Background
The automobile industry has been one of the most prominent industries since the start of the twentieth century. Originally it was created by the German and the French, but later in the first half of the twentieth century, it was adopted by Americans (Showi, 2018). The Automobile industry has since been around and is in the mature stage of its life cycle (Savaskan, 2019). The automobile industry consists of the production of sedans, passenger cars, luxury vehicles, and many others. The industry has impacted billions of people’s lives and the global economy. It is an ever-evolving industry; cars develop and adapt to the changing environment and current trends as the decades have come and gone.
In the five years leading to 2019, there has been a strong performance from this production division. Since 2014, the US has seen a steady increase in employment, disposable income, and low-interest rates (Savaskan, 2019). Along with other components, these factors have increased consumer spending on more extravagant products. As a result, people in the US have been buying automobiles. However, new car sales have been starting to slow down due to increased competition in the industry and low demand. New car sales growth decreased from 18 percent a year between 2006 and 2012 to a projected 6 percent a year between 2012 and 2020 (Mohr & Müller, 2013). In addition, IBISWorld estimates industry revenue to decrease at an annualized rate of 3.6% to $112.5 billion over the five years to 2019.
One way to compare the advantages and disadvantages of the automobile industry is by creating a SWOT analysis. A SWOT analysis stands for strengths, weaknesses, opportunities, and threats. Strengths and weaknesses are used to identify internal factors, while opportunities and threats are used to identify external factors. Strengths for the automobile industry include product innovations, evolving industry due to the popularity of cars among consumers, availability for different lifestyles, and involvement of the cheap workforce from developing countries in car manufacturing.
Strengths, such as technological changes, play a vital role in the automobile industry, due to changing demands and regulations. New technology and research make it possible to regularly upgrade the standards of driving in every new model. Many car brands have been creating environmentally friendly vehicles, that are aimed to reduce fuel consumption; following the trend towards an environmentally conscious society. Electric vehicles are becoming convenient and cost-efficient in the long run (UCSUSA, 2018). Along with environmental and technological changes, innovations directed towards customer safety concerns are constantly being updated in this industry. New seat belts, airbags, and other devices that help in avoiding injuries in case of a collision or an accident are continuously designed and undergo frequent testing. Other advances in safety technology have been in Anti-Brake Systems. Anti-Brake Systems have been able to increase stopping distance across many different surfaces (Mohr & Müller, 2013). Technological changes also contribute to the evolving industry. For example, people are able to travel, work, and live by using automobiles. Lastly, the industry has many manufacturing facilities in Asian nations to control costs. Producing cars using manufacturing facilities in developing countries, such as Thailand, helps to minimize costs and make the process cheaper.
Weaknesses included in the industry are the bargaining power of consumers and government regulations. Over the last 3-4 decades, the automobile market has shifted from a demand to a supply market (Showi, 2018). There is a large number of variants in the market causing stiff competition between all of them, because of this, there is a long list of competitors to choose from giving consumers the power to decide based on their preferences. As for regulations, the increase in regulations with respect to environmental and safety standards will raise costs according to McKinsey (Mohr & Müller, 2013). The growth rate of the Automobile industry is in the hands of the government due to regulations and higher taxes. For example, there is no chance of entry of outside vehicles in the state.
Opportunities are external factors that are able to increase profitability. One example is fuel-efficient cars, as mentioned before, are good opportunities due to environmental factors and it is an emerging market that will experience the most growth. Another is an opportunity for different makes and models for different psychographics and demographics. However, there has been a movement towards buying sport utility vehicles (SUVs), light trucks and more recently, crossovers (Savaskan, 2019). Consumer behavior is continuously changing, and the automobile industry has been able to adjust to changes such as this. In 2017, Fiat Chrysler Automobiles NV halted the production of cars and sedans in the United States because of shifting demands (Savaskan, 2019).
Threats include the intensity of the competition and fluctuation in the economy. There is intense competition in the automobile industry from a large number of car companies such as Honda, Toyota, BMW, and Chevrolet. This leaves little scope for new players and is now a supply market due to this fact. Also, the fluctuations in fuel prices remain the determining factor for its growth. For example, government regulations relating to the use of alternative fuels can cause a change in whether people are willing to buy cars (Mohr & Müller, 2013).
Analysis and Results
The database that is being analyzed consists of 418 cars of different brands, whether it was luxury or not, and origins. The data was put into SPSS, a data analysis software, in order to run tests to determine the relationships of variables in the dataset. The descriptive statistics are used to summarize the given data set. The scale variables that are used in the tests are reputation, proportion, and LogMaint.
Within the descriptive, N represents the number of vehicles which is 418 for all of the variables. The reputation has a range between 0 to 1 with a mean of .50964 and a standard deviation of .272277. The proportion has a minimum of 0.7256 with a maximum of 0.9574. It has a mean of 0.8816 and a standard deviation of 0.04528. LogMaint has a minimum of 3.546 and a maximum of 4.317 with a mean of 3.85 and a standard deviation of 0.164. The frequencies of the tests are luxury (1) vs non-luxury (0) and origin of the car brands 0-American, 1-European, and 2-Asian. The N value for luxury and origin is 418 cars which take into account every car in the dataset. The minimum for luxury is 0 while the max is 1 with a standard deviation of 0.477. For Origin, the minimum is 0 and the maximum is 2 with a standard deviation of 0.828. The frequency chart and graph for origin and luxury are used to see how many cars were in each categorical variable
To analyze the difference between origin and value they pose the following question: will the Asian car brands hold more value than American and European car brands in the market? To complete this test, it is necessary to use an ANOVA Scheffe. This test was decided to see if there is a difference between country of origin and the difference in the value they hold. ANOVA is used when analyzing to see if there is a difference between the different factors in the data. There is an assumption that there is no difference between groups. The Scheffe post-test is used to find out which pairs of means are significant. This test demonstrates the difference between the origin 0-American, 1-European, and 2-Asian, and Proportion. The origin is the factor and the dependent variable is the proportion in order to compare the pairs of means.
After running the ANOVA, it shows that the p-value is 0.022 which is lower than the 0.05 significance level. This shows the data is significant and that there is a difference between variables. The multiple comparisons chart shows that there is a slight significant difference between American and European cars with a sig value of 0.042. However, the relationship between American and Asian car brands has a significance value of 0.997 showing that the two factors are extremely similar.
Taking a look at their means in the descriptive chart, American car brands have a mean of 0.886 and Asian car brands have a mean of 0.885 which means that the two carry almost the same value in the market. Likewise, the relationship between European car brands and Asian car brands has a significance level of 0.072 showing that the relationship is not significantly different either. Therefore, American car brands hold more value than European brands while Asian car brands hold the same reputation as American and European brands.
The next test that is being run is a regression that predicts the proportion by using LogMaint. Regression is used to estimate the relationship between the independent variable and the dependent variable. Predict the amount of value the car retains based on the log of the maintenance cost (LogMaint). This data helps to determine consumer behavior based on how much maintenance cost is. Reputation was tested as a group level to see if it explains a statistically significant amount of variance in your dependent variable.
First, the R2 value is used to determine the amount of variance in the dependent variable based on the independent variable or regression line. The R2 value is 0.033 showing that the data is dispersed and a weak relationship between the variables. The ANOVA chart shows that the significance level is 0 showing that the data is significant and shows a linear relationship. The coefficients chart shows the variables that all of the variables are able to be used in the regression equation in order to predict future behavior because the significance level is less than 0.05 for all the variables and the random effect. The random effect has a significance level of 0.0 showing that reputation has a significant effect on the model, and it has to be put in the equation. Therefore, the equation is as follows:
Propi=b0+b1(LogMaint)+ei
B0i=1.01 + ei
B1i=-.027 – 0.048(Rep) + ei
Or to put them all together Propi=1.01 – (0.027 – 0.048(Rep)) + ei
An independent t-test was run to test if luxury or non-luxury affects the reputation of a car. The two groups that were luxury/non-luxury and reputation. An independent test was used because the test is looking at luxury vs non-luxury to determine whether there is statistical evidence that the two populations are significantly different. The first step in this process is to look at Levene’s Test for Equality of Variances. This is used for assuming the equality of variances for two groups. Considering the significance level for this is 0.0, equal variances are not assumed or the reputation for luxury cars will be different than that of the reputation of non-luxury cars. Then moving on to the Significance (2-tailed), with a 0.05 significance level, equal variances are not assumed. The mean for luxury vehicles is 0.6937 while the mean for non-luxury is 0.43725.
Limitations
There were multiple limitations that occurred when running statistical tests. The analyst has limited experience utilizing SPSS when creating and analyzing tests. A limitation while editing the data was the lack of knowledge of the analyst had of different car brands. For example, the origin and whether the car was luxury or not had to be added by using external sources. Due to a lack of knowledge, the analyst could only compile a limited amount of factors that could be tested. Although they are informed of the basics of the car industry overall, they were less knowledgeable of all car brands and their variables.
One of the first limitations found was that the dataset had multiple conflicting variables. Many of the variables in the excel sheet could not be utilized. For example, variables like class, clean retail, and all the price variables look like they could all be useful. However, there was no explanation of the factors and could not be used because of the lack of knowledge behind them. There is some data that is useless such as sub model, month, and year; those variables don’t have any explanation, deeming them worthless. Furthermore, the number of cars and car brands represented in the dataset was limited.
Finally, there were 100 cars in the dataset that did not contain a standard deviation, mean and coefficient variable. Therefore, the data might have been skewed and did not represent an accurate account of all the means, standard deviation, and coefficient variables. Also, while running an ANOVA test, the analyst noticed that there was a high significance level but there was no way to find out if other forces contributed to that. There were only 418 cars and a limited number of brands represented in the data that does not represent the different. The findings of this data set cannot be applied to alternate car brands because car brands have different reputations and attributes.
Discussion and Recommendations
The analyzed database consisted of 418 cars of different brands, makes, and models. The scale variables that are used in the tests were reputation, proportion, and LogMaint. While analyzing these descriptive, N represents the number of vehicles which is 418 for all of the variables. Reputation was a range from 0-1 that showed how well the car held its reputation. It was used when running the regression and independent t-test. Proportion and LogMaint are both used in the regression for forecasting future automobile habits. The two categorical variables that were used were whether they were a luxury car or not and the origin of the car brand.
During the research, several discoveries were found. An ANOVA Scheffe was run to see if there was a difference between how American, Asian and European car brands retain value. After running an ANOVA Scheffe, it was determined that there is a difference in how much value was retained between American and European car brands. Americans held more value than European cars in the market, with a 0.013 mean difference according to the ANOVA testing. However, that was not the case when testing the relationship between European and Asian, or the relationship between Asian and American car brands because both of the groups had similar means in proportion with a mean distribution of 0.0004 for American and Asian and 0.013 for the mean distribution for Asian and European.
Additionally, a regression test was run on SPSS in order to predict the amount of value the car retains based on the log of the maintenance cost (LogMaint). The ANOVA chart shows that the significance level is 0 indicating that the data is significant and represents a linear relationship. The random effect has a significance level of 0.0 showing that reputation has a significant effect on the model and is in the equation (Propi=1.01+-.027 – 0.048(Rep)+ei). Since Reputation affects our data, it is added in along with LogMaint and the constant.
Finally, an independent t-test was run to test if luxury or non-luxury will have the same or different reputation. The mean for luxury vehicles is 0.6937 while the mean for non-luxury is 0.43725. While looking at these means, it was found that the reputation for luxury cars will be different than that of the reputation of non-luxury cars. This conclusion is due to the Levene test as well as the significance level representing that there are differences between the two categories.
Referring back to the background, there is a large threat of increased competition. This comes from various car brands entering the market. Along with that, the automobile industry’s supplier market consists of a large number of variants in the automobile market. This is causing stiff competition between all brands; there is a long list of competitors to choose from which gives consumers the power to decide brands based on their preferences. The ANOVA Scheffe proves that there is a minimal difference in how the origin of the car affects the proportion.
Therefore, this can be generalized and inferred that origin has a minimal effect on proportion of a car. This shows that all cars are similar, in that sense, which is bad for competition because the origin does not differentiate cars from one another making them more similar and less unique. This can also be seen through the independent t-test showing that luxury holds a higher reputation than non-luxury vehicles. This shows that consumers have a preference in the market and in the supply market, and therefore, will go after their desired automotive transportation.
References
Mohr, D., & Müller, N. (2013, August). The road to 2020 and beyond: What’s driving the global
automotive industry? Retrieved from https://www.mckinsey.com/~/media/mckinsey/dotcom/client_service/Automotive and Assembly/PDFs/McK_The_road_to_2020_and_beyond.ashx.
Savaskan, D. (2019, June). Car & Automobile Manufacturing in the US. Retrieved from
https://clients1.ibisworld.com/reports/us/industry/default.aspx?entid=816.
Showi, Ashwin, Automobile Industry: Past, Present, and Future (February 13, 2018). Available
at SSRN: https://ssrn.com/abstract=3150440 or http://dx.doi.org/10.2139/ssrn.3150440
Top Five Reasons to Choose an Electric Car. (2018, March 12). Retrieved from
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Visram, T. (2018, July 10). How Thailand became the Detroit of Asia. Retrieved from
https://money.cnn.com/2018/07/10/news/world/thailand-auto-industry/index.html.
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