banner. Music & Screen Play. 2 None Direction & Production Banner. Promotion & Publicity. Remake & Sequel Effect. Star Cast & Quality of Dialogues. Direction & Production Banner. Music & Screen Play. 3 None Inference: The above factors are the major differentiating features between the categories. The audiences rate the better performing category with high factor values than the low/moderate category of movie. E.g. consider category 1 & 3, in this case, there are 4 differentiating factors. Consider one of the factor, Promotion & Publicity. For a blockbuster movie the mean rating provided by the audience is 4.34 (which is the highest rated factor for blockbuster movie).The same factor when considered in case of a super hit/hit movie, it is rated 3.44 points, which is quite low compared to the former rating. Thus the strength of this factor in a movie contributes to the judgment whether it would perform well at the boxoffice or not. Inference: Consider category 3 - If a movie in action genre concentrates on building the strength of the factors distilled above (listed in the table). It can easily move to a better performing category. Drama: DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=PreReleaseBuzz StarPowerampContentQuality DirectionampProductionBanner QualityofMusic /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /CLASSIFY=NONMISSING POOLED. Project Report: Recipe for Hindi Cinema Blockbuster | 72
  • 73. Discriminant Notes Output Created 19-MAR-2013 17:32:47 Comments F:PGPTERM Data 3RMDProject_ BollywoodDrama_Data_3.s av Active Dataset DataSet4 Filter <none> Weight <none> Split File Input <none> N of Rows in Working Data 686 File User-defined missing values Definition of Missing are treated as missing in the analysis phase. In the analysis phase, cases with no user- or systemmissing values for any Missing Value Handling predictor variable are used. Cases Used Cases with user-, systemmissing, or out-of-range values for the grouping variable are always excluded. Project Report: Recipe for Hindi Cinema Blockbuster | 73
  • 74. DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=PreReleaseBu zz StarPowerampContentQualit y DirectionampProductionBan Syntax ner QualityofMusic /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /CLASSIFY=NONMISSING POOLED. Processor Time 00:00:00.02 Elapsed Time 00:00:00.01 Resources Analysis Case Processing Summary Unweighted Cases N Valid Percent 686 0 Excluded .0 686 discriminating variable .0 0 At least one missing .0 0 group codes .0 0 Missing or out-of-range 100.0 100.0 Both missing or out-of-range group codes and at least one missing discriminating variable Total Total Group Statistics Temp Mean Std. Deviation Valid N (listwise) Unweighted Pre-Release Buzz 1 Star Power &amp; Content Quality Weighted 4.27 .835 245 245.000 4.51 .847 245 245.000 Project Report: Recipe for Hindi Cinema Blockbuster | 74
  • 75. Direction &amp; Production 4.36 .967 245 245.000 Quality of Music 4.06 1.201 245 245.000 Pre-Release Buzz 2.86 .932 196 196.000 2.65 .989 196 196.000 2.83 .910 196 196.000 Quality of Music 3.16 .877 196 196.000 Pre-Release Buzz 4.06 .922 245 245.000 3.59 .948 245 245.000 3.53 .903 245 245.000 Quality of Music 3.47 .977 245 245.000 Pre-Release Buzz 3.79 1.073 686 686.000 3.65 1.187 686 686.000 3.63 1.110 686 686.000 3.59 1.101 686 686.000 Banner Star Power &amp; Content 2 Quality Direction &amp; Production Banner Star Power &amp; Content 3 Quality Direction &amp; Production Banner Star Power &amp; Content Quality Total Direction &amp; Production Banner Quality of Music Tests of Equality of Group Means Wilks' Lambda Pre-Release Buzz F df1 df2 Sig. .693 Banner Quality of Music .000 221.155 2 683 .000 148.622 2 683 .000 .886 Direction &amp; Production 683 .697 Quality 2 .607 Star Power &amp; Content 151.473 43.902 2 683 .000 Pooled Within-Groups Matrices Pre-Release Correlation Quality Direction &amp; Production Banner &amp; Content Production Quality Star Power &amp; Content Direction &amp; Buzz Pre-Release Buzz Star Power Banner 1.000 .480 .509 .480 1.000 .395 .509 .395 1.000 Project Report: Recipe for Hindi Cinema Blockbuster | 75
  • 76. Quality of Music .037 -.051 .073 Pooled Within-Groups Matrices Quality of Music Pre-Release Buzz .037 Star Power &amp; Content Quality -.051 Direction &amp; Production Banner .073 Correlation Quality of Music 1.000 Analysis 1 Summary of Canonical Discriminant Functions Eigenvalues Function Eigenvalue % of Variance Cumulative % Canonical Correlation .926 2 a 88.3 88.3 .693 .122 1 a 11.7 100.0 .330 a. First 2 canonical discriminant functions were used in the analysis. Wilks' Lambda Test of Function(s) Wilks' Lambda Chi-square df Sig. 1 through 2 .463 525.172 8 .000 2 .891 78.617 3 .000 Standardized Canonical Discriminant Function Coefficients Function 1 Pre-Release Buzz 2 .142 .659 Direction &amp; Production Banner Quality of Music -.558 .371 Quality -.374 .326 Star Power &amp; Content 1.214 -.215 Structure Matrix Function 1 Star Power &amp; Content Quality .836 2 * -.001 Project Report: Recipe for Hindi Cinema Blockbuster | 76
  • 77. Direction &amp; Production .685 * -.104 Quality of Music .366 * -.191 Pre-Release Buzz .638 Banner .742 * Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function Functions at Group Centroids Temp Function 1 2 1 1.112 -.236 2 -1.293 -.289 3 -.078 .467 Unstandardized canonical discriminant functions evaluated at group means Classification Statistics Classification Processing Summary Processed 686 Missing or out-of-range Excluded 0 group codes At least one missing 0 discriminating variable Used in Output 686 Prior Probabilities for Groups Temp Prior Cases Used in Analysis Unweighted Weighted 1 .333 245 245.000 2 .333 196 196.000 3 .333 245 245.000 1.000 686 686.000 Total Project Report: Recipe for Hindi Cinema Blockbuster | 77
  • 78. Classification Results Temp a,c Predicted Group Membership 1 2 Total 3 1 11 41 245 2 12 163 21 196 3 47 56 142 245 1 78.8 4.5 16.7 100.0 2 6.1 83.2 10.7 100.0 3 19.2 22.9 58.0 100.0 1 193 11 41 245 2 12 159 25 196 3 47 56 142 245 1 78.8 4.5 16.7 100.0 2 6.1 81.1 12.8 100.0 3 Count 193 19.2 22.9 58.0 100.0 Original % Count Cross-validated b % a. 72.6% of original grouped cases correctly classified. b. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. c. 72.0% of cross-validated grouped cases correctly classified. Further Analysis of Data by the Researchers: Category Comparison Analysis Group Category % of Data classified Dominant Factors (decreasing order of dominance) 1 Blockbuster 78.8 Star Power & Content Quality (4.51), Direction & Production Banner(4.36) 2 Flop 83.2 Quality of Music (3.16) Pre release buzz (2.86) 3 Super hit/hit 58 Pre release buzz (4.06) Star Power & Content Quality (3.59) Comments The mean values of all the factors more or less lie in the range of 4.06 to 4.51, which justifies that all the factors are equally established in a blockbuster movie of the Drama genre. The mean values of all the factors are low leading to the poor box-office play. The range of mean values of all the factors 3.474.06, thus showing an average rating . Project Report: Recipe for Hindi Cinema Blockbuster | 78
  • 79. Note: The above dominant factors play a major role in the classification of a movie in the particular category. Points of Parity: Assumption is that the mean values of the factors of the two category can differ by a maximum of 0.30 to establish a point of parity. Group 1 2 3 1 All None Pre release buzz 2 All Quality of Music 3 All Inference: The factors above do not form the distinguishing element which categorises a movie into either of the groups as per the survey data collected. The stress on these factors, according to Moviegoers do not make a striking difference for the success of a movie or the movement of a movie from the lesser performing category to a better performing category. E.g. Quality of Music is not a differentiating factor between a hit movie and a flop movie. These are rated as more or less the same by the audience in case of both the categories of movies. Points of Difference: Assumption is that the mean values of the factors of the two category should differ by a minimum of 0.40 to establish a point of difference. Group 1 2 3 1 2 None Star Power & Content Quality . Direction & Production Banner. Quality of Music. Pre release buzz. None 3 Star Power & Content Quality . Direction & Production Banner. Quality of Music. Star Power & Content Quality. Direction & Production Banner. Pre release buzz. None Inference: The above factors are the major differentiating features between the categories. The audience rate the better performing category with high factor values than the low/moderate category of movie. E.g. consider category 1 & 3, in this case, there are 3 differentiating factors. Consider one of the factor, Star Power and content quality. For a blockbuster movie the mean rating provided by the audience is 4.51 (which is the highest rated factor for blockbuster movie).The same factor when considered in case of a super hit/hit movie, it is rated 3.59 points, which is quite low compared to the former rating. Thus the strength of this factor in a movie, contributes to the judgement whether it would perform well at the box-office or not. Inference: Consider category 3 - If a movie in drama genre concentrates on building the strength of the factors distilled above (listed in the table). It can easily move to a better performing category, thus capturing the box-office share. Romance: GET DATA /TYPE=XLSX /FILE='E:IIMBTerm 3RMDProjectDiscriminant AnalysisRomance Original Data.xlsx' /SHEET=name 'Sheet1' /CELLRANGE=full Project Report: Recipe for Hindi Cinema Blockbuster | 79
  • 80. /READNAMES=on /ASSUMEDSTRWIDTH=32767. EXECUTE. DATASET NAME DataSet1 WINDOW=FRONT. DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=TimeOfRelease QualityofMovieContent DirectionampProductionBanner PreReleaseMarketingBuzz StarCast /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /CLASSIFY=NONMISSING POOLED. Discriminant Notes Output Created 19-Mar-2013 16:48:57 Comments Input Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data 686 File Missing Value Handling Definition of Missing User-defined missing values are treated as missing in the analysis phase. Cases Used In the analysis phase, cases with no user- or system-missing values for any predictor variable are used. Cases with user-, system-missing, or out-of-range values for the grouping variable are always excluded. Project Report: Recipe for Hindi Cinema Blockbuster | 80
  • 81. Syntax DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=TimeOfRelease QualityofMovieContent DirectionampProductionBanner PreReleaseMarketingBuzz StarCast /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /CLASSIFY=NONMISSING POOLED. Resources Processor Time 00 00:00:00.016 Elapsed Time 00 00:00:00.017 Analysis Case Processing Summary Unweighted Cases N Valid Percent 686 0 0 .0 0 .0 0 Missing or out-of-range .0 .0 686 Excluded 100.0 100.0 group codes At least one missing discriminating variable Both missing or out-of-range group codes and at least one missing discriminating variable Total Total Group Statistics Valid N (listwise) Temp 1 Mean Std. Deviation Unweighted Weighted Time Of Release 4.03 .754 294 294.000 Quality of Movie Content 3.70 .953 294 294.000 Direction &amp; Production 4.00 .788 294 294.000 Pre Release Marketing Buzz 3.96 .772 294 294.000 Star Cast 4.41 .604 294 294.000 Time Of Release 2.40 .802 245 245.000 Banner 2 Project Report: Recipe for Hindi Cinema Blockbuster | 81
  • 82. Quality of Movie Content 2.38 .724 245 245.000 Direction &amp; Production 2.79 .714 245 245.000 Pre Release Marketing Buzz 2.71 .732 245 245.000 Star Cast 2.64 .603 245 245.000 Time Of Release 3.31 .615 147 147.000 Quality of Movie Content 3.22 .700 147 147.000 Direction &amp; Production 3.52 .805 147 147.000 Pre Release Marketing Buzz 3.48 .779 147 147.000 Star Cast 3.78 .999 147 147.000 Time Of Release 3.29 1.034 686 686.000 Quality of Movie Content 3.13 1.009 686 686.000 Direction &amp; Production 3.47 .933 686 686.000 Pre Release Marketing Buzz 3.41 .941 686 686.000 Star Cast 3.64 1.056 686 686.000 Banner 3 Banner Total Banner Tests of Equality of Group Means Wilks' Lambda F df1 df2 Sig. Time Of Release .517 318.636 2 683 .000 Quality of Movie Content .667 170.449 2 683 .000 Direction &amp; Production .672 166.623 2 683 .000 .650 183.635 2 683 .000 .447 423.054 2 683 .000 Banner Pre Release Marketing Buzz Star Cast Pooled Within-Groups Matrices Direction &amp; Time Of Production Release Correlation Quality of Movie Content Banner Time Of Release 1.000 .292 .259 Quality of Movie Content .292 1.000 .297 Direction &amp; Production .259 .297 1.000 Pre Release Marketing Buzz .333 .300 .462 Star Cast .212 .258 .289 Banner Pooled Within-Groups Matrices Project Report: Recipe for Hindi Cinema Blockbuster | 82
  • 83. Pre Release Marketing Buzz Correlation Star Cast Time Of Release .333 .212 Quality of Movie Content .300 .258 Direction &amp; Production .462 .289 1.000 .382 .382 1.000 Banner Pre Release Marketing Buzz Star Cast Analysis 1 Summary of Canonical Discriminant Functions Eigenvalues Canonical Function Eigenvalue % of Variance Cumulative % Correlation 1.897 2 a 99.7 99.7 .809 .005 1 a .3 100.0 .073 a. First 2 canonical discriminant functions were used in the analysis. Wilks' Lambda Test of Function(s) Wilks' Lambda Chi-square df Sig. 1 through 2 .343 727.963 10 .000 2 .995 3.619 4 .460 Standardized Canonical Discriminant Function Coefficients Function 1 2 Time Of Release .477 -.918 Quality of Movie Content .165 .400 Direction &amp; Production .148 -.128 .021 .112 .613 .549 Banner Pre Release Marketing Buzz Star Cast Structure Matrix Function 1 Star Cast Time Of Release 2 .808 * .463 .700 * -.681 Project Report: Recipe for Hindi Cinema Blockbuster | 83
  • 84. .532 * .077 .513 * .269 .507 Pre Release Marketing * -.037 Buzz Quality of Movie Content Direction &amp; Production Banner Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function Functions at Group Centroids Function Temp 1 2 1 1.370 -.042 2 -1.738 -.033 3 .158 .139 Unstandardized canonical discriminant functions evaluated at group means Classification Statistics Classification Processing Summary Processed Excluded 686 Missing or out-of-range 0 group codes At least one missing 0 discriminating variable Used in Output 686 Prior Probabilities for Groups Cases Used in Analysis Temp Prior Unweighted Weighted 1 .333 294 294.000 2 .333 245 245.000 3 .333 147 147.000 1.000 686 686.000 Total Project Report: Recipe for Hindi Cinema Blockbuster | 84
  • 85. Classification Results b,c Predicted Group Membership Temp Original Count 1 2 3 Total 0 234 11 245 46 39 62 147 1 71.4 3.1 25.5 100.0 .0 95.5 4.5 100.0 31.3 26.5 42.2 100.0 1 210 9 75 294 2 0 233 12 245 3 46 39 62 147 1 71.4 3.1 25.5 100.0 2 .0 95.1 4.9 100.0 3 % 294 3 Count 75 2 Cross-validated 9 3 a 210 2 % 1 31.3 26.5 42.2 100.0 a. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. b. 73.8% of original grouped cases correctly classified. c. 73.6% of cross-validated grouped cases correctly classified. Further Analysis of Data by the Researchers: Category Comparison Analysis Group 1 2 Movie Category Blockbuster Flop %ge of Data classified correctly Dominant Factor Comments 71.4 StarCast (4.41), TimeOfRelease (4.03), Direction& ProductionBanner (4.00) Star Cast plays an important role for the success of a romantic movie with good direction and production banner. The mean values of all these factors are above 4. which justifies that all the factors are equally established in a blockbuster movie of the Romance genre. 95.5 DirectionampProductionBann er (2.79). The average ranges from 2.38 to 2.79. The mean values of all the factors are low leading to the poor box-office play. Project Report: Recipe for Hindi Cinema Blockbuster | 85
  • 86. 3 Super hit/hit StarCast (3.78), Directionamp ProductionBanner(3.52), PreReleaseMarketingBuzz (3.48) 42.2 The range of mean values of all the factors 3.22-3.78, thus showing an average rating . Note: The above dominant factors play a major role in the classification of a movie in the particular category. Points of Parity: Assumption is that the mean values of the factors of the two category can differ by a maximum of 0.30 to establish a point of parity. Group 1 2 3 1 All None None All None 2 3 All Inference: The factors above do not form the distinguishing element which categorises a movie into either of the groups as per the survey data collected Points of Difference: Assumption is that the mean values of the factors of the two categories should differ by a minimum of 0.40 to establish a point of difference. Group 2 2 3 All Start cast, Time of release, PreRelease Marketing Buzz, QualityofMovieContent, Start cast, Time of release, PreRelease Marketing Buzz, QualityofMovieContent, DirectionampProductionBanner 1 1 DirectionampProductionBanner All Start cast, Time of release, PreRelease Marketing Buzz, QualityofMovieContent, DirectionampProductionBanner 3 All Inference: The above factors are the major differentiating features between the categories. The audience rates the better performing category with high factor values than the low/moderate category of movie. Eg: If we consider category 1 & 3 , all the 5 factors are differentiating factors. If we consider one factor, ‘ Starcast ’ . For a Block buster movie the mean rating provided by the audience is 4.41.The same factor when considered in case of a super hit/hit movie, it is rated 3.78, which is quite low compared to Project Report: Recipe for Hindi Cinema Blockbuster | 86
  • 87. the former rating. Thus the strength of this factor in a movie, contributes to the judgment whether it would perform well at the box-office or not. If a movie in romance genre concentrates on building the strength of the factors distilled above (listed in the table). It can easily move to a better performing category, thus capturing the box-office share. Thriller: GET DATA /TYPE=XLSX /FILE='C:UsersReechalDesktopRMD analysisDiscriminant Analysis DocThriller Data Final.xlsx' /SHEET=name 'Sheet1' /CELLRANGE=full /READNAMES=on /ASSUMEDSTRWIDTH=32767. EXECUTE. DATASET NAME DataSet1 WINDOW=FRONT. DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=PreReleaseBuzz SpecialEffects StarCastFitwithScript DirectionProductionBanner QualityofMusic /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /PLOT=COMBINED /CLASSIFY=NONMISSING POOLED. Discriminant Notes Output Created 20-MAR-2013 01:32:52 Comments Active Dataset Filter Input DataSet1 <none> Weight <none> Split File <none> N of Rows in Working Data 650 File User-defined missing values Missing Value Handling Definition of Missing are treated as missing in the analysis phase. Project Report: Recipe for Hindi Cinema Blockbuster | 87
  • 88. In the analysis phase, cases with no user- or systemmissing values for any predictor variable are used. Cases Used Cases with user-, systemmissing, or out-of-range values for the grouping variable are always excluded. DISCRIMINANT /GROUPS=Temp(1 3) /VARIABLES=PreReleaseBu zz SpecialEffects StarCastFitwithScript DirectionProductionBanner QualityofMusic Syntax /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF CORR TABLE CROSSVALID /PLOT=COMBINED /CLASSIFY=NONMISSING POOLED. Processor Time 00:00:00.39 Elapsed Time 00:00:00.84 Resources Analysis Case Processing Summary Unweighted Cases Valid Percent 650 Missing or out-of-range group codes At least one missing discriminating variable Excluded N 100.0 0 .0 0 .0 0 .0 0 .0 Both missing or out-of-range group codes and at least one missing discriminating variable Total Project Report: Recipe for Hindi Cinema Blockbuster | 88
  • 89. Total 650 100.0 Group Statistics Temp Mean Std. Deviation Valid N (listwise) Unweighted Weighted PreReleaseBuzz 3.89 .971 200 200.000 StarCastFitwithScript 4.09 .931 200 200.000 4.10 .902 200 200.000 4.11 .899 200 200.000 PreReleaseBuzz 2.02 .781 250 250.000 SpecialEffects 2.02 .744 250 250.000 StarCastFitwithScript 2.31 .854 250 250.000 DirectionProductionBanner 2.33 .780 250 250.000 QualityofMusic 2.33 .804 250 250.000 PreReleaseBuzz 2.93 .780 200 200.000 SpecialEffects 2.89 .703 200 200.000 StarCastFitwithScript 3.15 .912 200 200.000 DirectionProductionBanner 3.08 .870 200 200.000 QualityofMusic 3.17 .967 200 200.000 PreReleaseBuzz 2.95 1.191 650 650.000 SpecialEffects 2.86 1.118 650 650.000 StarCastFitwithScript 3.12 1.159 650 650.000 DirectionProductionBanner 3.11 1.118 650 650.000 QualityofMusic Total 200.000 QualityofMusic 3 200 DirectionProductionBanner 2 .891 SpecialEffects 1 4.13 3.13 1.151 650 650.000 Tests of Equality of Group Means Wilks' Lambda F df1 df2 Sig. PreReleaseBuzz .468 368.428 2 647 .000 SpecialEffects .523 295.433 2 647 .000 StarCastFitwithScript .596 218.909 2 647 .000 DirectionProductionBanner .572 242.199 2 647 .000 QualityofMusic .591 223.619 2 647 .000 Pooled Within-Groups Matrices PreReleaseBuz SpecialEffects z PreReleaseBuzz Correlation StarCastFitwith Script 1.000 .468 .501 SpecialEffects .468 1.000 .449 StarCastFitwithScript .501 .449 1.000 Project Report: Recipe for Hindi Cinema Blockbuster | 89
  • 90. DirectionProductionBanner .491 .444 .639 QualityofMusic .453 .391 .581 Pooled Within-Groups Matrices DirectionProductionBa QualityofMusic nner PreReleaseBuzz .453 SpecialEffects .444 .391 StarCastFitwithScript Correlation .491 .639 .581 1.000 .583 .583 1.000 DirectionProductionBanner QualityofMusic Analysis 1 Summary of Canonical Discriminant Functions Eigenvalues Function Eigenvalue % of Variance Cumulative % Canonical Correlation 1.508 2 99.7 99.7 .775 .005 1 a a .3 100.0 .072 a. First 2 canonical discriminant functions were used in the analysis. Wilks' Lambda Test of Function(s) Wilks' Lambda Chi-square df Sig. 1 through 2 .397 596.384 10 .000 2 .995 3.375 4 .497 Standardized Canonical Discriminant Function Coefficients Function 1 2 PreReleaseBuzz .514 -.562 SpecialEffects .382 .376 StarCastFitwithScript .037 .718 DirectionProductionBanner .151 -1.031 QualityofMusic .186 .652 Structure Matrix Function 1 PreReleaseBuzz SpecialEffects 2 .869 * -.238 .778 * .232 Project Report: Recipe for Hindi Cinema Blockbuster | 90
  • 91. .704 StarCastFitwithScript -.301 * .361 .670 QualityofMusic * .677 DirectionProductionBanner * .325 Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function Functions at Group Centroids Temp Function 1 2 1 1.645 -.048 2 -1.318 -.048 3 .003 .108 Unstandardized canonical discriminant functions evaluated at group means Classification Statistics Classification Processing Summary Processed 650 Missing or out-of-range Excluded 0 group codes At least one missing 0 discriminating variable Used in Output 650 Prior Probabilities for Groups Temp Prior Cases Used in Analysis Unweighted Weighted 1 .333 200 200.000 2 .333 250 250.000 3 .333 200 200.000 1.000 650 650.000 Total Project Report: Recipe for Hindi Cinema Blockbuster | 91
  • 92. Classification Results Temp a,c Predicted Group Membership 1 2 Total 3 1 4 43 200 2 0 180 70 250 3 25 38 137 200 1 76.5 2.0 21.5 100.0 2 .0 72.0 28.0 100.0 3 12.5 19.0 68.5 100.0 1 151 4 45 200 2 0 176 74 250 3 25 41 134 200 1 75.5 2.0 22.5 100.0 2 .0 70.4 29.6 100.0 3 Count 153 12.5 20.5 67.0 100.0 Original % Count Cross-validated b % a. 72.3% of original grouped cases correctly classified. b. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. c. 70.9% of cross-validated grouped cases correctly classified. Further Analysis of Data by the Researchers: Category Comparison Analysis Group Category % of Data classified Dominant Factors (decreasing order of dominance) Pre Release Buzz (4.13), Quality of Music (4.11), 1 Blockbuster 76.5 Direction & Production Banner(4.10), Star Cast Fit with Script (4.09) Comments The mean values of all the factors more or less lie in the range of 3.89 -4.13, which justifies that all the factors are equally established in a blockbuster movie of the Drama genre. Quality of Music (2.33), 2 Flop 72.0 Direction & Production Banner (2.33), Star Cast Fit with Script (2.31), The mean values of all the factors are low leading to the poor box-office play. Project Report: Recipe for Hindi Cinema Blockbuster | 92
  • 93. Quality of Music (3.17), 3 Super hit/hit Star Cast Fit with Script (3.15), 68.5 The range of mean values of all the factors 2.89-3.17, thus showing an average rating. Direction & Production Banner (3.08), Note: The above dominant factors play a major role in the classification of a movie in the particular category. Points of Parity: Assumption is that the mean values of the factors of the two categories can differ by a maximum of 0.30 to establish a point of parity. Group 1 2 3 1 All None None All None 2 3 All Inference: The factors above do not form the distinguishing element which categorises a movie into either of the groups as per the survey data collected. The stress on these factors, according to Moviegoers do not make a striking difference for the success of a movie or the movement of a movie from the lesser performing category to a better performing category. Points of Difference: Assumption is that the mean values of the factors of the two categories should differ by a minimum of 0.40 to establish a point of difference. Group 3 Star Cast Fit with Script, Direction & Production Banner, Direction & Production Banner, Quality of Music, Quality of Music, Pre Release buzz, Pre Release buzz, Special Effects None 2 Star Cast Fit with Script, 1 1 Special Effects Star Cast Fit with Script, Direction & Production Banner, 2 None Quality of Music, Pre Release buzz, Special Effects 3 None Project Report: Recipe for Hindi Cinema Blockbuster | 93
  • 94. Inference: The above factors are the major differentiating features between the categories. The audience rates the better performing category with high factor values than the low/moderate category of movie. E.g. consider category 1 & 3, in this case, all the 5 factors are differentiating factors. If we consider one of the factor, ‘Pre Release Buzz’. For a blockbuster movie the mean rating provided by the audience is 4.13 (which is the highest rated factor for blockbuster movie).The same factor when considered in case of a super hit/hit movie, it is rated 2.93 points, which is quite low compared to the former rating. Thus the strength of this factor in a movie, contributes to the judgment whether it would perform well at the box-office or not. If a movie in drama genre concentrates on building the strength of the factors distilled above (listed in the table). It can easily move to a better performing category, thus capturing the box-office share. Project Report: Recipe for Hindi Cinema Blockbuster | 94
  • 95. 7. References 1. India Entertainment & Media Outlook 2012 – A Report by PwC and CII (Confederation of Indian Industry 2. Film Industry in India: New Horizons - A Report by Ernst & Young and LA India Film Council, 2011 3. BoxOfficeIndia.com (boxofficeindia.com/showProd.php?itemCat=317&catName=MjAxMC0yMDE5) 4. Indian Media & Entertainment Report 2012 – A report by FICCI-KPMG, FICCI Frames 2012 Project Report: Recipe for Hindi Cinema Blockbuster | 95