Monthly Archives: December 2017

Data Analytics: Influences of Gross Film Revenue Across Three Decades

 

Data Analytics: Influences of Gross Film Revenue & Opportunity Analysis

December 6, 2017

Todd Benschneider, Austin Deno, Leigh Harris, Sarah Lassiter, Lisa Velesko
Table of Contents

Problem Significance:                                                                                                         3-4

Data Source & Preparation:                                                                                               4-5

Variable Selection:                                                                                                              5

Preliminary Analysis:                                                                                                          6-8

Models:                                                                                                                                  8-12

Insights:                                                                                                                                 12-13


Problem Significance:

Several societal trends can be mined from the data captured in consumer spending patterns of the film industry, especially a comparison of different genres of films which indicate rising and falling patterns of popular fiction. Films, more so than television, literature, or music, closely correlate with upcoming trends by using a responsive pull towards consumer tastes in fiction-fantasy and most accurately reflects the psyche of a generation and its ever shifting emotional underpinnings. The nimble demand responsiveness of filmmakers has become astoundingly proficient at catering to the emotional voids that drive the fiction market and are reflected with clarity in the ever changing mix of successful films. Through the unspoken demand for clearly defined types of storylines, these quickly produced films reveal a meaningful cross-section of a society’s unfulfilled drives and highlights which particular aspects that a society’s members yearn for in their own life situations.

In addition to trending popularity of varying scripts, other valuable economic indicators can be harvested through reverse engineering techniques to capture the downward trending genres that clarify the contextual changes that indicate which previous underlying drives have since been fulfilled through sociological evolution. Marketing professionals are wise to take note of the peaking decline of each passing trend, as those peaks and valleys encapsulate at a macro level of measure, the unspoken barometers reflected in the overall mood of a culture.

In the industries of entertainment and media, consumer spending directed towards different types of fiction produces great insight into the long-term patterns of emotional and economic wants, that are as useful to producers of consumer goods, as they are to providers of entertainment. It is imperative for businesses to be on the forefront of any trend.

Our data set summarizes three decades of consumer spending trends on tales that potentially reveals early predictors of future spending behaviors. It is through the trend forecasting of these patterns of film revenue data, that a business can be on the forefront of meeting changing consumer tastes, whether that firm creates new movie plots, automobiles, or widgets. With insight into the deepest desires of the society around it, a business can tailor its marketing message to align its product with a representative cross-section of every consumers vision, of not who they are, but instead, what they want to be. Few other data sources can provide the insights into the self-identity of fantasy characters as well as film plots and with this three decade dataset, we expect to gauge the tipping points of long term trends and witness the rebounds that those tipping points predicted.

Our team viewed the movie revenue data from the perspective of a movie merchandiser, evaluating which unreleased movies in production would provide our firm with its highest return on investment for movie-themed posters, toys, clothing and related merchandise. The highest budget films command the highest royalty percentages and also require the greatest undiversified commitment of our manufacturing lines to individual movie projects. Because of the risk and profitability factors affiliated with marketing the high budget prospects, our team instead drilled down into the data looking for the more cost effective prospects. Films that maximize the return on investment allow our firm to utilize a more diversified portfolio of projects with more promising cash flows.

With this goal in mind, we chose instead, to use regression models to dig deeper into other categorical data from the set, hoping to find other actionable predictors that could be valuable on a shorter time-line. With that goal, we evaluated the given variables in search of the most significant predictors to cinematic success to determine the confidence of future investments.

Data Source & Preparation:

The data set was originally gathered from IMDb and then sourced directly from Kaggle using 6,820 movies from 1986 to 2016 and includes details such as budget, gross revenue, the production company, country of origin, director, primary genre, movie name, motion picture rating, date released, runtime, IMBd user score, lead star, IMBd user votes, writer, and year released.

Not all movies contained information regarding the budget of the movie.  Those were removed as it was critical in our analysis to be able to collate the relationships for complete data points, especially in regards to budget.  We also investigated the relationship between profit and return on investment between gross and budget independently.

Tableau and Excel were first used to identify the greatest amounts in each respective variable.  This allowed us to postulate our first level of filtering.  R was then used to plot data using histograms, box plots, and scatter plots to consider outliers, run regression models, multicollinearity and direct correlations, identify R-squared and adjusted R-squared, along with Aikaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), to determine goodness of fit, utilized numeric and qualitative predictors, and with interaction.  Charts in Tableau were generated to visually verify the interaction effects.  Tableau, Excel, and R were all used collectively to ultimately determine the strongest correlation, interaction, numeric, and qualitative predictors in using the variables.

Variable Selection:

Response Variable: In our effort to uncover the driving forces behind blockbuster films, we questioned what causes box office achievement. There are far too many flops in show-business; artistic potential is drowned out, consumer trends are completely misinterpreted, and lucrative investments are wasted. We must review success in cinema and provide a supportive study to investors in major motion pictures to appease the masses and create a stable platform for performers, thereby providing a concrete analysis of how gross revenue is determined. We therefore selected “Gross”, defined by our IMDb source as “gross revenue at the box office” as our response variable for all data modeling in this study.


Predictor Variables:
In order to evaluate the best variables to test against our response variable, we created a correlation table (below) to test the relationship amongst the quantitative variables. We focused on which variables could have a strong effect in deciding gross. The motive in tracking down the most determinant variables is so the investor can later account these factors into their decision to support a film.

Correlation Chart Budget Gross Runtime Score Votes
Budget 1 0.680033 0.313064 0.073579 0.451467
Gross 0.680033 1 0.253273 0.229552 0.642904
Runtime 0.313064 0.253273 1 0.417031 0.359817
Score 0.073579 0.229552 0.417031 1 0.470648
Votes 0.451467 0.642904 0.359817 0.470648 1

To no surprise, the correlation that stood out the most was between gross revenue and budget with .68003256. This correlation suggests that a higher budget movie will most likely fund a movie that generates more revenue. As we believe budget is the heaviest deciding factor in funding the crucial elements for a financially successful film, we regard it as our primary predictor variable which our other qualitative and quantitative variables will be matched against.

 

The second highest correlation was found between “gross” revenue and “votes” (that is IMBd viewer reviews on a scale from 1 to 10) at .642904. We can justify this correlation two-fold. First, more “votes” logically means more tickets were purchased to watch the movie in theaters. Second, a high number of votes can drive consumer demand, influencing movie-goers who have not yet viewed the film to either watch or avoid depending on how positive the review was. While our first conclusion is provided after the fact of viewership, the second has the potential to boost viewership, making this variable causal. However, since we cannot account for whether “votes” were causal or coincidental, and since the standard error in a simple regression with gross is very large, we decided not to make it a popular predictor variable in our study. Derived from the votes, we deemed “scores” as unacceptable variables in our models because we cannot control the scores that are given by the reviewers.

 

As “runtime”, the final quantitative variable which refers to the length of the film expressed in minutes, has a relatively moderate correlation with “gross” at positive .2532733, we must take into consideration what this logically means. The correlation expressed as runtime increases, gross revenue also increases. We know that this statement has a limit because if movies were formatted into countless of hours, we cannot logically expect the popularity to rise accordingly. In support we also can see from a simple regression that, like the “votes” variable, “runtime” standard error at 52262 is unacceptably high.

 

As far as qualitative data, we opted to use both primary “genre” and motion picture “rating” as major predictors of gross, as supported by their high multiple r-squared values. We determined these were likely predictors of movie success based on consumer taste.

 

Finally, we decided not to use the production company, country of origin, director, movie name, date released, and year released as these factors would be completely out of control of the film investors. This is due to the variables being too widely diverse to classify accurately since they are spread so thinly across the data.

Preliminary Analysis:

Following our variable selection, we began looking at patterns surrounding the relationships between revenue and movie genre and motion picture rating. It’s important for investors to stay current on consumer trends in order to predict where the big money will be made in the film industry.
Hypothesis Testing:

 

Hypothesis 1:

  1. Since the Action genre and PG-13 rating have the highest gross revenue out of all movies, it is logical to assume that these types will also generate the highest return on an investor’s funding once the production hits the theaters. We have solid evidence that this is true because budget accounts for over 47% of the prediction of a high grossing movie.

H0: Action genre and PG-13 rating have the highest return on investment and an Action PG-13 rated movie will generate the most dollars per dollars invested.
Ha: Action genre and PG-13 rating do not have the highest return on investment.

Genre:

After realizing high correlations between gross and motion picture genre, we dove into separating genres to see which classifications raked in the most at the box office. We found that the movies with the highest gross revenue were Action with
a combined total of over $708 million. By seemingly no coincidence we also noticed that Action movies
had a higher total budget than all other genres. Since budget has a strong linear correlation with gross, we can assume that Action will produce the highest return on investment than any other genre.
Rating:

We similarly compared motion picture ratings to gross revenue to identify that PG-13, R, and PG, respectively, generated the most revenue over the course of the 30 year history and looked at the gross revenue and budget within each sector.


Hypothesis 2: Since popular actors have a strong influence over consumer taste, we can assume that starpower has a significant effect on gross revenue. Since high budget is needed we can also assume that as budget increases, more coveted actors can be casted, resulting in a very popular, high grossing film.

 

H0: Movies with budgets in the upper 3rd quartile will have a significant relationship between star and gross.
H1: Movies with budgets in the upper 3rd quartile will have no relationship between star and gross.

 

Star: We attempted to identify the correlation of stars to gross revenue by exploring the total number of movies that they been the lead in and the sum of the gross revenue for those movies using Tableau.  We believed that particular stars would impact the budget and also impact the gross revenue.  Frequency of a star being in movies could also lead to their popularity and consequently generate more box office revenue as consumer-demand increased to see that star.  In running a regression model, there were specific stars, such as Chris Pratt(1), Daisy Ridley(1), Ellen DeGeneres(1), Felicity Jones(2), Heather Donahue(1), Jennifer Lawrence(8), Louis C.K(1), Neel Sethi(1), Paige O-Hara(1), Quinton Aaron(1), Sam Neill(3), Sam Worthington(4), Scott Weinger(1), and Taylor Kitsch(1) that had significant influence as interacted with budget to predict gross revenue.  With all but Jennifer Lawrence being listed as the star in less than five films and most less than two, as indicated by the number next to each star, we determined that there were additional factors driving this further, such as co-star, if the movie already had a cult following, was a book first, etc.  We did run a sample test using Jennifer Lawrence and Will Smith to note that, at least for these two stars, there was a positive correlation between gross revenue and budget as depicted in the scatterplot below.

   


Models:

Model 1<-lm(d$gross ~ d$budget)

The correlation chart was a basic look at the significance between gross revenue at the box office and film budget. We soon affirmed our prediction that the correlation between budget and gross was causal by running a simple regression. With a multiple r-squared value of .4624, this model shows that 46.24% of gross revenue can be explained by the budget. Budget also has a very low p-value (2e-16), proving to be a significant factor in predicting a high gross. A higher budget movie has greater potential to purchase the necessary artists, talent, and advertising to create a higher grossing product.
Model 2<-lm(d$gross~d$budget+as.factor(genre), data=d)

Using the as.factor for genre we are able to build a second model that explains how a movie budget and genre affects the revenue of a movie. This model had a slightly higher adjusted R-square with .4691. This model also shows that out of all the genres, the most significant ones were Action, Adventure, Animation, Comedy, and Horror. This indicates that these five genres will be more impactful on the revenue of a film with knowledge of the budget of the film. However, without knowing the budget, Comedy, Drama, and Horror have the most significant impact on gross revenue.
However, we know that correlation does not translate to causation. We carefully curbed our analysis with a linear regression model, placing “Gross” as the response variable and “Budget” as a factor of “Genre”. We used budget as a control because we want to know how the effect of dollars invested in a movie, and more specifically movie genre, would be returned. To our surprise, Action was not the most significant factor, Animation was, as confirmed by a lower p-value and a higher coefficient. In fact, the regression explained that with a hypothetical budget of $0, an Animation movie would produce $22.2M more in revenue than an Action movie. This was an astonishing and valuable discovery.  We noted that Action, Adventure, Animation, Comedy, and Horror all had significant influences.
Model 3<-lm(d$gross~d$budget+as.factor(genre)+d$budget*as.factor(genre), data=d)

For our third model we adjusted it to show a model that explains gross revenue with the budget and genre of the film and the interaction effect between budget and genre. This model was slightly better with an adjusted R-Square of .4696. The model showed that a specific genre budget has a slight effect on gross revenue. Budget is more significant for the Action, Comedy, Drama, and Horror genres.

Model 4<-lm(d$gross~d$budget+as.factor(rating)+d$budget*as.factor(rating), data=d)

For our fourth model, we looked at gross revenue with the interaction between budget and rating. This helped us narrow our data to find the most significant rating for gross revenue as budget increases. This model had an adjusted r-square of .4736. Out of all the different ratings, rated R and G movies were the most statistically significant.

 

Looking at just the adjusted r-squared and the AIC/BIC; the fourth model was the best predictor of increasing gross revenue. However, the rating to budget interaction was only slightly better than the genre to budget interaction. Both our third and fourth model narrowed down our data because they took into consideration the genre and rating with respect to budget of the film. These two qualitative variables were the most significant in predicting the gross revenue outside of just the movie budget.  In joining the interaction together, PG-13 and Horror had the highest and only interaction, with a slightly higher R-squared but higher AIC and BIC, therefore prompting us to return to the previous model and generating the below chart to illustrate our findings.

 

Confidence Interval Testing:

 

With the information we gathered from the regression models, we now have an in-depth look at the effect of budget on genre and rating as they relate to gross revenue. However, these findings contradict our earlier hypotheses. To examine our original assumptions, we performed confidence interval testing.

 

First, we subsetted the data by creating a new dataframe with only Action genre movies rated PG-13. Then we created another variable, ROI, by implementing the ROI formula using budget and gross data sets. We took a summary of the data discovering the mean ROI for PG-13 Action movies was .1666255 or 16.67%, which seems reasonable. If an investor was to invest $100,000, they could expect an average gross return of $116,000 after the movie hits theaters. With a sample size of 468, we used the normal distribution and with 97.5% confidence to determine that the range for ROI on this type of movie would fall between .0899811 and .4232491. This is a fairly large range. But we can say confidently that the largest return on investment should be 42.32%.

 

Using the assurance of strong significance, and high coefficient strength of our regression models, we will use the same confidence interval testing on an R rated Horror film to test the strength of our first null hypothesis. We performed the same subsetting technique to attain a dataframe of only R rated Horror movies to gather a set of 173 movies. After removing two extreme outliers, the mean ROI was pinpointed at 2.6610 or 266.1%. The testing gave us 97.5% confidence that the range of expected ROI should fall between 113.89% and 646.1%.

 

Concluding, R rated Horror movies have a 97.5% confidence in producing a high of 646.1% ROI compared to the maximum potential of 42.32% of a PG-13 Action movie.
We can view this practically and justify the logic in Horror movies having the highest total ROI. When looking at the data it seems that horror movies can be made with relatively low budgets and yield much higher profit. Movies like Paranormal Activity and The Blair Witch Project (the two outliers we removed before confidence interval testing) are prime examples of this phenomenon. The Blair Witch Project cost only around $15,000 to make, but made $107,918,810 in box office revenue, a 7,193% ROI. This data will allow us to make the most informed decision in consideration for investing or merchandising.

 

Insights:

In analyzing the data, we uncovered that budget had the strongest significance and correlation to gross revenue.  Genre as a factor of budget, nor rating, influenced the gross revenue more than the budget itself but were highly significant subfactors.  Ratings of “R” and “G” along with genres of Action, Comedy, Drama, and Horror, had the highest significance when factored with budget to gross revenue, as depicted in the charts above.

As score and and votes would come after the fact, an investor or merchandising company looking to predict which movies would gross the highest revenue and consequently have the potential to yield the highest returns on product related to that movie, we would look to an “R” or “G” rated movie that is an Action, Comedy,Drama, or Horror genre specifically. This can be demonstrated by the movie “The Hangover,” which led to a major economic impact in Las Vegas.

In conclusion, while we have familiarized ourselves with the tools and theories of data mining for business applications, the most important lesson we have learned, has been to view data insights with cautious skepticism. We are confident that our regression analysis was accurate and that our data source appeared reliable; however, few of us are prepared to wager our professional reputations by advising a CEO to allocate millions of dollars of investor capital into the actionable insights that we are recommending. In the actual practice, we would be recommending finding alternate sources of similar data sets to verify these conclusions. In addition to our newfound perspective on the practical values of data mining, we are now prepared to temper future data sourced predictions with a managerial “P-Value”, named the “Group 6 N-Value” to represent common sense and intuition. We therefore recommend, that when proposed data sets lead us down a path of  assumptions based on high P and Adj R sq values, but contradict our own personal “N-Values”, we should first pursue additional data sets and alternate models to demonstrate, without doubt, that those high statistical probabilities are indeed replicable and justifiable in the abstract science of strategic management and consumer behavior.

Italian Assignments, Navigating Cultural Differences

Italian Assignments – Guidelines for Navigating Cultural Differences

University of South Florida

December 4, 2017

Todd Benschneider, Gabriel Bussell, Ali Dogan Sivritepe, Pam Sundown

 

Table of Contents

 

 

  1. Employee Responsibility and Preparing for Success
  2. Bureaucratic Tasks and Tips
  3. Anticipated Economic Adjustments
  4. Schooling for Children
  5. Transportation
  6. Housing
  7. Medical Care
  8. General Business Etiquette
  9. Effective Negotiation Styles
  10. Dress Code and Personal Fashion
  11. Dining Etiquette
  12. Gifts
  13. References

Employee Responsibility

Please understand that your assignment is first and foremost to serve as a corporate diplomat for your employer: Interglobal. One thing that is certain, you should expect that the progress toward your business objective to take much longer than you anticipate, so please adjust the timelines of your assignment objectives to accept these differences, a good rule of the thumb is U.S. estimated timeline multiplied by 2.5 (20). It will also benefit you to resign to the fact that controlling your Italian business partners sense of urgency or work ethic will usually damage relationships. With that stated, your first task will be to adapt your pace to the Italian partner’s timeline.

 

In addition, it is your duty to understand the Italian’s “Ugly American Stereotype”, and avoid reinforcing any negative preconceptions such as: Americans are rude, self-righteous and condescending towards their Italian hosts. It is imperative to understand that over 30% of American expats abandon their first foreign assignment in the first twelve months, the high failure rate is easily reduced by intensive culture preparation like the training course you have been provided (20).

 

Another obstacle in cultural acclimation is the preparation of your family for the assignment, which is why they will also be participating in preparatory conditioning for the cultural adventure the family is about to embark upon. Success on a foreign assignment will prove your resourcefulness and adaptability and be an important stepping stone in the advancement of your areas of responsibility here at Interglobal.

BUREAUCRATIC REQUIREMENTS FOR YOUR EXPAT ASSIGNMENT

For Italian work assignments that last longer than three months, US citizens are required to obtain a “National Work Visa”. Most companies handle the application on your behalf; however, the approval process usually takes six to nine months; therefore, it is imperative that you follow the status of the application and understand your company’s policy. Family visas are comparatively simple to acquire after your National Visa has been granted, your family will only require a written request and valid passport to join you (12).

 

Depending on the living arrangements provided by your employer, you will probably need also to apply for a residency permit to rent or purchase a home. With a residency permit, you will need to apply for tax number, this number, much like a social security number will be required for many routine family needs such as obtaining insurance or healthcare (12).

 

In addition, most expats learn the hard way that their U.S. mobile plan does not transfer to Italy and that roaming charges can rack up a frightening bill unless you modify your plan to an international package prior to your arrival. For business purposes it is standard to have a prepaid Italian SIM card installed on your mobile phone which can convert your current smartphone to a local Italian phone number. When you want to switch back to your American number, you just swap back to the original SIM. A popular technology in Italy has become dual SIM mobile phones that allow you to carry two independent phone lines on a single phone, one as your personal number and the other as your work number (19).

 

ANTICIPATED ECONOMIC ADJUSTMENTS

The cost of living in Italy is not significantly higher than in the US, but the regionally adjusted income to cost of living equates to around 50% higher depending on region. The first observations many expats realize is are larger pay gaps between junior and mid-level managers, this may be a factor of the Hofstede Power Distance Index of 50 compared to the US score at 40 (9). The Hofstede-insights.com website is a valuable tool to use in understanding international cultural differences. For example, Italy pays its young workers the lowest entry level wages when compared to its western European neighbors, you can expect to find students from with excellent academic credentials hiring in at a 2016 average starting pay that equates to $32,500. In contrast nearby Switzerland offers its new graduates an average starting salary equal of $99,300. In the United States you are accustomed to working with young college graduates that typically hire in at $50,200. Fortunately for our prospective mid-career expats you will learn that Italy uses the money saved on young employee salaries to compensate older workers. While still at 11th of 15 western European countries Italy pays its mid-level supervisors the equivalent of $84,100 annually, which climbs quickly as you climb in the corporate ranks (14). These salary differences are partly cultural and due to the fact that Italy has limited resources that impact its current potential GDP. On the following page are detailed comparisons of each region’s major city with cost of living compared to Tampa. You will notice surprisingly inexpensive rents but low local purchasing power due to lower average salaries and higher tax rates (13).

 

 

Milan’s Local Salary Purchasing Power is 50% lower than Tampa’s

Rome Local Salary Purchasing Power 49% Lower than Tampa

Naples’s Local Salary Purchasing Power is 59% lower than Tampa’s

 

 

SCHOOLING FOR CHILDREN

Most expat families send their children to international schools to alleviate the challenges of mastering academic fluency in a second language. The benefit of attending an accredited international school, is that these institutions provide a standardized coursework that transfers into other international locations and can increase chances of being accepted into the most selective international universities (11).

 

In Italy, schooling is broken into three cycles plus kindergarten, which begins with three years of optional kindergarten through age six, then a mandatory elementary school through age 11, during this cycle Italian education system requires all students to learn two foreign languages, the first is typically English which is introduced while children are seven years old, a second foreign language is required at age eleven (7).

 

Following the elementary school cycle, Italian children enter middle school for ages eleven through fourteen. At fifteen they begin the third cycle that we call high school which they are required to take an admission test to qualify for the academic courses to prepare them for the universities. If their academic skills lag their peers, they will typically be assigned to a vocational training school rather than a high school (11).

 

 

 

 

TRANSPORTATION

You should plan on relying on public transportation for the first several months, since you are legally required to carry an International Driving Permit to rent a car. In addition, expect to pay about 60% more for gasoline, however on the bright side, Italians also drive on the right side of the road and most major highways have no speed limits (7).

 

Public buses and trains are the most affordable and reliable mode of transportation in major cities, many expats use the iBus line.  Fortunately, Uber has become very popular in Italy’s major cities in recent years and is often the most affordable mode of transport in many areas, however the Uber network is not well developed in smaller towns (7). Unlike Uber, many taxi drivers will frequently take advantage of foreigners by insisting on payment much higher than the meter reading (7).

 

HOUSING

While many of you will be taking advantage of housing provided by the company, some may choose to explore alternative housing, especially those who find a work from home culture. In Milan and Rome, many prime apartments are being bought up by investors and utilized as AirBNB rentals, which is a type of personal Home to rental Hotel room application that works on a similar principal as Uber as does for personal transportation, this trend is creating a shortage of small apartments with a view of the city (12); however, it is an excellent way to rent for a short while in different parts of the city prior to signing rental contracts.

 

 

MEDICAL CARE

Italian employers are required to contribute to the government health insurance of their workers. Unemployed and retired are covered by the government plan which ensures that as a nation, Italians are well cared for. In comparison to other overseas assignments, Italy provides some of the best healthcare for its residents, including expats. Expats may want to consider private add-on insurance to expedite the timeliness of their medical care visits, especially on assignments where they may not qualify for the host company insurance provisions. As a general rule of thumb, medical attention is given to all regardless of insurance coverage, add on insurance speeds the process and covers most items not covered by the government health insurance. Consult with other expats in your region for their recommendations on medical centers that are familiar with the expat health insurance requirements (12).

 

GENERAL BUSINESS ETIQUETTE

As a rule, Italians have difficulty trusting strangers and most business relationships require and introduction by a third party from the host culture. It is generally considered unprofessional to approach executive level business partners and introduce yourself to begin pitching them your ideas, instead you would normally be introduced to them by someone of similar rank that vouches for your expertise and trustworthiness and begins the conversation for you mentioning your expertise on solutions (10).

 

While English is often the preferred second language of Italian international firms, you should understand that most professionals expect you learn Italian to be accepted into the inner fold of office politics. The Italians are very proud of their heritage, language and culture, and have a hard time building relationship with foreign business associates that are not interested in learning their language. Remarkably, even if you have a mastery of the standard Italian dialect taught to American students, you should expect to be puzzled by the countless number of local dialects that distinguish one region from another (8).

 

NEGOTIATION STYLE

Overcoming the differences in negotiation styles between the American approach that you might take for granted and the decision-making processes of your Italian counterparts has the potential to make or break your success on the assignment. In the first year on your Italian assignment you will wise to negotiate using a host country assistant since mistakes made during this period can cause long term damage to your relationships. You can expect to have a difficult time gaining access to decision makers above your own rank, in the Italian business world, the decision makers are rarely in the meeting where the negotiations take place, because of this you can expect that no hard decisions are normally made during the meeting. You are simply presenting your side and responding to their objections, the junior associates at the meeting will relay the information to the decision makers and who may mull over the proposal for months before responding that they are interested (22).

 

In Italy, decision makers are expecting to deal with someone their own level of seniority, so if you are under 35, it will be wise to reconsider representing yourself in the negotiations, the Italians can feel insulted that they are not dealing with your boss, even if you are the sole decision maker. With that in mind, if the Italians send older representatives to the negotiating table, be certain to address them first, last and with a great deal of deference, even if you are unsure that they are the true decision makers.

 

Italian corporate culture typically requires that a respected host country executive introduce the interested parties, during that introduction day, avoid talking business unless a senior member of their team specifically brings up business matters. Expect several weeks to pass and several visits before the prospects bring up business. It is at these critical points that you be patient and respect their business traditions, this is an area where many Italians expect you to turn into “an ugly American” and try to rush them into viewing you as a trusted business partner. Your Italian partners feel that high pressure tactics are an indicator that they are being tricked into an agreement that is not in their best interest, by instead drawing out the agreement they are comforted by the fact that you are comfortable with them looking at your proposal at every angle. Once they see that you have their best interests at hand when proposing what should appear to be a “win-win” collaboration of resources in your first few agreements your future partnerships will progress with less effort (22).

 

Italians will avoid being direct and often ignore your demands for an immediate acceptance or refusal of terms. It will be rare to hear host country partners use the word “No” during negotiations; instead, they will change the focus to a different part of the agreement or skirt away from business talks and ask about your family. Patience will become a daily mantra during your first year in Italy, without patience your business objectives will often fail to win the cooperation needed from your Italian counterparts. Compare the graphics below to gain a deeper understanding of the difference in the negotiating process (21).

(21)

 

DRESS CODE AND PERSONAL FASHION

Dress codes vary by region and climate; however, you can expect that your Italian coworkers invest a larger portion of their income in their clothing and spend a larger amount of time evaluating their wide range of potential outfits for the day at hand. At first glance you might be misled by the lower number of white shirt and tie dress codes than in the United States; however, if you look closely you may find that knit crew neck shirt is anything but a t-shirt, instead it could be a meticulously pressed merino wool knit that might cost several hundred dollars, the type of attire that Americans would save only for weekend social events.  For example, few American workers with salaries under $100,000 even consider purchasing a $500 pair of shoes or a $2000 handbag; however, it is common in Rome and Milan to notice that some junior level managers own several pairs of $500 designer shoes and a couple of $2000 bags, and what astounds most Americans is that they wear such expensive fashions to work on a daily basis. Forget about the American concept of “work clothes” being the ones that you are not concerned about getting soiled or torn. With that in mind, Italian business people take great care of their clothing, most items are professionally laundered after each day’s wear with the exception of wool suits which are usually hand steamed at home before returning to the closet. As for sneakers or sneaker inspired casual shoes, they are not often seen inside a business office, so when in doubt plan to limit your sneakers to the gym or the ballfield and the same can also be said of typical American jersey cloth sweat suits.

For women, take note that colored nails are uncommon, and makeup is light or often non-existent inside the workplace and is generally considered tacky and the hallmark of a stereotypical tourist. Instead of makeup, women invest their primping time into the preparation of their hair to achieve the best possible shape and shine. You will often be surprised as well to learn that even junior level females commonly spend $100 per week to have their hair care maintained by the countless number of highly respected salons across the major cities, in Milan stylists at the most elegant salons make over $100,000 year (19).

 

While women’s business attire might initially seem sexier than you might find in an American business environment, you will notice that it comes from Italian clothes to be form fitting rather than expose skin, so be cautious about the low-cut blouse or high hemmed skirt that is considered acceptable in American offices. You will later gain an overall perspective on where each culture places its discretionary income, when those Milan coworkers are shocked to learn that you had two $40,000 cars in your garage back in the United States at your pay level.

 

DINING ETIQUETTE

Italians take food very seriously, and take their time enjoying the food experience. When at a restaurant, it is not uncommon to spend several hours enjoying the company of those you came with. Expect to be at a restaurant for a minimum of 1-2 hours, possibly more, especially on Sundays. Each region, and sometimes even individual cities, have their own specialty dishes, so the best way to immerse yourself in the local culture is to ask your server about the specialties. A full Italian meal typically consists of an appetizer, a first course, and a second course with a side dish.

Most Italians drink mineral water and/or wine with meals and you can expect to see a charge on your bill even for tap water. Coffee is not served until after the meal. Italians usually eat late meals, where lunch will not start until approximately 1pm, and dinner not until 8pm. Nearly all shops and restaurants are closed for three to four hours between lunch and dinner; however, in large tourist areas, one may find restaurants open all afternoon. Because Italians spend significantly more time at restaurants than Americans, the server will almost never bring the bill to the table until asked to do so. In addition, table etiquette is similar to most countries in terms of utensil use. However, forearms (not elbows) should rest on the table, not on the lap, which is common in American culture.

Large tips are frowned upon in Italy; most wait staff are viewed as distinguished professionals and receive a respectable living wage salary. In most regions a “service fee” or table fee is included in on the bill which represents a built-in tip, in these situations it would be uncommon to leave an additional tip. Therefore, the most valued tip is enthusiastic praise to the chef and server. Ask your business associates for their recommendations on tipping practices in the area that you will be working, they will appreciate your concern for adapting your behavior to the local practices and be more willing laugh off any inappropriate mistakes made along the way.

GIFT GIVING

Italians like Americans have corporate restrictions on receiving gifts from vendors, so gifts are not expected from business associates, but are common when invited to a home for a dinner party. Typical gifts are inexpensive and often representative of your home country such as American liquors or chocolate, when in doubt flowers are suitable for any occasion, however avoid chrysanthemums which are used for funerals and never given an even number of flowers as it is considered bad luck.

 

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