Recently, organizations are focusing more on corporates diversity to reinforce organizational adaptability and encourage competitive advantage. However, a debate has arisen that tackles whether diversity is a variable that has an immediate effect on success or not. The advancement in technology and the easiness of travel in the 21st century have caused a fundamental change in workplace dynamics. Cross-border trade and investment barriers have been eradicated by the advancement in telecommunication and transportation.

The breaking of the transportation and telecommunication barriers has consequently diversified the workforce in various organizations. According to [1] , workforce diversity refers to similarities and differences that occur among a company’s employees or a group of people working together to achieve a common goal. The similarities and difference are in terms of race, cultural background, age, physical abilities, physical disabilities, gender, sexual orientation, and religion. Diversity is known to make a working environment heterogeneous. Various researches have been done to establish the effect of diversity on the success of a company or a group of people working together towards a common goal.

One of the ways that organizations seek to improve their performance is through diversification of workforce. In recent years, many organizations and companies have implemented diversity initiatives that are beyond the traditional monolithic structure, compliance standards and affirmative action. Employers seek to employ competitive workforce regardless of age, gender, race, ethnicity, religion, language, perception, and attitude. As the global economy continues to expand, business leaders have learned the value of a multi-cultural workforce with regard to different aspects of their businesses. There is a greater understanding of diversity as a competitive edge when leveraged. Thus, the need for it to be accounted for in the workforce equation for continued development. Advantages of diversity in work place [2] :

• Improves approachability to new and diverse customer marketplaces.

• Increases innovation and productivity.

• Increases revenue.

• Leads to the development of new products and services.

• Allows greater flexibility and adaptability in a more globalized environment.

• And improves social cohesion.

1) To investigate impact of ethnic diversity on the success of a movie in terms of movie ratings and Box Office earnings.

2) To investigate the impact of ethnic diversity of the Board of Directors of Fortune 500 companies on the success of the company in terms of net sales and gross profit.

Since the research is carried out on two independent data set (movie dataset and Fortune 500 company’s dataset), there are to null hypothesis and two alternative hypotheses for each dataset.

• Null hypotheses

1) H01 Ethnic diversity of movie writers and directors has no impact on the success of the movie.

2) H02 Ethnic diversity of Board of Directors of Fortune 500 Company has no impact on the net sales and gross profit.

• Alternative Hypotheses

1) Ethnic diversity directly affects the success of a movie.

2) Ethnic diversity of the board of directors has an impact on the net sales and gross profit.

The study focused on the effect of ethnic diversity to the success of a movie in terms of movie ratings and box office earnings and, the effect of ethnic diversity on the BOD of a company and the success of the company in terms of net sales and net profits. In the research, movie data from The Internet Movie Database (IMDB) and Rotten Tomatoes database were used. Also, data of Fortune 500 companies were used. The Internet Movie Database is a movie repository that store comprehensive data about movies, scriptwriters, movie directors, movie release dates among other relevant details. The IMDB repositories stores more than 900,000 movie titles. Furthermore, the site data contained more than 2.3 million individuals. Also, data contains details of scriptwriters, movie directors, movie producers, movie reviewers among others. The in-depth storage of data in the Internet Movie Database makes it a rich source of information for various analyses. Figure 1 displays the most common genres provided by IMDB Website and the number of movies for each genre within the dataset.

Rotten Tomatoes is a repository for movie data as well as movie review aggregation. The Rotten Tomatoes provides a platform whereby movies can be revised by professional movie reviews, movie critics, and the audience. The website uses a special algorithm to combine the reviews into a single aggregation referred to as the Tomatometer. The Tomatometer is trusted by millions of people around the world in determining whether to watch a movie not based on the review.

The research further utilizes data from Fortune 500 companies, Fortune 500 companies refer to a list that is compiled annually by the Fortune magazine. The list ranks the top 500 companies in the United States of America based on the total revenue per given fiscal year. The data provided by the Fortune magazine includes details about the Chief Executive officers of the company, the list of the Members of Board of Directors, Net profits, and Net sales among other important information. The dataset from the repository mentioned above is collected and analyzed in python using important python libraries.

There are several variables that can be used to measure workplace diversity. The most important ones include the following:

1) Age Diversity

Age is a generational difference between employees of an organization. Growing age diversity is increasingly becoming part of quite a number of business organizations. According to Kunze [9] , the social identity and categorization theory may be used to understand this relationship. According to this theory, it is suggested that individuals tent to classify themselves based on dimensions that seem relevant to them. As a result, individuals tend to favor employees of their own group, and discriminate employees from other age groups. Thus, the employees generational belonging is an important criterion for distinction that may stir emotional conflict at workplace.

2) Gender Diversity

Ali defines gender diversity as psychological and experienced disparities that are culturally or socially attached to being of a male or a female.

3) Ethnic Diversity

An ethnic group refers to a group of people with a sense of common origin, and most often, a sense of common destiny. Pitts [10] argue that as firms impress ethnic diversification, there is need to pay more attention on how different ethnic groups interact with each other at workplace. Hoogendoorn Van Praag [11] defines ethnic diversity as the heterogeneity in races, languages, and cultures among employees of an organization.

Workforce diversity has an influence on both employee and organizational performance, and consequently, on the organizational performance. This means that a positive effect of workplace diversity at employee level will have a positive effect at organizational level, and vice versa.

1) Age Diversity

Most often, organizations avoid utilizing the expertise of old employees because of stereotypes and false assumptions that they are slow in adopting to changes and new technologies, prone to health problems, poor performance, and expensive compared to the older generation [12] . According to a study done by Hamilton Nickerson [13] , on simple production technology, it was found that work-forces with age diversity were less productive. A similar finding is reported by Leonard Levine [14] , where they indicated that retail store businesses that had age diversity among employees were slow in making profits. In another study by Ilmarinen [15] , it was reported that there was no relationship between employees age and work performance. Many studies have shown that older employees perform work-related tasks as effective and efficient as younger employees. According to Williams OReilly [16] , having a heterogenous age employee team is more productive than having an employee team with a homogenous age.

2) Gender Diversity

Mixed gender team of employees performs better compared to a team of employees of the same gender [17] . In studies carried out by [18] , it was evident that there was a positive relationship between organizational performance and gender diversity, based on the organization resources. Many other studies have found a negative effect of gender diversity on team performance in male dominated samples, and insignificant effects in female dominated samples [19] . According to Samuel [20] , the organizations competitive advantage increases when gender diversity is at a moderate level, while a greater level of gender diversity reduces organizational performance. The study results obtained by [21] , showed an inverted U-shaped connection between employees’ gender and organizational performance. In a similar study conducted [21] , it was found that there was an inverted U-shaped relationship between gender mix and employee productivity. The research also found that moderately heterogenous teams demonstrated better performance compared to gender homogenous teams. Gender diversity contributed positively to the services industry, while in the manufacturing industry, it had negative effects. Therefore, gender diversity in service industry workforce might bring a positive impact compared to companies in the manufacturing industry.

3) Ethnic Diversity

Jackson, et al. [14] studied the effect of gender diversity on performance. In his studies, he found that ethnically diverse teams of employees exhibited poor performance compared to homogenous teams. A close study by Jones [22] , also demonstrated that ethnic groups were less cohesive compared to teams. Thus, ethnic diversity is likely to have a less positive impact on group performance when compared to team performance. An ethnically diverse team of employees possess high creativity and innovation that comes with learning opportunities. According to Sander Hoogendoorn [12] , ethnic diversity at moderate level has no impact on organizational performance. Samuel [20] , also reported a positive effect of ethnic diversity on innovation, productivity, market share, and sales. Jones [22] investigated the effect of ethnic diversity in the Oil Gas Industry. The researchers reported a positive relation between ethnic diversity and team performance. In another study conducted by Jones [22] , it was found that there was no relationship between ethnic diversity and sales productivity, revenue, and customer satisfaction.

This study was conducted by choosing two major film websites (Rotten Tomatoes), the reasons in which these websites were chosen due to the fact that these are the essential websites that provides up to date movies ratings and have massive amount of data. Other websites provide datasets that are not up to date or include missing data.

Two main movie websites were chosen as a source for data gathering; which were imdb and rotten tomatoes websites. The objective of the study was to analyze the rotten tomatoes data to determine the relationship between diversity in terms of movie writers, movie directors and the success of a movie in terms of earnings. To carry out the analysis, 14,235 movie sets were scrapped from Rotten Tomatoes website. The data contained 29 variables each describing a varied aspect of the movie. The analysis was done using Python programming language that required use of various libraries to ease the analysis process. The list of libraries used in the analysis includes Numpy, pandas, Scikit-lean, OS, and SYS. The analysis was carried out in Jupyter Notebook. Table 1 demonstrates variables of the imdb dataset and description for each of the variable studied while Figure 2 shows distribution of average rating.

Before starting the analysis, it was paramount that the data were cleaned for the analysis. The preparation of the data entailed carrying out the following process;

1) Import the required libraries (Pandas, Numpy, OS, Scikit-learn and OS).

2) Loading all the files using the imported libraries.

3) Merging the data in the files into a single dataset.

Implementation.

After setting up the environment, firstly, the dataset was loaded, the Rotten Tomatoes movies dataset and IMDB movies dataset in the Jupyter environment.

The next task was merging the datasets to find the number of movies which were common in both datasets, an inner join on Title column was used to merge the two datasets. It was found that the number of movies which were common in both datasets were 8553. It was also concluded that in IMDB dataset, Title and Original Title column have same number of unique observations. There were 107,645 distinct movies in IMDB Dataset and 13,904 movies in Rotten Tomatoes Dataset.

After finding the number of movies which are similar in both datasets, in terms of critics score and audience score and revenues. The Tomatometer rating which was discussed above is based on the published opinions of hundreds of film and television critics—is a trusted measurement of movie and TV programming quality for millions of moviegoers and it can also be observed that Tomatometer rating is greater than the audience ratings which shows that critics rated the movie in more positive manner as compared to the audiences. It can be observed that most of the movies are drama and comedy. Also, for 13,367 movies the genre is not recognized. Next, the number of movies or programmes of various title types present in the dataset was analyzed. And finally, for the IMDB Dataset, the distribution was also analyzed in terms of runtimes in minutes and found that most of the movies were of 90 minutes in runtime.

In a similar manner, what was also analyzed was the scatter plots among the audience/critics score and the overall rank of the movie in the two scatterplots. But, here in this case, we can observe that Audience Score is highly correlated to the Rank of the movie as compared to the Critic Score. Figure 3 demonstrates the various numeric variables. And Figure 4 exhibits a correlation matrix heat map.

Statistical tests were also performed to understand how these variables are dependent on each other and how significantly they differ from each other. The results shown below are the Pearson’s coefficient of Correlation which has been already discussed above in the scatter plots. One of the measures used to establish if there is a relationship between two variables is the Pearson product-moment correlation coefficient normally referred to as Pearson’s coefficient.

The Pearson’s coefficient measures the strength of the linear relationship between two variables. The formula for calculating the Pearson’s coefficient is as shown below:

1 − λ = 1 − ∑ i = 1 R ρ i 2 = 1 − 1 2 D

The value of Pearson product-moment correlation coefficient ranges between −1 and 1. A strong positive correlation tends to lean towards positive one while a strong negative correlation tends to lean towards negative one. If the value of Pearson product-moment correlation coefficient is zero, there is an indication that there is no correlation between the variables under study. There are various ways in which the value of the Pearson product-moment correlation coefficient can be calculated. One of the most common method entails plotting a scatter plot and then establishing a line of best fit through the plotted data points. Pearson product-moment correlation coefficient is then used to indicate how far the plotted points are from the best line of fit. In this project, the Pearson product-moment correlation coefficient was determined using the Numpy library using the corref() function.

r = ∑ i = 1 n ( x i − x ¯ ) ( y i − y ¯ ) ∑ i = 1 n ( x i − x ¯ ) 2 ∑ i = 1 n ( y i − y ¯ ) 2

Data about the Board of Directors of Fortune 500 companies was collected. The data was contained in two CSV files. One file contained data about the details of the Board of directors, while the other file contained information about net sales and gross profit. To have an overview of the data, the head () fiction was used. Furthermore, the data about the company was grouped as either top gainers or top losers. The following table shows a snippet of the Board of directors’ top gainers data and top loser data. Table 2 shows Fortune’s Less Profitable Companies Diversity Score.

The data of the top losers and the top gainers company were merged with the data of the net sales and gross sales of the respective companies. Using the data of the board of directors of each of the fortune 500 companies, the ethnic diversity score was calculated. After the calculation, a histogram was plotted to visualize how diverse the board of directors are in the company basing on the earlier calculated ethnic diversity score. Figure 5 shows a plot for Ratings versus Diversity for Writers.

Figure 6 shows Box Office Versus Diversity Score for Writers. The movie writers have varied diversity, consisting of Hispanics, Blacks, and whites, among others. It is thus important to establish how the diverse the movie writers are according to the Rotten Tomatoes data, it can be seen that the diversity among the writers is normally distributed save the maximum number of observations. Furthermore, the chart shows that most of the movies have writer ethnic diversity score that is greater than 0.6 and less than 0.8.

It can be seen that the diversity score with the highest distribution is between 0.68 and 0.70. Thus, it can be concluded that the distribution of diversity among the movie directors is relatively normal.

Figure 7 shows the Rank Versus Audience Rating and Audience Score. To visualize how diversity among the writers relates the diversity movie ratings, a regression joint plot was drawn. From the regression chart, it can be seen that the Pearson product moment correlation coefficient is 0.00017 (the Pearson correlation product moment coefficient is approximately equal to zero). Since the value of r is almost close to zero, thus be concluded that there is no relationship between the diversity among the movie writers and he movie rating.

Analysis was also done to establish if there is a relationship between writer’s diversity score and the movies box office earnings. From the regression chart above, it can be seen that the Pearson’s correlation coefficient, r, is equal to 0.11. The value of r is significant and thus, it can be concluded that there is a weak positive relationship between movie diversity and box office earrings. The relationship is such that as the diversity score increases, the box office earnings increase.