Importance Of Internet In E Commerce

Importance Of Internet In E Commerce Chapter 1 It is difficult to define tourism. Tourism is not an industry that is grouped into a single heading within the Standard Industrial Classification (SIC) (Bardgett, 2000). The definition of tourism mostly come from the purchaser, tourist, not the product itself (Bargett, 2000). Thereupon, World Tourism Organization (WTO) (1993) had defined tourism consists of activities of persons travelling to and staying places outside their usual environment for not more than one continually one year for leisure, or business purposes. In Malaysia, tourism industry has been recognizes as second largest foreign exchange earning sector (Mohd Hanafiah Mohd Harun, 2010). According to United Nations World Trade Organization (UNWTO) World Tourism Barometer (2009), in 2008, tourist arrivals in Malaysia are substantially higher than its neighbors. The table below (Table 1.1) provides a good picture on comparison of number of visitors among Malaysia, Singapore and Thailand. On the other hand, as the society has driven into Internet Era, tourism is extensively transformed by the by e-commerce phenomenon (Lee Letho, 2012). Hensdill (1998) described the web as a perfect medium for selling travel. While, with the popularization of computers through the Internet, travel product suppliers such as airlines, car rental companies, and hotels have grasped the opportunity to change their traditional distribution methods by launching their products directly on the Internet (Kasavana, 1997; Montgomery, 1999; Morrison, Taylor, Morrison Morrison 1999). Similarly, travel information is also available on the Internet from regional and national tourism boards in a large amount (Weeks Crouch, 1999). Nowadays, Internet has now become a promotional and advertising tool (Countryman, 1999). According to Martin (2004), the Internet suits the marketing principles for travel and tourism because it: (a) allows travel suppliers to set up a direct link with customers; (b) gets rid of the unfair barriers for customers and suppliers; (c) relieve equal competition; and (d) decreases price discrimination opportunities. Consequently, the Internet not only serves to deliver information (Walle, 1996) but also act as a public relations tool (Connolly, Sigala, 2001). Hence, it is important for travel agents to know which factors can affect travelers intention to use travel website. By knowing this, travel agents not only can formulate the marketing strategies but also can design a website which the features are different from the competitors in order to gain competitive advantages. Problem Statement Due to the rapid growing of tourism industry in Malaysia and the importance of internet in e-commerce, many of the travel agencies have created their own website. The rivalry among the travel website itself has increase rapidly. Hence, to be an outstanding website among the others, and to gain the competitive advantage, the travel agency need to know which factor has the most significant influence the user to use travel website. The result is not necessary only for strategic marketing formulation, but also for the design of the web site features that are superior from competitors (Aziz et al., 2010). The main problem that influences the users to adopt the websites will be the multi stage process for the travel decision making process (Aziz et al., 2010). The consumers need to look careful each step before until final steps to successful purchases the travel packages. For instance, users may need to login the membership before to process to purchase the package. This continue with the double confirm before the purchased package is correct. Until the payment step, users also need to check the all information is correct and submit to pay. Besides that, the security, privacy and trust concerns are other minor problems that influence the users to adopt the websites. The informations quality, security, privacy, trust and the companys reputation have strongly influence on online consumers trust in the websites (Kim, Ferrin, Raghav Rao, 2007) According to the past study of Aziz et al. (2010), social influence is the superiority in encouraging people to use travel websites. While, in the study of Ahmad and Juhdi (2008), they found out that internet self-efficacy had affected Malaysian travelers to use travel website. In the study of Chen (2006), trustworthy has a significant influence in an online travel websites. There have been various studies conducted in different country such as, a study on web site quality for online travel agency in United States (Park, Gretzel, Sirakaya-Turk, 2007); the factor affecting consumers attitude towards online reservation by Vathianathan and Roy (2009) in India; and examine e-travel site, an empirical study in Taiwan (Lin, 2010). Yet, only a few studies have been carried out in Malaysia. In the study of Aziz et al. (2010), the influence of predictors on travel website, the study is only conducted in Klang Valley, which is just a portion of Malaysia. The result cannot represent as a whole. While, in the study of Ahmad and Juhdi (2008), the number of respondents is small and mainly specific in e-group subscribers who were basically active users of internet. Thus, the generalization of the findings is questionable. Research Questions and Objectives Research Questions General Objectives To identify and examine factors that affect Malaysian travelers intention to use travel website. General Question What are the factors that affect Malaysian travelers intention to use travel website? Specific Objectives To examine the relationship between Perceived Ease of Use (PEOU) and Malaysian travelers intention to use travel website. To examine the relationship between perceived usefulness (PU) and Malaysian travelers intention to use travel website. To examine the relationship between social influences and Malaysian travelers intention to use travel website. To examine the relationship between self efficacy and Malaysian travelers intention to use travel website. To examine the relationship between travel motivation and Malaysian travelers intention to use travel website. Specific Questions Is there any relationship between Perceived Ease of Use (PEOU) and Malaysian travelers intention to use travel website? Is there any relationship between perceived usefulness (PU) and Malaysian travelers intention to use travel website? Is there any relationship between social influences and Malaysian travelers intention to use travel website? Is there any relationship between self efficacy and Malaysian travelers intention to use travel website? Is there any relationship between travel motivation and Malaysian travelers intention to use travel website? Source: Developed for the research Significance of the Study Generally, the acceptance of the internet among users is normally examined with technology acceptance model (TAM) (Aziz et al. 2010). However, the two variable of TAM (Perceived Ease of Use and Perceived Usefulness) are still insufficient to test on the factors affecting Malaysian travelers intention to use travel website. Therefore, the proposed model of this study is an extension of Technology Acceptance Model (TAM) as theoretical foundation. The model will be adding other variables such as, social influence, self efficacy and travel motivation to test on the dependent variable. Travel agency would be among the first to benefit from the study. The finding of this study may help the travel agency to understand several affected factors that influence Malaysian travelers to use travel website. It is important to understand the consumers acceptance of the online concept and the product and services provided are more suitable to be selling online. Hence, travel agency will have more relevant information or idea when creating the website. Besides, its also helping those agencies that have an intention to invest in online store, so, they have a better idea of the trends in the market and thus they can increase their portion of the market place. Last but not least, travel agency can save more cost and time. For instance, small tourism enterprises wont be facing any risk in spending on costly web-based solutions and time with the help of this study by providing them useful information about the preference of the internet users. 1.5 Chapter Layout 1.5.1 Chapter One: Research Overview This research report comprises a total of five chapters. In chapter one, researcher furnish an overview of the study and describing the research situation. It has covered background of this study, a brief introduction of our research, statement of the problem, objectives of the study, hypothesis of the study and significance of the study. 1.5.2 Chapter Two: Literature Review In chapter two, it includes the review of the literature, review of relevant theoretical models. Furthermore, it also consists of proposed theoretical framework and the hypotheses development. 1.5.3 Chapter Three: Methodology For chapter three, it is an overview of the studys research methodology. This chapter includes our research design, data collection methods, sampling design, research instrument, measurement construction, data processing, as well as data analysis. 1.5.4 Chapter Four: Data Analysis In chapter four, it presents the research results and the analysis of the results which are relevant to the research questions and hypotheses. This chapter includes descriptive analysis, scale measurement and inferential analyses. 1.5.5 Chapter Five: Discussion, Conclusion and Implications In chapter five, it is the summarizing chapter that discusses the overall findings followed by the conclusion. Thus, this chapter finds out the implication and limitation of the study. Finally, this chapter outlines the recommendations for future researches.

The Role of the Supreme Court in the Civil Rights Movement Essay

The Supreme Court was important in both suppressing and aiding the Civil Rights Movement. However, decisions taken by the President, the continued white opposition and improvements in media communications also had an effect. Although all were important, the Civil Rights movement alone would have reached the same end without the help of the Supreme Court, and the devotion of its many members and leaders is the major factor in advancing Civil Rights. The Supreme Court is perhaps most well known for the Brown vs. Board of Education decision in 1954. By declaring that segregation in schools was unconstitutional, Kevern Verney says a ‘direct reversal of the Plessy †¦ ruling’1 58 years earlier was affected. It was Plessy which gave southern states the authority to continue persecuting African-Americans for the next sixty years. The first positive aspect of Brown was was the actual integration of white and black students in schools. Unfortunately, this was not carried out to a suitable degree, with many local authorities feeling no obligation to change the status quo. The Supreme Court did issue a second ruling, the so called Brown 2, in 1955. This forwarded the idea that integration should proceed 'with all deliberate speed', but James T. Patterson tells us even by 1964 ‘only an estimated 1.2% of black children ... attended public schools with white children’2. This demonstrates that, although the Supr eme Court was working for Civil Rights, it was still unable to force change. Rathbone agrees, saying the Supreme Court ‘did not do enough to ensure compliance’3. However, Patterson goes on to say that ‘the case did have some impact’4. He explains how the ruling, although often ignored, acted ‘relatively quickly in most of the boarder s... ...day .23 Mark Rathbone, The US Supreme Court and Civil Rights, History Today .24 James T. Patterson, The Troubled Legacy of Brown v. Board, p. 10 .25 Mark Rathbone, The US Supreme Court and Civil Rights, History Today26 The Troubled Legacy of Brown v. Board, James T. Patterson, p. 6.27 Martha Gellhorn, Justice at Night, The Spectator 193628 Douglas A. Blackmon, Slavery by Another Name, p.729 Paterson and Willoughby, Civil Rights in the USA, 1863-1980, p.200.30 Douglas A. Blackmon, Slavery by Another Name, p.53.31 Mark Rathbone, 20th Century History Review, The US Presidency.32 Mark Rathbone, 20th Century History Review, The US Presidency.33 Clive Webb, Modern History Review, The Ku Klux Klan.34 Clive Webb, Modern History Review, The Ku Klux Klan.35 John A. Kirk, History Toady volume 52 issue 2, The Long Road to Equality for African-Americans

Tension and Atmosphere in The Red Room by H.G.Wells, The Signalman by C

Tension and Atmosphere in "The Red Room" by H.G.Wells, "The Signalman" by Charles Dickens and "A Withered Arm" by Thomas Hardy To investigate tension and atmosphere, I have looked at three pre1900 pieces- 'The Red Room' H.G.Wells, 'The Signalman,' Charles Dickens, & 'A Withered Arm' Thomas Hardy. They use a variety of different techniques, each with their own individual style but achieving the same overall effect. They focus on setting, description of characters & use of language. The Red Room is a tale of a man on a quest to discover the truth about the legend of 'The Red Room' in Lorraine Castle, as the young man's fate unfolds the audience are led with him, they feel his fear, hear his thoughts and experience his terror. 'The Red Room' has such a mystery behind it, fear itself nearly leads him to his death. A tale that lacks warmth and everything about it instils terror. The title of the story has a suggestive air, the word red makes the audience think of blood, danger, and death, in 'The Signal Man,' red is also the main focus colour for the same reasons but this time in the form of the danger light in the mouth of the tunnel. H.G. Wells writes in the first person so the audience can follow what is happening and believe they are there, 'I have lived' The opening line sets the tone of the story, and the audience is filled with anticipation. The narrator is very confident, which is displayed almost immediately 'I can assure you, it will take a very tangible ghost to frighten me.' The audience is then effectively led into an early assumption that the narrator will be proved wrong, that there will be a ghost, and it will, most certainly frighten him. The setting is described in clues hidden thr... ...oy it more. Therefore this raises questions in the audiences mind, if only he had gone straight there, he may of saved the poor man's life, if only, he had believed him. Finally, that it now looked incredibly doubtful that it was coincidence what the signalman had 'imagined', the words, gesture and even the appearance of the train driver that had 'cut him down'. Pity is also a great factor in the conclusion; it makes the story have a more memorable and sad atmosphere. Finally the way that the narrator, whom had written the story after it had happened, finished of by giving the audience yet more to think about, reminding us of its obscurities, unbelievable coincidences and in turn reinforcing the mournful atmosphere. 'close at the mouth of the tunnel, I saw the appearance of a man, with his left sleeve across his eyes, passionately waving his right arm.'

Microeconomics Market Structures

According to the principles of microeconomics market structures can be identified as perfect competition, oligopoly or monopoly. In our society today and the way business is conducted, market structures are not strictly defined by on of these particular types. They can be composed of a mix of them. A market structure that has a higher level of competition can be more efficient than those that have lower levels of competition. We know this since lower competition increases the producer’s surplus; in return it decreases the consumer surplus.The loss in the consumer’s surplus is means it will be greater than the increase in the producer’s surplus. This leads to what we have learned as a deadweight loss. A perfect competitive market can also be thought as the most efficient form of market, where consumers are the most beneficial. Realistically it is difficult to identify and choose a perfect competitive market. Perfect competitive markets have the following character istics: * No entry or exit barriers: These markets should have their structures set up with no entry or exit barriers.Simply meaning that new suppliers can invest in their company without any significant capitol or risk. They can also exit the industry without facing any significant loss. *Infinite buyers and sellers: the markets have unlimited or infinite amounts of producers that are willing to sell their products to an unlimited or infinite amount of buyers. This is to ensure that there is no supplier or buyer has a significant market share. This is to prevent others in the market from being able to determine the market price. Perfect Information: This is when all competitors in the market have to provide and share equal information among/between each other. Suppliers and buyers alike, go forward and profit from having full and complete information, just like the other suppliers and buyers in the markets. The above conditions benefit consumers as these firms develop and become pr ice takers. The price of the product will be decided by the supply and demand. The equilibrium point is where the supply curve and demand curve intersect and establish the market price of the product.Therefore, in all aspects of business the price is equal to marginal cost. If we look at the short-term affects then there is the possibility for a firm to make a profit. However, this is not the traditional outcome and will quickly attract other firms in the industry. In turn it will elevate the level of supply demands. As a result of this increase, the products price will decrease. Simply meaning that in a perfect competitive market it is not feasible for a firm to profit in a long-term aspect.This doesn’t necessarily mean that a firm will not earn any profits. It means that the profit earned covers all the costs to produce the goods. This can include interest and liability. In turn, the long-term outcome for the firm in a perfect competitive market will earn a normal profit. T he profit will always be significantly less then firms in a monopoly or oligopoly would earn. Capitalism is based on the principles of the free market. In free markets the supply and demand variables help determine the quantity of the goods produced along with its market price.There are many foreign countries and their firms that have tried to implement free markets but have not been very successful because there has never been that type of market in international transactions. This is because global trade and national trade are derived from the same principles. The foundation of international trade is the principle of comparative advantage. Each country has different sets of resources. These variables are different because some countries have large populations, which influence the labor pool, especially when some people have specific developed skills in a one industry.For example, if people in Antarctica wanted to drink coffee that would have to or import them since the climate is not conducive to growing it. If Antarctica decided not to use international trade, they would either have to go without coffee or grow it in an un-natural environment. The cost of growing and providing coffee would increase the market price. Instead since Antarctica participates in international trade, they have the ability to focus and provided coffee to its countries consumers. Simply meaning that countries that have comparative advantages get it from their products for which has the lowest opportunity cost.If these countries focus on products that create their comparative advantages then the total output would be greater for the consumers of the world than if each country tried to individually produce everything. The marginal cost or the cost of producing a new product usually cost less than the average cost of producing a good. This is because the additional production usually is achieved without making any substantial new investments. (V,Kumar, 2008). Globalization allows produ ction to shift to the people and places that are in a position to produce the best products at the most competitive price (V,Kumar, 2008).When the total supply demanded is more, the consumers will benefit since the price will be lower. Simply meaning that in the absence of international trade, consumers around the world ended up having fewer suppliers who could provide the necessary good to produce their products and meet their demands. Ultimately letting the supplier controls the market and its price. If we consider an assignment we have earlier in this class, let us consider the automobile industry of the 1950’s and the present industry. In the 1950’s there were only three companies who produced and sold cars in the United States.Based on what we learned in this class it is defined as an oligopoly. The automobile industry was a highly profitable industry in the 1950’s. When international cars were imported in to the United States, they were competitively price d much lower than the cars produced on home soil, and the imported goods had better quality with more fuel efficiency. In turn consumers quickly shifted their interests and money to the imported vehicles. This caused local producers to be forced to lower their prices and improve their standards in order to stay competitive.International trade brought more options to consumers. It’s providing higher levels of competition and improved quality and efficiency. With free international trade, producers can provide and compete for customers across the world. Consumer surplus is defined as, â€Å"An economic measure of consumer satisfaction, which is calculated by analyzing the difference between what consumers are willing to pay for a good or service relative  to  its  market price. A consumer surplus occurs when the consumer is willing to pay more for a given product than  the current market price† (Investopedia: dictionary).When the number of suppliers in the indust ry increases it leads to higher levels of competition, and these market conditions found in a perfect competitive market end up being more profitable to consumers. Social cost are one of the variables that lack in all studies of economy however, as our society advances, so do social costs and this is reflecting in the markets. We have learned a lot about comparing the cost of a product and revenue generated from it In turn if we take into account the growing effect of social costs and the social benefits that can emerge from this trend.We cannot forget that there are also marginal social costs. The marginal social cost becomes greater with each additional thing that happens that is considered undesirable or also knows as un-anticipated cost activity. For example, an oil company that is drilling must extend its hours on Friday to reach the minimal amount of oil harvested. This will mean there will be a marginal cost for each additional hour the oilrig must operate to reach its quota. Marginal social cost would be the upward slope that shows the cost increasing with each additional output.Marginal benefits would be the opposite as marginal social cost with a downward slope. Environmental factors show the relationship connections between marginal social cost and marginal social benefit. Lets assume that all firms are constantly trying to reduce the level of pollution that they create. This is not an over night process and it does require a significant amount of capitol. This would affect the marginal benefit. Now on a different hand lets assume that a company has been cited for not following the regulations that govern company’s pollution control. This company is now required to get up to code.They are going to have to buy the new equipment that is required. This will inflate company’s costs and may affect the company’s ability to produce their products competitively. This could create many problems for the company but will ensure they are fo llowing required rules. Since it is for the good of society it means that the marginal social cost is beneficial than the marginal social benefit. Proving that marginal social cost and marginal social benefit lines cross where the two are equal. This class have class has taught many aspects and principles of microeconomics.It began with supply and demand and ended with marginal costs and benefits. All of the principles affect local and international firms, as well as consumers that are in both places. These studies will continue to evolve as business and society moves forward in the future.Work Cited * V,Kumar, VK. (2008, July 28). Globalization:who benefits from it. Retrieved from http://www. helium. com/items/398030-globalization-who-benefits-from-it * Investopedia: dictionary. (n. d. ). Retrieved from http://www. investopedia. com/terms/c/consumer_surplus. asp

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

Computers help in agriculture?

Agriculture Computers help in agriculture? It can't sow seeds or harvest crops or irrigate fields. But it can definitely help the farmers and the agricultural scientist in various stages of farming. And why haven't we used computers in agriculture till date? Just one reason, farmers are not comfortable with computers. And since there's no market for such a technology, the industry never bothered to consider that field for making any software.Countries like USA and Australia use software in their day to day farming activity. It helps them choose the right crop for their field, track the growth and accounting after harvesting. Clearly such technology is useless for Indian agriculture scenario because most farmers in India cannot effectively use it and only a few would be willing to invest in such a tool. So can computer technology really help Indian or other countries agriculture? And my answer is a big yes.It has a bigger role to play in Indian agriculture than in any other countries. Indian agriculture systems have a symbiosis between farmers and agriculture scientists. Scientists need input from farmers about their experience, to come up with new strategies for farmers. If we can somehow avail all the necessary data to scientist, they can for sure come up with better strategies. And if we can aid in their strategy planning by providing tools to do that, scientists will be able to compare different strategies.Having leveraged the scientists to come up various farming strategies, the next obvious step is to enforce these strategies through farmers by letting them choose the one which suits their land the best. Imagine software, which can predict the soil fertility of a land after harvesting a particular crop, provided we have the soil fertility information of the land and the crop to be harvested now. Well if you can predict the fertility of the soil after a particular crop harvest, it lets you decide what's the other crops can be cultivated later and helps deci de the best crop rotation policy.This would guarantee the highest yield all the time. We are not in an ideal world where everyone is a computer wizard. In such a non-ideal world, what good would it bring if given to farmers? How many farmers are out there who can efficiently use such a tool? How to make software that is so easy to use even for farmers? On the other hand, if we give this tool to scientist or government officials, they can help farmers choose the best crop for their lands.

Renewable Energy Research Proposal Example | Topics and Well Written Essays - 750 words

Renewable Energy - Research Proposal Example The trend of FDI is common among developed countries of the world including the United States. In the US, technologies have been developed and utilized efficiently to meet the needs of renewable energy in the country (Nations, 2010). However, with the increasing population and their demands of daily use for home, businesses and transport, it has become important to secure basis for the USs future energy needs and so the need of FDI in the energy sector. In an official report by OCO Global (2012), it was stated that the United State is facing challenges and lack of opportunities in terms of Foreign Direct Investment. Though, during the government of former president Bill Clinton it was noticed that $1 billion were invested in coal plant and a great many improvements had been made in solar and wind turbines. It is evident from the fact, at that time 1900, jobs were opened in solar energy sector and 3,300 jobs were offered in wind turbine sectors of energy industry. FDI market reports have revealed that there is an exceptional form of political and social force that prop up the development and evolution of renewable energy sources. On the other hand, it has been evaluated by the researchers that political leaders of the US and other countries as well emphasize on clearer and more sustainable sources of renewable energy that can help the country to fulfill future needs of the population (Energy Politics, 2012). AEO (2012) report discusses that previous and present both governments has always targeted to have set goals, when it comes to alternatives for renewable energy sources. Likewise, in the United States the New Energy for American program has a 25 percent target for coming years particularly focusing 2025. Payne and Yu, (2011) report discusses that an increasing flow of FDI in the United State can potentially accelerate the economic growth and