Assignment #4
Course: ISA 414
Instructor: Dr. Arthur Carvalho
Points:100
Due date: November 18th, 2019, before 11:59 pm
Submission instructions: this assignment is to be done individually. All your answers should be in a single R script. Your code must be well formulated (i.e., no errors) and sound (i.e., it does what the question asks it to do). In particular, the grader must be able to open your .R file using RStudio and run the code without running into errors. Code with errors may receive zero points. Submit the final document on Canvas before the due date.
Question 1: suppose you are responsible for developing the code that computes the number of times each word appears on Twitter every day. One can use these frequencies, for example, as input when calculating the daily trending words. Clearly, Twitter’s data are massive. Being an expert in Hadoop, you quickly realize that you can use MapReduce to complete your task. I highly encourage you to use Remote Desktop Connection to complete this question since all of the required libraries are already installed there, and these libraries are not straightforward to install. Also, make sure you set the version of R to 3.4.3.
a) To test your solution, you will be working with a sample of Twitter’s data. Start by loading the file tweets_asst_4.csv (available on Canvas) to R using the read.csv command (remember to set the argument stringAsFactors = FALSE). Next, upload the resulting data frame to HDFS using the command to.dfs. [10 points]
b) Define a map function to solve your task. Hint: you might want to consider keys created by combining (“pasting”) the date a tweet was tweeted with each word in the tweet. For example, for a tweet tweeted on April 10th, 2017 containing the word “spider”, a possible key returned by the map function would be 2017-04- 10_spider. [20 points]
c) Define a reduce function that counts the number of times each word appears on Twitter per day. Hint: see the reduce function in the word-counting example we covered in class. [10 points]
d) Run the mapreduce function using the data in a), the map function in b), and the reduce function in c). Thereafter, retrieve the final output from HDFS and display the same as a data frame (table). [10 points]
Question 2 – real-life case study: bol.com
A 2015 study sponsored by the Dutch electronic-commerce company bol.com, led by Arthur Carvalho (previously: Rotterdam School of Management – Erasmus University; currently: Farmer School of Business – Miami University) and Esther Hundepool (PwC), investigated some of the factors that affect customers’ willingness-to-buy in B2C e-commerce environments. The case below is an adaptation of the above study.
Business Understanding:
Over the past 20 years, the Internet has changed the way consumers buy goods and/or services. Ranging from groceries to vacation packages and clothing, more and more people are using the Internet to shop online. The online selling of products and/or services by businesses to consumers is often defined as business-to-consumer (B2C) electronic commerce (e-commerce).
E-commerce makes up a big share of the retail industry, often providing more product choices and faster delivery time than “bricks-and-mortar” retailers do. The transactions related to B2C e-commerce in Western Europe totaled 177.7 billion euros in 2013, an increase of 12 percent when compared to the previous year. Another interesting fact is that 95 million consumers in Western Europe bought goods and/or services online in 2013. The total e-commerce sales in the United States amounted to 1,233 billion US dollars in 2013. It is clear that e-commerce is a booming business, which creates an extensive array of research opportunities, e.g., understanding the factors that influence customers’ willingness-to-buy in B2C e- commerce environments.
One can argue that trust perception is one of the biggest barriers for consumers to engage in electronic commerce. A potential lack of trust will likely discourage consumers to participate in online shopping. Therefore, it is interesting to study how
to manage trust in e-commerce environments as well as to study the influence of different types of trust on consumers’ willingness-to-buy online.
In addition to trust perception, risk perception can be another challenging factor in e-commerce. Different types of risk perception are likely to influence consumers’ attitude towards online transactions.
Finally, consumers’ demographic traits might also be of influence when it comes to online shopping behavior.
The goal of this study is to investigate the variables that either positively or negatively significantly influence customers’ willingness-to-buy in B2C e- commerce environments. Following the above background sketch, one can formulate the underlying business problem as:
What are the determinants of customers’ willingness-to-buy in B2C e-commerce environments?
In particular, this study aims at measuring the effects of perceived risk and perceived trust on consumers’ willingness-to-buy online. As e-commerce sales are expected to continue growing over the years, understanding these factors, and how to effectively deal with them, will play a crucial role in online strategies of companies engaging in e-commerce.
Data Understanding:
The data in this study were collected by means of an electronic survey developed in partnership with PwC and bol.com. To illustrate the process of online shopping, the survey started by showing the respondents a 5-minute video containing an actual browsing and shopping behavior on bol.com, the number one online retailer in the Netherlands. Specifically, after exhibiting some features of the website, the video showed a search for and a purchase of a digital camera.
When the video was over, the survey showed a web page from bol.com containing a detailed description of the purchased camera. Following the video and product description, the survey measured three dimensions of perceived risk and three dimensions of perceived trust using five question-items per dimension. The six dimensions are: Perceived Product Risk (PPR), Perceived Informational Risk (PIR), Perceived Economic Risk (PER), Perceived Integrity (PI), Perceived Safety (PS), and Perceived Benevolence (PB).
Next, the survey measured the main dimension of interest, Willingness-to-Buy (WTB), using five question-items. All the question-items used a 0-100 scale. Think about a chosen scale-value as the likelihood (represented in percentage values) that the respondent agrees with a statement in the question-item. At the end, the survey collected demographic information, such as respondents’ age, income, and gender.
The survey was available from March 17th, 2015 to April 18th, 2015. We invited participants via social networks and by sending emails to subject pools from Rotterdam School of Management at Erasmus University, and the office of the company PricewaterhouseCoopers (PwC) located in Rotterdam (the Netherlands). In total, 360 participants started the survey.
After the data collection phase, we prepared the resulting data set for posterior analysis by removing all incomplete survey responses, which resulted in a total of 199 full observations in the data set, a completion rate of 55.27%. We show below the structure of the survey we used to collect data (translated from Dutch):
Perceived Product Risk (PPR)
– PPR_1: I think this product will perform as expected.
– PPR_2: The product purchased will likely not perform as expected.
– PPR_3: I think it is difficult to judge the quality of this product adequately.
– PPR_4: In case of a product purchase on this website, it is likely to fail the
performance requirements originally intended.
– PPR_5: I believe the likelihood is high that something is wrong with the
performance of this product.
Perceived Informational Risk (PIR)
– PIR_1: It is clear to me whether Bol.com intends to give my personal
information to third parties.
– PIR_2: I believe this website will protect my personal information from
exposure to third parties.
– PIR_3: I believe Bol.com does not intend to misuse the personal
information provided by me.
– PIR_4: I believe Bol.com will protect and store my personal information
correctly.
– PIR_5: I believe Bol.com is likely to misuse my personal information.
Perceived Economic Risk (PER)
– PER_1: Purchasing from this website would involve economic risk (fraud,
hard to return).
– PER_2: I believe I can return this product and get a refund easily.
– PER_3: I believe there is a high chance that I stand to lose money if I
purchase this product.
– PER_4: When I purchase this item from Bol.com I have the chance of
financial loss.
– PER_5: I believe there is a great chance I do not receive the intended
product.
Perceived Integrity (PI)
– PI_1: Bol.com acts sincere in dealing with their customers. – PI_2: I believe this online shop is honest to their customers. – PI_3: I believe Bol.com would keep its promise.
– PI_4: I would characterize Bol.com as honest.
– PI_5: Bol.com acts truthful in dealing with their customers.
Perceived Safety (PS)
– PS_1: I believe this online shop has sufficient technical capacity to ensure
my data cannot be intercepted by hackers.
– PS_2: I believe this online shop shows great concern for the security of
any of the transactions.
– PS_3: I think this online shop has mechanisms to ensure the safe
transmission of my information.
– PS_4: I believe to have a safe transaction when purchasing from Bol.com.
– PS_5: Purchasing from this online shop is safe.
Perceived Benevolence (PB)
– PB_1: When problems occur, I believe this website will be prepared to
solve my problems.
– PB_2: In case of a problem, I believe it will be easy to report a complaint
to this website.
– PB_3: I believe, when required, Bol.com would do its best to offer help.
– PB_4: In case of a problem, I believe this website will make all the
necessary efforts to solve it.
– PB_5: I believe this online shop keeps the well-being of the consumer
needs in mind.
Willingness to Buy (WTB)
– WTB_1: The likelihood that I would shop at this online shop is high. – WTB_2: I would consider buying this product at this price.
– WTB_3: I would be willing to recommend this online shop to friends. – WTB_4: I would be willing to buy at this online shop.
– WTB_5: It is likely that I will purchase at this online shop.
Demographics:
– Gender: What is your gender?
Male
Female
– Age: What is your age?
Below 18 years old
Between 18 and 25 years old Between 26 and 35 years old Between 36 and 45 years old Between 46 and 55 years old Above 55 years old
– Income: What is your current yearly income? Less than $20.000
Between $20.000 and $35.000 Between $35.000 and $50.000 Between $50.000 and $65.000 More than $65.000
I prefer not to say Data Preparation:
It is now time to analyze our data in order to provide an answer to the business problem. From now on, you will be using the Spark technology in conjunction with R programming language. I highly encourage you to use Remote Desktop Connection to complete this question. Make sure you set the version of R to 3.6.1. Then, run the following commands to install the required libraries:
install.packages(“sparklyr”) spark_install(version = “2.0.2”)
a) Start by downloading the data set bol.csv from Canvas. Next, run the following commands to load the data locally, connect to a Spark cluster, and send the survey data to the Spark cluster. [0 points]
library(“sparklyr”)
library(“dplyr”)
survey_data <- read.csv(“bol.csv”)sc <- spark_connect(master = “local”, version = “2.0.2”)survey_tbl <- copy_to(sc, survey_data, “survey”, overwrite = TRUE)Unless otherwise stated, all the following questions must be answered with code that is executed on the Spark cluster. You should expect to use functions from the R package dplyr in conjunction with Spark.b) Note that the scales of PPR_1, PIR_5, and PER_2 are different from the scales of the other items in their dimensions (constructs). For example, the scale of PPR_1 is increasing in positivity, whereas the scales of PPR_2, PPR_3, PPR_4, and PPR_5 are decreasing in positivity. Hence, you have to transform the scales for the sake of consistency. The goal of this preprocessing step is to have all risk-related variables using scales in increasing negativity, and all trust-related variables using scales in increasing positivity. To do so, transform (mutate) the variables PPR_1, PIR_1, PIR_2, PIR_3, PIR_4, and PER_2 by subtracting their original values from 100, e.g., the new values of PPR_1 must be equal to 100 minus the old values. These transformations should change the data set in the Spark cluster. [10 points]c) After fixing the scales, it is now time to create our variables. Remember that we measured each risk and trust dimensions using five question-items. Since the question-items are highly subjective, one should expect that the respondents’ answers contain some “random component”. A common approach to eliminate some of this “randomness” is by averaging the values of the question-items across each dimension. In practice, one would have to perform reliability analysis and check for internal consistency before doing so (e.g., performing a confirmatory factor analysis and calculating Cronbach’s alpha), but this is beyond the scope of this assignment.Using the mutate function from dplyr, add the following features to the data set in the Spark cluster: [10 points]PPR = (PPR_1 + PPR_2 + PPR_3 + PPR_4 + PPR_5)/5 PIR = (PIR_1 + PIR_2 + PIR_3 + PIR_4 + PIR_5)/5 PER = (PER_1 + PER_2 + PER_3 + PER_4 + PER_5)/5 PI = (PI_1 + PI_2 + PI_3 + PI_4 + PI_5)/5PS = (PS_1 + PS_2 + PS_3 + PS_4 + PS_5)/5PB = (PB_1 + PB_2 + PB_3 + PB_4 + PB_5)/5WTB = (WTB_1 + WTB_2 + WTB_3 + WTB_4 + WTB_5)/5Data Modeling:d) Next, you will build an explanatory model that tries to relate the risk and trust dimensions to willingness-to-buy. To simplify the analysis, ignore the demographic variables in the data set. Using the ml_linear_regression function from the sparklyr package, build a linear regression model where the dependent variable is WTB and the independent variables are PPR, PIR, PER, PI, PS, and PB. Apply the summary function to your model to retrieve coefficients and associated p-values. [10 points]Conclusion:e) Given the coefficients and p-values from above, which actions would you suggest bol.com to take to increase consumers’ willingness-to-buy? List and carefully explain at least three features that bol.com could add to its website to alleviate some significant risk and trust perception issues, e.g., money back guarantees to reduce perceived economic risks, online reviews to decrease perceive product risk, etc. (sloppy answers will receive zero points) [20 points]
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