Information Technology Management Essay Example for Free

Information Technology Management Essay Information and communication technologies play critical roles in sustaining an organizations growth and profitability (Galliers Leidner, 2003). If managed properly, investments in information and communication technologies can improve efficiency and effectiveness of business processes and an organization’s competitive posture in the market. Proper management of information and communication technologies investments can also enrich people’s lives in the organization improving job satisfaction and productivity. Galliers Leidner, 2003). Success in managing information and communication investments depend largely on exercising good management practices like human capital management, staff training management, information technology architecture management and software management (Galliers Leidner, 2003). However, with all the potential benefits of investments in information and communication projects, they can be risky, costly and unproductive if not managed properly (Galliers Leidner, 2003). Organizations should therefore strive to attract and retain information technology personnel that are qualified and talented to ensure the success of information and technology investments. This is further complicated by a tight information and technology labor market where qualified information and technology personnel enjoy high mobility. Discussion The position I am required to design is a leadership position intended to provide technical direction and guide an organization in implementing strategic information and communication projects (Food and Agriculture organization of the United Nations, 2010). The occupant of this position is expected to be able to handle a wide range of duties and responsibilities. He or she should be able to use his/ her exposure, technical experience and business knowledge in developing an organization’s technical plans and to advise senior management on information technology strategies, standards and governance (Galliers Leidner, 2003). In addition he or she will be expected to monitor the industry’s trends in information technology and respond appropriately by formulating long term information technology strategies capable of improving an organization’s competitiveness. The senior information technology manager will work under the general supervision of the chief executive officer. In line with work plans and resource allocation coordination provided by the chief executive officer, he or she will be responsible for effective planning, supervision and delivery of assigned functions that fall within an information and communication department (Food and Agriculture organization of the United Nations, 2010) so as to ensure that an organization’s requirements for information systems and information technology are effectively and efficiently met. He will also be responsibility for ensuring that an organization’s computer systems have the capacity to meet the business needs of an organization by either upgrading existing systems or developing new systems (Info Tech Employment, 2008). Additional functions will include participation in planning, coordinating and setting policies for the development and implementation of an organization’s information technology strategies, supporting standards, procedures and practices, supervising and coordinating works of external firms in special projects or functions to ensure quality and timely delivery, providing consultant services in regards to procurement of new information technology equipments and computer systems, supervising members of staff assigned to special projects or functions and coordinating their training and development to ensure that they are up to the tasks and finally, developing and monitoring key performance indicators of assigned functions within an information technology department (Food and Agriculture organization of the United Nations, 2010). The senior information technology manager is expected to have an excellent working knowledge in information technology and a commitment to keep up to dat e with the latest development (Galliers Leidner, 2003). He should demonstrate people’s management skills with an ability to motivate staff members, provide a cooperative and productive work environment, manage resources effectively to achieve objectives, organize and coordinate work in the department and explain technical issues clearly (Info Tech Employment, 2008). He or she will be tasked with leading changes that fall within the information technology department, hence must be able to integrate organizational and departmental goals, priorities and values. In addition, he or she should have experience in managing large scale projects in information systems and technology (Food and Agriculture organization of the United Nations, 2010). To fulfill these expectations, a university degree in computer science or related fields and appropriate professional certifications are mandatory. Moreover, the candidate of choice should have at least five years management experience in information technology. He should demonstrate experience with standard software applications and data manipulation, analysis and interpretation tools (Food and Agriculture organization of the United Nations, 2010). Conclusion Information technology investments can be very beneficial to an organization as a whole if they are managed properly. Organizations should therefore strive to hire and retain qualified, experienced and talented information technology managers. This is not easy given the current information technology labor market. The labor market is characterized by high mobility of qualified labor and organizations must put in place effective measures to ensure they hire the right people. One of the measures an organization should take is defining clear structures and responsibilities of all employees in the information technology department. The senior information technology manager will provide technical direction and guidance to the organization in implementing strategic information technology projects. He will be responsible for ensuring effective and efficient management of resources within the information technology department.

Beardless Children :: Arthurian Legends English Literature Essays

Beardless Children Sir Gawain and the Green Knight is considered to be one of the finest Arthurian romances in English. Unfortunately, the 14th-century author of the epic remains unknown. The poem describes a common game at the time the "Beheading Game," which turns out to be a great physical as well as moral challenge to the main character, Sir Gawain. The passage (130-202) of Sir Gawain and the Green Knight describes the appearance of a strange knight in King Arthur's court. The anonymous author of the epic describes the rider in great detail, emphasizing the importance of this character. The passage is intended to arouse readers' curiosity, and at the same time, to introduce the mighty danger that the main character, Sir Gawain, will have to face. Furthermore, the strange knight is shown to be a test or trial for King Arthur and his knights. Finally, the passage presents the actual dynamics of Arthur's court as incompatible with the poet's initial praising of nobility, justice and chivalric ideals. The Green Knight is clearly a magical figure. This strange rider is of green hue, and he is riding a green horse. Physically, the knight is presented as strong and of a great size: From broad neck to buttocks so bulky and thick, And his loins and his legs so long and so great, Half a giant on earth I hold him to be...(138 - 140). The author gives these characteristics to the character for a reason. Possibly, the author aims to arouse interest of the readers or to emphasize the danger that Sir Gawain is about to face. However, at this point of the story, the reader is unaware of the true identity of the Green Knight, which makes it more exciting to read the poem. The passage describes the great festivities in King Arthur's court during the celebration of Christmas. And already Arthur is portrayed behaving childishly, when he refuses to eat unless he hears an entertaining story: But Arthur would not eat till all were served; So light was his lordly heart, and a little boyish; And also a point of pride pricked him in heart, For he nobly had willed, he would never eat.

International Movie Revenues: Determinants and Impact of the Financial Crisis

Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Empirical Project Assignment — Econometrics II Due on Friday, 13 January 2012, 11. 00 International movie revenues: determinants and impact of the financial crisis Marek Kre? mer, Jan Mati? ka c c International movie revenues : Determinants and impact of the ? nancial crisis Table of Contents Abstract Keywords Introduction Literature survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data analysis variables used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion References primary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . secondary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix Descriptive statistics for the dependent variables model 1 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted values plot . . . . . Breusch-Pagan test for heteroskedasticity . model 2 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted values plot . . . . . . Breusch-Pagan test for heteroskedasticity . The correlation matrix . . . . . . . . . . . . 2 2 2 2 3 3 4 4 4 4 6 6 6 7 8 8 8 8 9 9 10 11 11 12 13 13 14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marek Kre? mer, Jan Mati? ka c c Page 1 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Abstract This empirical project examines the determinants of international box o? ce revenues for movies produced in United States during 2006 – 2010. Our sample consists of 424 ? lms released in this period. We also test the hypothesis if the world ? nancial crisis had any signi? can t impact on the international box o? ce revenues. Keywords the ? ancial crisis, movie international box o? ce revenue, movies produced in the United States, budget, rating, Academy Awards, Introduction When choosing a topic of our empirical paper we were considering di? erent suggestions. As we both are pretty much interested in movies we ? nally decided to exit a viewer seat for a while and perform an empirical study on the movie industry. While being newcommers in sophisticated movie data analysis, we needed ? rst to get acquainted with important theoretical concepts and empirical papers concerning this topic. Literature survey When going down the history, [Litman, 1983] was the ? st who has attempted to predict the ? nancial success of ? lms. He has performed a multiple regression and found a clear evidence that various independent variables have a signi? cant and serious in? uence on the ? nal success of a movie. Litemans work has been gradually getting developed, [Faber & Oâ₠¬â„¢Guinn, 1984] tested the in? uence of ? lm advertising. They proved, that movie critics and word-of-mouth are less important then movie previews and excerpts when explaininng movie succes after going on public. [Eliashberg & Shugan, 1997] explored the impact of restricted-rating labeled movies on their box o? e performance. [Terry, Butler & De’Armond, 2004] analysed the determinants of movie video rental revenue, ? nding Academy Award nominations as the dominant factor. [King, 2007] followed their research and used U. S. movie data to ? nd the connection between the criticism and box o? ce earnings†¦ Many other authors has extended the initial work of [Litman, 1983], but none of them has focused on the key factors of the international box o? ce revenues as we planned to. So we ? nally decided to use [Terry, Cooley & Zachary, 2010] as our primary source. Their object of interest is very much similar to our resarch.Therefore we studied their metodology the most and we u se their results in the analytical part as a primary resource of comparison. Marek Kre? mer, Jan Mati? ka c c Page 2 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Data We got quickly stucked realising that the strong majority of movie data on the internet are not free available. It was quite a surprise because there are many movie-oriented sites with seemingly endless data access. But when there is a need of more profound, well structured and complete set of random data everything gets little bit tricky.After hours of searching, we luckily got to a 30 days free access to this kind of databases [opusdata. com] and got the core data for our analysis. Then we wanted to add some interesting or usefull variables just as the movie rating or the number of AcademyAwards to complete our dataset. It has been done using well known and free accessed databases [imdb. com], [numbers. com] and [boxo? cemojo. com]. Thanks to our literature survey we discovered a model which we have thought would be interesting to test on di? erent or new data. The most interesting would be to test it on our domestic data but these are quite di? ult to obtain (as explained before). Anyway, it would be possible to get data for the highest grossing ? lms but that would violate the assumption of random sample. Therefore we decided to use data from U. S. and Canada which we considered the most likely to obtain. We also wanted to test whether the ? nancial crisis have had an impact on movie box o? ce revenues and whether the world ? nancial crisis made people less likely to go to the cinema. Model We considered several models and in the end we used two models. The ? rst one is just the same as the one used in paper [Terry, Cooley & Zachary, 2010], but it is slightly modi? d by using di? erent data plus setting the crisis variable. We considered it as a dummy variable, which was 1 if the movie was released during crisis (2008-2009), otherwise it is equal to zer o. As it was proposed before, this model has been used as a comparison to the original model [Terry, Cooley & Zachary, 2010] wihle we wanted to test whether their inference holds up with slightly di? erent and newer data. In the second model we tried to use a slightly di? erent approach. We used a time series model with year dummies and we also used all the variables which we obtained and were statistically signi? ant. Our ? rst model is basic linear regression with cross-sectional data. Our data are a random sample thanks to [opusdata. com] query which was capable of selecting a random sample of movies. We have tested all the variables for multicollinearity with the correlation matrix and there is no proof for multicollinearity in our used variables. The only high collinearity is between domestic and budget variables, which is about 0. 75. After running the regressions we have used the Breusch-Pagan test for heteroscedasticity and the chi squared was really high therefore showing s igns of strong heteroscedasticity.Even after looking at the graph of residuals against ? tted values it was clear that the heteroscedasticity is present. Therefore we had to run the regressions with the heteroscedasticity robust errors. We therefore tested in both models for presence of these: †¢ the variables which have an impact on movie international box revenues †¢ any signi? cant impact of ? nancial crisis on these revenues Marek Kre? mer, Jan Mati? ka c c Page 3 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Data analysis Here we list all the used variables in both models and their a description. ariables used academy awards . . . . . . . . . number of Academy Awards a ? lm earned action . . . . . . . . . . . . . . . . . . categorical variable for movies in action genre animation . . . . . . . . . . . . . . . categorical variable for movies in animation production method budget . . . . . . . . . . . . . . . . . . the estimated pr oduction and promotion cost of a movie comedy . . . . . . . . . . . . . . . . . . categorical variable for movies in comedy genre crisis . . . . . . . . . . . . . . . . . . dummy variable for movies released during crisis domestic . . . . . . . . . . . . . . . omestic box o? ce earnings horror . . . . . . . . . . . . . . . . . . categorical variable for movies in horror genre international . . . . . . . . . . . . international box o? ce earnings kids . . . . . . . . . . . . . . . . . . categorical variable for movies for children rating . . . . . . . . . . . . . . . . . . average user rating from the [imdb. com] source ratingR . . . . . . . . . . . . . . . . . . is a categorical variable for movies with a restricted rating romantic . . . . . . . . . . . . . . . . . . categorical variable for movies in romantic genre sequel . . . . . . . . . . . . . . . . . categorical variable for movies derived from a previously released ? lm y06 ? y10 . . . . . . . . . . . . . . . . . . dummy vari able for movies released in a year The list of variables is followed by both model equations and reggression table comparism, while model 1 and model 2 mean the original [Terry, Cooley & Zachary, 2010] model and our new model respectivelly. model 1 international = ? 0 + ? 1 domestic + ? 2 action + ? 3 kids + ? 4 ratingR+ + ? 5 sequel + ? 6 rating + ? 7 academy awards + ? 8 budget + ? 9 crisis model 2 international = + + ? 0 + ? 1 academy awards + ? 2 budget + ? 3 domestic + ? 4 sequel + ? horror + ? 6 romantic + ? 7 comedy + ? 8 action + ? 9 ratingR + ? 10 animation + ? 11 y06 + ? 12 y07 + ? 13 y08 + ? 14 y09 Marek Kre? mer, Jan Mati? ka c c Page 4 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Table 1: Model comparison model 1 domestic action kids rating R sequel rating academy awards budget crisis horror romantic comedy animation y 06 y 07 y 08 y 09 Constant Observations t statistics in parentheses ? model 2 1. 025 (13. 31) -18. 56? (-2. 29) 1 . 028 (12. 70) -13. 43 (-1. 79) 48. 33? (2. 10) 5. 922 (1. 52) 26. 91? (2. 06) 0. 309 (1. 42) 6. 978? (2. 33) 0. 68 (5. 48) -5. 320 (-1. 01) 9. 259? (2. 36) 28. 74? (2. 16) 7. 097 (2. 59) 0. 508 (4. 73) -9. 867? (-2. 23) 13. 41 (1. 79) -17. 77 (-3. 31) 52. 02 (2. 87) -7. 962 (-1. 24) 1. 182 (0. 17) -6. 748 (-1. 01) -11. 79 (-1. 30) -43. 25 (-3. 05) 424 -15. 11? (-2. 41) 424 p < 0. 05, p < 0. 01, p < 0. 001 Marek Kre? mer, Jan Mati? ka c c Page 5 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Results model 1 After running the ? rst regression we get quite similar results as [Terry, Cooley & Zachary, 2010], so their inference holds up even under our data.The similar results we get are that one dollar in revenues in US makes $1. 02 in international revenues, therefore succesful movie in US is likely to be similarly succesful in international theatres, if movie is a sequel it adds to revenues about $26 mil. , every academy award adds about $7 mil. and every additional dollar spent on budget adds about $0. 57 so there is about 57% return on budget. We also have similarly insigni? cant variables which are whether is movie rated as restricted and how great or poorly is movie rated by critics or other people.That means that international audience is not in? uenced by age restrictions and critical movie ratings. When we look at our and theirs results regarding the genres then we get quite di? erent results. They say that when a movie is of an action genre then it adds about $26 mil. whereas we obtained results that revenues for an action movie should be lower about $13 mil. and our result for children movies is two times larger and it says that a children movie should make about $48 mil. more. It could be explained that movie genre preferences shifted in the last two years.But more likely explanation is the di? erence in our data in labeling the movies. In our data we have had more detailed labeling and movies which they had labe led as action movies, we had labeled adventure movies etc. Therefore the strictly action movie genre is not so probable to make money as it would seem. Action movies are usually of low quality and many of them could be labeled as B-movies which usually are not very likely to have high revenues. The children movies could be getting more popular and taking children to the movies could be getting more usual thing.Our last and new variable is the crisis dummy which is not signi? cant and therefore we have no proof that the ? nancial crisis had any e? ect on movie revenues. Our model has quite high R2 which is about 0. 83, that is even higher then [Terry, Cooley & Zachary, 2010] have. But the main reason behind this high R2 is that most of the variation in data is explained by US revenues. If we regress international revenues on domestic alone we still get high R2 which is about 0. 59. model 2 In our time series model we get quite similar results as in the ? rst one. We have there ? e ne w variables which are genres comedy, romantic and horror, animation dummy, which tells us whether the movie is animated or not and year dummies. Our model implies that when a movie is a comedy it will make about $17 mil. less in revenues, when horror about $10 mil. less, when romantic about $13 mil. more and when animated it will add about $52 mil to its revenues. The restricted rating is now also statistically signi? cant and it should add to the revenues about $9 mil. which is quite unexpected. Y ear dummies are statistically non-signi? cant and even when we test them for joint signi? ance they are jointly non-signi? cant. Therefore even in this model there appears no reason to believe that the ? nancial crisis or even year makes di? erence in the movie revenues. Marek Kre? mer, Jan Mati? ka c c Page 6 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Conclusion The inferences from our models are quite like we expected. We expected that people ar e more likely to go to cinema to see movies that had won academy awards, that were succesful in U. S. theatres and that are some kind of sequel to previous succesful movies. The resulting e? cts of di? erent movie genres could be quite puzzling but these e? ects depend highly on quality of the movies released these years and on the mood and taste of current society. If we had had larger sample with data from many years then it is possible that we would have seen trends in the di? erent movie genres. The insigni? cance of the ? nancial crisis on movie revenues was also likely because the severity of the crisis and impact on regular citizen has not been so large that it would in? uence his attendence of movie theatres. Marek Kre? mer, Jan Mati? ka c c Page 7 of 14International movie revenues : Determinants and impact of the ? nancial crisis Reference primary [Terry, Cooley & Zachary, 2010] Terry, Neil, John W. Cooley, & Miles Zachary (2010). The Determinants of Foreign Box O? ce Reven ue for English Language Movies. Journal of International Business and Cultural Studies, 2 (1), 117-127. secondary [Eliashberg & Shugan, 1997] Eliashberg, Jehoshua & Steven M. Shugan (1997). Film Critics: In? uencers or Predictors? Journal of Marketing, 61, 68-78. [Faber & O’Guinn, 1984] Faber, Ronald & Thomas O’Guinn (1984). E? ect of Media Advertising and Other Sources on Movie Selection.Journalism Quarterly, 61 (summer), 371-377. [King, 2007] King, Timothy (2007). Does ? lm criticism a? ect box o? ce earnings? Evidence from movies released in the U. S. in 2003. Journal of Cultural Economics, 31, 171-186. [Litman, 1983] Litman, Barry R. (1983). Predicting Success of Theatrical Movies: An Empirical Study. Journal of Popular Culture, 16 (spring), 159-175. [Ravid, 1999] Ravid, S. Abraham (1999). Information, Blockbusters, and Stars: A Study of the Film Industry. Journal of Business, 72 (4), 463-492. [Terry, Butler & De’Armond, 2004] Terry, Neil, Michael Butler & D e’Arno De’Armond (2004).The Economic Impact of Movie Critics on Box O? ce Performance. Academy of Marketing Studies Journal, 8 (1), page 61-73. data sources [opusdata. com] Opus data – movie data through a query interface. 30-days free trial. http://www. opusdata. com/ [imdb. com] The Internet Movie Database (IMDb). The biggest, best, most award-winning movie site on the planet. http://www. imdb. com [numbers. com] The numbers. Box o? ce data, movies stars, idle speculation. http://www. the-numbers. com [boxo? cemojo. com] Box o? ce mojo. Movie web site with the most comprehensive box o? ce database on the Internet. ttp://www. boxofficemojo. com Marek Kre? mer, Jan Mati? ka c c Page 8 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Appendix Descriptive statistics for the dependent variables Marek Kre? mer, Jan Mati? ka c c Page 9 of 14 International movie revenues : Determinants and impact of the ? nancial crisis model 1 Regr ession of the original model published in [Terry, Cooley & Zachary, 2010] Marek Kre? mer, Jan Mati? ka c c Page 10 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Residuals versus ? tted values plotBreusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 11 of 14 International movie revenues : Determinants and impact of the ? nancial crisis model 2 Regression of our model Marek Kre? mer, Jan Mati? ka c c Page 12 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Residuals versus ? tted values plot Breusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 13 of 14 International movie revenues : Determinants and impact of the ? nancial crisis The correlation matrix Marek Kre? mer, Jan Mati? ka c c Page 14 of 14 International Movie Revenues: Determinants and Impact of the Financial Crisis Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Empirical Project Assignment — Econometrics II Due on Friday, 13 January 2012, 11. 00 International movie revenues: determinants and impact of the financial crisis Marek Kre? mer, Jan Mati? ka c c International movie revenues : Determinants and impact of the ? nancial crisis Table of Contents Abstract Keywords Introduction Literature survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data analysis variables used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion References primary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . secondary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix Descriptive statistics for the dependent variables model 1 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted values plot . . . . . Breusch-Pagan test for heteroskedasticity . model 2 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted values plot . . . . . . Breusch-Pagan test for heteroskedasticity . The correlation matrix . . . . . . . . . . . . 2 2 2 2 3 3 4 4 4 4 6 6 6 7 8 8 8 8 9 9 10 11 11 12 13 13 14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marek Kre? mer, Jan Mati? ka c c Page 1 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Abstract This empirical project examines the determinants of international box o? ce revenues for movies produced in United States during 2006 – 2010. Our sample consists of 424 ? lms released in this period. We also test the hypothesis if the world ? nancial crisis had any signi? can t impact on the international box o? ce revenues. Keywords the ? ancial crisis, movie international box o? ce revenue, movies produced in the United States, budget, rating, Academy Awards, Introduction When choosing a topic of our empirical paper we were considering di? erent suggestions. As we both are pretty much interested in movies we ? nally decided to exit a viewer seat for a while and perform an empirical study on the movie industry. While being newcommers in sophisticated movie data analysis, we needed ? rst to get acquainted with important theoretical concepts and empirical papers concerning this topic. Literature survey When going down the history, [Litman, 1983] was the ? st who has attempted to predict the ? nancial success of ? lms. He has performed a multiple regression and found a clear evidence that various independent variables have a signi? cant and serious in? uence on the ? nal success of a movie. Litemans work has been gradually getting developed, [Faber & Oâ₠¬â„¢Guinn, 1984] tested the in? uence of ? lm advertising. They proved, that movie critics and word-of-mouth are less important then movie previews and excerpts when explaininng movie succes after going on public. [Eliashberg & Shugan, 1997] explored the impact of restricted-rating labeled movies on their box o? e performance. [Terry, Butler & De’Armond, 2004] analysed the determinants of movie video rental revenue, ? nding Academy Award nominations as the dominant factor. [King, 2007] followed their research and used U. S. movie data to ? nd the connection between the criticism and box o? ce earnings†¦ Many other authors has extended the initial work of [Litman, 1983], but none of them has focused on the key factors of the international box o? ce revenues as we planned to. So we ? nally decided to use [Terry, Cooley & Zachary, 2010] as our primary source. Their object of interest is very much similar to our resarch.Therefore we studied their metodology the most and we u se their results in the analytical part as a primary resource of comparison. Marek Kre? mer, Jan Mati? ka c c Page 2 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Data We got quickly stucked realising that the strong majority of movie data on the internet are not free available. It was quite a surprise because there are many movie-oriented sites with seemingly endless data access. But when there is a need of more profound, well structured and complete set of random data everything gets little bit tricky.After hours of searching, we luckily got to a 30 days free access to this kind of databases [opusdata. com] and got the core data for our analysis. Then we wanted to add some interesting or usefull variables just as the movie rating or the number of AcademyAwards to complete our dataset. It has been done using well known and free accessed databases [imdb. com], [numbers. com] and [boxo? cemojo. com]. Thanks to our literature survey we discovered a model which we have thought would be interesting to test on di? erent or new data. The most interesting would be to test it on our domestic data but these are quite di? ult to obtain (as explained before). Anyway, it would be possible to get data for the highest grossing ? lms but that would violate the assumption of random sample. Therefore we decided to use data from U. S. and Canada which we considered the most likely to obtain. We also wanted to test whether the ? nancial crisis have had an impact on movie box o? ce revenues and whether the world ? nancial crisis made people less likely to go to the cinema. Model We considered several models and in the end we used two models. The ? rst one is just the same as the one used in paper [Terry, Cooley & Zachary, 2010], but it is slightly modi? d by using di? erent data plus setting the crisis variable. We considered it as a dummy variable, which was 1 if the movie was released during crisis (2008-2009), otherwise it is equal to zer o. As it was proposed before, this model has been used as a comparison to the original model [Terry, Cooley & Zachary, 2010] wihle we wanted to test whether their inference holds up with slightly di? erent and newer data. In the second model we tried to use a slightly di? erent approach. We used a time series model with year dummies and we also used all the variables which we obtained and were statistically signi? ant. Our ? rst model is basic linear regression with cross-sectional data. Our data are a random sample thanks to [opusdata. com] query which was capable of selecting a random sample of movies. We have tested all the variables for multicollinearity with the correlation matrix and there is no proof for multicollinearity in our used variables. The only high collinearity is between domestic and budget variables, which is about 0. 75. After running the regressions we have used the Breusch-Pagan test for heteroscedasticity and the chi squared was really high therefore showing s igns of strong heteroscedasticity.Even after looking at the graph of residuals against ? tted values it was clear that the heteroscedasticity is present. Therefore we had to run the regressions with the heteroscedasticity robust errors. We therefore tested in both models for presence of these: †¢ the variables which have an impact on movie international box revenues †¢ any signi? cant impact of ? nancial crisis on these revenues Marek Kre? mer, Jan Mati? ka c c Page 3 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Data analysis Here we list all the used variables in both models and their a description. ariables used academy awards . . . . . . . . . number of Academy Awards a ? lm earned action . . . . . . . . . . . . . . . . . . categorical variable for movies in action genre animation . . . . . . . . . . . . . . . categorical variable for movies in animation production method budget . . . . . . . . . . . . . . . . . . the estimated pr oduction and promotion cost of a movie comedy . . . . . . . . . . . . . . . . . . categorical variable for movies in comedy genre crisis . . . . . . . . . . . . . . . . . . dummy variable for movies released during crisis domestic . . . . . . . . . . . . . . . omestic box o? ce earnings horror . . . . . . . . . . . . . . . . . . categorical variable for movies in horror genre international . . . . . . . . . . . . international box o? ce earnings kids . . . . . . . . . . . . . . . . . . categorical variable for movies for children rating . . . . . . . . . . . . . . . . . . average user rating from the [imdb. com] source ratingR . . . . . . . . . . . . . . . . . . is a categorical variable for movies with a restricted rating romantic . . . . . . . . . . . . . . . . . . categorical variable for movies in romantic genre sequel . . . . . . . . . . . . . . . . . categorical variable for movies derived from a previously released ? lm y06 ? y10 . . . . . . . . . . . . . . . . . . dummy vari able for movies released in a year The list of variables is followed by both model equations and reggression table comparism, while model 1 and model 2 mean the original [Terry, Cooley & Zachary, 2010] model and our new model respectivelly. model 1 international = ? 0 + ? 1 domestic + ? 2 action + ? 3 kids + ? 4 ratingR+ + ? 5 sequel + ? 6 rating + ? 7 academy awards + ? 8 budget + ? 9 crisis model 2 international = + + ? 0 + ? 1 academy awards + ? 2 budget + ? 3 domestic + ? 4 sequel + ? horror + ? 6 romantic + ? 7 comedy + ? 8 action + ? 9 ratingR + ? 10 animation + ? 11 y06 + ? 12 y07 + ? 13 y08 + ? 14 y09 Marek Kre? mer, Jan Mati? ka c c Page 4 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Table 1: Model comparison model 1 domestic action kids rating R sequel rating academy awards budget crisis horror romantic comedy animation y 06 y 07 y 08 y 09 Constant Observations t statistics in parentheses ? model 2 1. 025 (13. 31) -18. 56? (-2. 29) 1 . 028 (12. 70) -13. 43 (-1. 79) 48. 33? (2. 10) 5. 922 (1. 52) 26. 91? (2. 06) 0. 309 (1. 42) 6. 978? (2. 33) 0. 68 (5. 48) -5. 320 (-1. 01) 9. 259? (2. 36) 28. 74? (2. 16) 7. 097 (2. 59) 0. 508 (4. 73) -9. 867? (-2. 23) 13. 41 (1. 79) -17. 77 (-3. 31) 52. 02 (2. 87) -7. 962 (-1. 24) 1. 182 (0. 17) -6. 748 (-1. 01) -11. 79 (-1. 30) -43. 25 (-3. 05) 424 -15. 11? (-2. 41) 424 p < 0. 05, p < 0. 01, p < 0. 001 Marek Kre? mer, Jan Mati? ka c c Page 5 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Results model 1 After running the ? rst regression we get quite similar results as [Terry, Cooley & Zachary, 2010], so their inference holds up even under our data.The similar results we get are that one dollar in revenues in US makes $1. 02 in international revenues, therefore succesful movie in US is likely to be similarly succesful in international theatres, if movie is a sequel it adds to revenues about $26 mil. , every academy award adds about $7 mil. and every additional dollar spent on budget adds about $0. 57 so there is about 57% return on budget. We also have similarly insigni? cant variables which are whether is movie rated as restricted and how great or poorly is movie rated by critics or other people.That means that international audience is not in? uenced by age restrictions and critical movie ratings. When we look at our and theirs results regarding the genres then we get quite di? erent results. They say that when a movie is of an action genre then it adds about $26 mil. whereas we obtained results that revenues for an action movie should be lower about $13 mil. and our result for children movies is two times larger and it says that a children movie should make about $48 mil. more. It could be explained that movie genre preferences shifted in the last two years.But more likely explanation is the di? erence in our data in labeling the movies. In our data we have had more detailed labeling and movies which they had labe led as action movies, we had labeled adventure movies etc. Therefore the strictly action movie genre is not so probable to make money as it would seem. Action movies are usually of low quality and many of them could be labeled as B-movies which usually are not very likely to have high revenues. The children movies could be getting more popular and taking children to the movies could be getting more usual thing.Our last and new variable is the crisis dummy which is not signi? cant and therefore we have no proof that the ? nancial crisis had any e? ect on movie revenues. Our model has quite high R2 which is about 0. 83, that is even higher then [Terry, Cooley & Zachary, 2010] have. But the main reason behind this high R2 is that most of the variation in data is explained by US revenues. If we regress international revenues on domestic alone we still get high R2 which is about 0. 59. model 2 In our time series model we get quite similar results as in the ? rst one. We have there ? e ne w variables which are genres comedy, romantic and horror, animation dummy, which tells us whether the movie is animated or not and year dummies. Our model implies that when a movie is a comedy it will make about $17 mil. less in revenues, when horror about $10 mil. less, when romantic about $13 mil. more and when animated it will add about $52 mil to its revenues. The restricted rating is now also statistically signi? cant and it should add to the revenues about $9 mil. which is quite unexpected. Y ear dummies are statistically non-signi? cant and even when we test them for joint signi? ance they are jointly non-signi? cant. Therefore even in this model there appears no reason to believe that the ? nancial crisis or even year makes di? erence in the movie revenues. Marek Kre? mer, Jan Mati? ka c c Page 6 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Conclusion The inferences from our models are quite like we expected. We expected that people ar e more likely to go to cinema to see movies that had won academy awards, that were succesful in U. S. theatres and that are some kind of sequel to previous succesful movies. The resulting e? cts of di? erent movie genres could be quite puzzling but these e? ects depend highly on quality of the movies released these years and on the mood and taste of current society. If we had had larger sample with data from many years then it is possible that we would have seen trends in the di? erent movie genres. The insigni? cance of the ? nancial crisis on movie revenues was also likely because the severity of the crisis and impact on regular citizen has not been so large that it would in? uence his attendence of movie theatres. Marek Kre? mer, Jan Mati? ka c c Page 7 of 14International movie revenues : Determinants and impact of the ? nancial crisis Reference primary [Terry, Cooley & Zachary, 2010] Terry, Neil, John W. Cooley, & Miles Zachary (2010). The Determinants of Foreign Box O? ce Reven ue for English Language Movies. Journal of International Business and Cultural Studies, 2 (1), 117-127. secondary [Eliashberg & Shugan, 1997] Eliashberg, Jehoshua & Steven M. Shugan (1997). Film Critics: In? uencers or Predictors? Journal of Marketing, 61, 68-78. [Faber & O’Guinn, 1984] Faber, Ronald & Thomas O’Guinn (1984). E? ect of Media Advertising and Other Sources on Movie Selection.Journalism Quarterly, 61 (summer), 371-377. [King, 2007] King, Timothy (2007). Does ? lm criticism a? ect box o? ce earnings? Evidence from movies released in the U. S. in 2003. Journal of Cultural Economics, 31, 171-186. [Litman, 1983] Litman, Barry R. (1983). Predicting Success of Theatrical Movies: An Empirical Study. Journal of Popular Culture, 16 (spring), 159-175. [Ravid, 1999] Ravid, S. Abraham (1999). Information, Blockbusters, and Stars: A Study of the Film Industry. Journal of Business, 72 (4), 463-492. [Terry, Butler & De’Armond, 2004] Terry, Neil, Michael Butler & D e’Arno De’Armond (2004).The Economic Impact of Movie Critics on Box O? ce Performance. Academy of Marketing Studies Journal, 8 (1), page 61-73. data sources [opusdata. com] Opus data – movie data through a query interface. 30-days free trial. http://www. opusdata. com/ [imdb. com] The Internet Movie Database (IMDb). The biggest, best, most award-winning movie site on the planet. http://www. imdb. com [numbers. com] The numbers. Box o? ce data, movies stars, idle speculation. http://www. the-numbers. com [boxo? cemojo. com] Box o? ce mojo. Movie web site with the most comprehensive box o? ce database on the Internet. ttp://www. boxofficemojo. com Marek Kre? mer, Jan Mati? ka c c Page 8 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Appendix Descriptive statistics for the dependent variables Marek Kre? mer, Jan Mati? ka c c Page 9 of 14 International movie revenues : Determinants and impact of the ? nancial crisis model 1 Regr ession of the original model published in [Terry, Cooley & Zachary, 2010] Marek Kre? mer, Jan Mati? ka c c Page 10 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Residuals versus ? tted values plotBreusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 11 of 14 International movie revenues : Determinants and impact of the ? nancial crisis model 2 Regression of our model Marek Kre? mer, Jan Mati? ka c c Page 12 of 14 International movie revenues : Determinants and impact of the ? nancial crisis Residuals versus ? tted values plot Breusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 13 of 14 International movie revenues : Determinants and impact of the ? nancial crisis The correlation matrix Marek Kre? mer, Jan Mati? ka c c Page 14 of 14

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