Citation: Trends in Internet-based business-to-business marketing

Abstract: The Internet is changing the transactional paradigms under which businesses-to-business marketers operate. Business-to-business marketers that take advantage of the operational efficiencies and effectiveness that emerge from utilizing the Internet in transactions are out performing firms that utilize traditional transactional processes. As an example, Dell computers, by utilizing business-to-business processes that take advantage of the Internet, has gained the largest market share in the PC business when compared to traditional manufacturers such as Compaq. This paper first examines the genesis of the Internet movement in business-to-business markets. The long-term impact of the increase of business-to-business utilization of the Internet on the marketing theory and marketing process is then discussed. Finally, managerial implications and directions for future research are highlighted.

Dataset includes:

1)  Business marketing focus - traditional or forward thinking.

2)  Internet use - low, medium, or high levels of business marketing use on the internet.

3)  Time _ 1 - sales scores at the first measurement time.

4)  Time _ 2 - sales scores at the second measurement time

On all of these questions, be sure to include a coherent label for the X and Y axes. You should change them to be “professional looking” (i.e. Proper Case, explain the variable listed, and could be printed in a journal). The following will be assessed:

1)  Is it readable?

2)  Is X-axis labeled appropriately?

3)  Is Y-axis labeled appropriately?

4)  Is it the right graph?

5)  Do the labels in the legend look appropriate?

6)  Are there error bars when appropriate?

We won’t grade for color of bars or background color, but you should consider that these things are usually printed in black/white - so be sure you know how to change those values as well as get rid of that grey background.

Please note that each subpoint (i.e. a, b) indicates a different chart.

str(lab5)
## 'data.frame':    300 obs. of  5 variables:
##  $ id       : Factor w/ 300 levels "S001","S002",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ biz_focus: Factor w/ 2 levels "forward","traditional": 1 1 1 1 1 1 1 1 1 1 ...
##  $ internet : Factor w/ 3 levels "high","low","medium": 1 1 1 1 1 1 1 1 1 1 ...
##  $ time.1   : num  7.54 7.57 5.93 6.77 7.62 ...
##  $ time.2   : num  9.71 10.24 9.18 10.5 8.69 ...
head(lab5)
summary(lab5)
##        id            biz_focus     internet       time.1       
##  S001   :  1   forward    :150   high  :100   Min.   :-0.6805  
##  S002   :  1   traditional:150   low   :100   1st Qu.: 2.0516  
##  S003   :  1                     medium:100   Median : 3.3273  
##  S004   :  1                                  Mean   : 3.6253  
##  S005   :  1                                  3rd Qu.: 4.7821  
##  S006   :  1                                  Max.   : 9.9014  
##  (Other):294                                                   
##      time.2       
##  Min.   :-0.1169  
##  1st Qu.: 3.0242  
##  Median : 4.4151  
##  Mean   : 5.2299  
##  3rd Qu.: 7.2588  
##  Max.   :12.1847  
## 
lab5dat <- melt("lab5", id.vars = c('id', 'biz_focus', 'internet'), measure.vars = c('time.1', 'time.2'))

  1. Make a simple histogram using ggplot:

    1. Sales at time 1

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

b.  Sales at time 2
lab5_hist_time2 <- ggplot(lab5, aes(time.2))
lab5_hist_time2

##Example for practice of labels section i.e. "labs" function
lab5_hist_time21a <- ggplot(lab5, aes(time.2)) +
    geom_histogram(color = "green", bins = 5, binwidth = 0.25) +     labs(title = "Business Marketing Focus and Internet Use", subtitle = "Data to study impact of internet use", tag = "Figure for Sales at Time.2", x = "X-axis label goes here", y = "Y-axis label goes here", caption = "Long-term impact of the increase of business-to-business utilization of the Internet on the marketing theory and marketing process", colour = "Time.2") +
    theme_classic()
lab5_hist_time21a

lab5_hist_time21 <- ggplot(lab5, aes(time.2)) +
    geom_histogram(color = "cyan", bins = 10, binwidth = 0.25) + 
    xlab("X-axis label here") + 
    ylab("Y-axis label here") + 
    theme_minimal()
lab5_hist_time21

lab5_hist_time22 <- ggplot(lab5, aes(time.2)) +
    geom_histogram(color = "red", bins = 20, binwidth = 0.5) + 
    xlab("Sales scores at the second measurement time2") + 
    ylab("Frequency 2") + 
    theme_bw()
lab5_hist_time22

lab5_hist_time22a <- ggplot(lab5, aes(time.2)) +
    geom_histogram(color = "pink", bins = 30, binwidth = 0.75) + 
    xlab("Sales scores at the second measurement time 3") + 
    ylab("Frequency 3") + 
    theme_classic()
lab5_hist_time22a

lab5_hist_time23 <- ggplot(lab5, aes(time.2)) +
    geom_histogram(color = "blue", bins = 5, binwidth = 1) + 
    xlab("Sales scores at the second measurement time 4") + 
    ylab("Frequency 4") + 
    theme_classic()
lab5_hist_time23

lab5_hist_time24 <- ggplot(lab5, aes(time.2)) +
    geom_histogram(color = "green", bins = 15, binwidth = 0.25) + 
    xlab("Sales scores at the second measurement time 5") + 
    ylab("Frequency 5") + 
    theme_test()
lab5_hist_time24

  1. Make a bar chart with two independent variables:

    1. Business focus, internet, DV: sales at time 2
lab5 <- read.csv("/Users/na/Desktop/Shri R Projects/ANLY500/05_data.csv")

lab5_bar1a <- ggplot(lab5, aes(biz_focus, time.2, fill = internet))
lab5_bar1a

lab5_bar1 <- ggplot(lab5, aes(biz_focus, time.2, fill = internet)) +
    stat_summary(fun.y = "mean", geom = "bar", position = "dodge") + 
    stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", position = position_dodge(width = 0.50), width= 0.5) + 
    xlab("Business marketing Focus 1") + 
    ylab("Sales scores at second measurement time") +
    scale_fill_manual(name = "Internet Use", labels = c("High", "Low", "Medium"), values = c("Blue", "Pink", "Cyan")) +
    theme_classic()
lab5_bar1

lab5_bar2 <- ggplot(lab5, aes(biz_focus, time.2, fill = internet)) +
    stat_summary(fun.y = "mean", geom = "bar", position = "dodge") + 
    stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", position = position_dodge(width = 0.50), width= 0.25) + 
    xlab("Business marketing focus") + 
    ylab("Sales scores at second measurement time") + 
    theme_light() +
    scale_fill_manual(name = "Internet Usage", labels = c("High", "Low", "medium"), values = c("Red", "Green", "Gray"))
lab5_bar2

  1. Make a bar chart with two independent variables:

    1. Time (time 1, time 2), Business focus, DV: is sales from time 1 and 2
lab5 <- read.csv("/Users/na/Desktop/Shri R Projects/ANLY500/05_data.csv")
lab5dat <- melt(lab5, id.vars = c("biz_focus", "internet"), measure.vars = c("time.1", "time.2"))

lab5_bar_2a <- ggplot(lab5dat, aes(biz_focus, value, fill =variable))
lab5_bar_2a

lab5_bar_3 <- ggplot(lab5dat, aes(biz_focus, value, fill =variable)) +
    stat_summary(fun.y = "mean", geom = "bar", position = "dodge") + 
    stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", position = position_dodge(width = 0.5), width= 0.5) + 
    xlab("Business Marketing Focus") + 
    ylab("Sales scores at both measurement times") + 
    scale_fill_manual(name = "Time Data Series", labels = c("Time.1 Data", "Time.2 Data"),
                    values = c("Pink", "Cyan"))
lab5_bar_3

lab5_bar_4 <- ggplot(lab5dat, aes(biz_focus, value, fill =variable)) +
    stat_summary(fun.y = "mean", geom = "bar", position = "dodge") + 
    stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", position = position_dodge(width = 0.75), width= 0.75) + 
    xlab("Business Marketing Focus") + 
    ylab("Sales scores at both measurement times") + 
    scale_fill_manual(name = "Time Data Series", labels = c("Time.1 Data", "Time.2 Data"),
                    values = c("Black", "Gray"))
lab5_bar_4

  1. Make a simple line graph:

    1. Time (time 1, time 2), DV: is sales from time 1 and 2
lab5_line <-ggplot(lab5dat, aes( variable, value))
lab5_line

lab5_line1 <-ggplot(lab5dat, aes( variable, value)) +
    stat_summary(fun.y = mean, geom = "point") + 
    stat_summary(fun.y = mean, geom = "line", aes(group=1)) + 
    stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.1) + 
    xlab("Time of Measurement") + ylab("Sales scores") + 
    theme_bw()
lab5_line1

lab5_line1a <-ggplot(lab5dat, aes( variable, value)) +
    stat_summary(fun.y = mean, geom = "point") + 
    stat_summary(fun.y = mean, geom = "line", aes(group=1)) + 
    stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2) + 
    xlab("Time of Measurement") + ylab("Sales scores") + 
    theme_classic()
lab5_line1a

  1. Make a simple scatterplot:

    1. Sales at Time 1, Time 2
lab5_scat <- ggplot(lab5, aes(time.1, time.2))
lab5_scat

lab5_scat1 <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point() +
    xlab("Sales scores at Time.1") + 
    ylab("Sales scores at Time.2") + 
    theme_bw()
lab5_scat1

lab5_scat2 <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(color= "blue") +
    xlab("Sales scores at Time.1") + 
    ylab("Sales scores at Time.2") + 
    theme_classic()
lab5_scat2

lab5_scat3 <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(color= "red") +
    xlab("Sales scores at Time.1") + 
    ylab("Sales scores at Time.2") + 
    theme_tq_green()
lab5_scat3

lab5_scat4 <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(color= "red") +
    xlab("Sales scores at Time.1") + 
    ylab("Sales scores at Time.2") + 
    theme_minimal()
lab5_scat4

lab5_scat5 <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(color= "black") +
    xlab("Sales scores at Time.1") + 
    ylab("Sales scores at Time.2") + 
    theme_classic()
lab5_scat5

  1. Make a grouped scatterplot:

    1. Sales at time 1 and 2, Business focus
lab5_scat2 <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(color= "blue") +
    xlab("Sales scores at Time.1") + 
    ylab("Sales scores at Time.2") + 
    theme_classic()
lab5_scat2

lab5_scat2a <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(aes(col = lab5$biz_focus)) + 
    geom_smooth(method = "lm", color = "blue") + 
    xlab("Sales scores at Time.1 data point") + 
    ylab("Sales scores at Time.2 data point")  + 
    scale_fill_manual(name = "Business Marketing Focus", labels = c("Forward Thinking","Traditional Thinking"), values = c("Cyan", "Red")) +
    scale_color_manual(name = "Business Marketing Focus", labels = c("Forward Thinking","Traditional Thinking"), values = c("red", "orange")) + 
    theme_bw()
lab5_scat2a

lab5_scat3a <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(aes(col = lab5$biz_focus)) + 
    geom_smooth(method = "lm", color = "red") + 
    xlab("Sales scores at Time.1 data points") + 
    ylab("Sales scores at Time.2 data points")  +
    scale_color_manual(name = "Business Marketing Focus", labels = c("Forward Thinking","Traditional Thinking"), values = c("blue", "orange")) +
    theme_bw()
lab5_scat3a

##Attempted a grouped scatter plot between Time.1, Time.2 and the Internet for practice
lab5_scat4a <- ggplot(lab5, aes(time.1, time.2)) +
    geom_point(aes(col = lab5$internet)) + 
    geom_smooth(method = "lm", color = "blue") + 
    xlab("Sales scores at Time.1 data points") + 
    ylab("Sales scores at Time.2 data points")  +
    scale_color_manual(name = "Internet", labels = c("high", "low","medium"), values = c("red", "orange", "green")) + 
    theme_classic()
lab5_scat4a

a <-ggpairs(lab5dat, title = "Business Marketing focus using internet") 
a
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

b <- a + theme_classic()
b
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

c <- a + theme_minimal()
c
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

d <- a + theme_tq_green()
d
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.