Week 1 - A Business Analytic Approach to the Value of Information Technology

Welcome to AFM241

Every week, I will provide you with a class update, which will summarize what we have done in the previous week, what we are planning to do in the following week, as well as a reminder of assignments/quizzes which are due in the following week.

Data analytics, big data, cloud computing, internet of things, and blockchain are just a few of the recent technological innovations. Over 200 emerging technologies were identified by Gartner Inc., in the last ten years (Stratopoulos 2016).  Course focus will be on the following questions:  1) Why firms invest in information technology (IT), i.e., what are the expected benefits, costs, and risks. 2) What are some of the most common information technology investments that firms undertake. 3) How do companies justify, monitor, and control IT spending, and how they evaluate the expected payoffs from these investments.

By the end the course, students should be able to achieve the following objectives: 1) Understand appropriate accounting, business and IT strategy concepts in order to explain why firms invest in IT, what investments they make, and how firms justify, monitor, and evaluate IT investments. 2) Leverage and apply appropriate structured/unstructured data and business analytics tools in order to answer questions related to why, what, and how of IT investments. 3) Integrate appropriate accounting, business and IT strategy concepts with facts based evidence from business analytics in order to make recommendations on how to justify, monitor, and evaluate IT investments.

Please start by reviewing the syllabus, which you access from the following link: Syllabus 2016. Course delivery will be based on a combination of lectures (theory) and hands on seminars (leverage business analytics to understand the theory).

Topics and Readings for Week 1

During the first week’s lecture, we will use the Whirlpool case study as a way of exploring the main questions examined in this course (i.e., why firms invest in information technology, what investments they make, and how they justify/evaluate these investments). In chapter one, we will explore emerging technologies and technology adoption (sections 1.3 and 1.4, pp. 12-27).

During the first week’s seminar, we will start working with R (a very powerful and versatile open source software) on business analytics problems. We will explore the RStudio interface and work with stock market data (Appendix 1.A, pp. 39-50), as well as the use of R and Google Trends to understand and predict emerging technology adoption (Appendix 1.B, pp. 51-56).

Please read the Whirlpool case and assigned pages from text before you come to class.

1. Stratopoulos, T. C. 2016. Business Value of Information Technology: A Business Analytics Approach. University of Waterloo, ON. The text is available from the University of Waterloo bookstore. Please note that that the price charged by the bookstore is just the printing cost.
2. Ruback, R. S., Balachandran, S., and Aldo Sesia 2001. Whirlpool Europe. Case Study, Boston: Harvard Business School. You can purchase/download the case from the following URL: http://hbr.org/product/whirlpool-europe/an/202017-PDF-ENG?Ntt=%2520whirlpool%2520europe

Assignments/Quizzes for week 1

There is going to be a quiz on Friday based on the Whirlpool case, assigned pages from the text, and material covered in the lecture/seminar. The quiz is open books,  you will take it online (via Learn), and you will have to take it during a specific time (12:30 pm).

Again welcome to AFM241 and best wishes for a healthy and productive term.

Prof. Theo Stratopoulos

Guest Lecture (CS485): Exploring machine learning opportunites in accounting/finance topics

The objective of this presentation is to explore opportunities for applying machine learning techniques in accounting and finance related topics. More specifically I will outline two of my research projects (Emerging technology adoption and expected duration of competitive advantage, and Financial reports based proxies for the bargaining power of buyers and suppliers) and discuss ways to improve them or automate the suggested processes.

The papers can be accessed from the following url:

1. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2695858
2. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2650793

You can access the slides for the guest lecture from the following link:

1. Guest Lecture CS485

Emerging Technology Adoption and Expected Duration of Competitive Advantage

Data analytics, big data, cloud computing, and internet of things are just a few of the recent technological innovations. A total of 236 emerging technologies were identified by Gartner Inc., over the period 2003 to 2015. The rate of adoption of new technologies has significant implications for adopting firms, suppliers of these technologies, and investors. Some of these new technologies have the potential to disrupt the competitive landscape and provide firms that adopt with a sustained competitive advantage. In the paper "Emerging Technology Adoption and Expected Duration of Competitive Advantage," I have developed a framework for predicting the expected duration of a competitive advantage due to adoption of an emerging technology, and suggest a process for generating technology specific predictions of expected duration.

The framework integrates elements from the technology adoption (diffusion) cycle, hype cycles of emerging technologies, and the resource based view conceptualization of number of firms associated with a perfectly competitive market equilibrium. The objective of this synthesis is to generate a framework for estimating average technology diffusion time and standard deviation. Given the prevailing assumption that technology diffusion follows an approximate bell shaped distribution, we can use these two values to estimate the duration of a technology adoption related competitive advantage. The paper demonstrates the empirical estimation of the mean and standard deviation, as well as expected duration of competitive advantage for a specific emerging technology (i.e., cloud computing) using three methods: Google Trends to capture web search interest, LexisNexis to capture news stories with focus in cloud computing, and Gartner Hype Cycles for emerging technologies. All three methods produce comparable results.

The paper is available from the following URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2695858

UWCISA 8th Symposium & Data Analytics and Data Visualization Workshop

 

The Centre for Information Integrity and Information Systems Assurance at the University of Waterloo (UWCISA) is pleased to announce its 8th biennial symposium to be held in Toronto, Canada. Our symposia are recognized for the extensive interaction between practitioners and academics. Papers and panels will be presented by academics and practitioners addressing risks, controls and assurance issues.

http://saf.uwaterloo.ca/uwcisa/symposiums/index.html

There is pre-conference workshop on Data Analytics and Data Visualization (Oct 1, 2015 8:30 am - 5:30 pm)
Learn through hands-on workshops and post-workshop online material; featuring
IDEA Analytics (data analytics software)
Tableau (data visualization software)
R (the open source analytics solution)

Can every [accounting] course be a big data and analytics course?

During the Accounting IS Big Data conference I was a member of the panel on “How does Big Data Change What We Teach?” One of the issues that was raised during the panel discussion was the following: Can every [accounting] course be a big data and analytics course?

In my opinion, we as teachers need to ask ourselves two questions:

• Does my course aim to help students understand decision making under conditions of uncertainty (does the decision making require some judgment)? For example, does the budgeting process,  calculation of accruals, transfer pricing or auditing process involve some judgment? If the answer is yes, then your course is a good candidate for making it an analytics class, and you should proceed to the next question.

• Are there data (structured or unstructured - you may want to think outside the traditional data box) that we can use to remove some of this uncertainty?

If you answered yes to both questions then you can add an analytics components in your class. The two questions that I have raised are the necessary and sufficient conditions for making your course an analytics course. If your course meets these conditions, may I suggest that you start by focusing on just one specific topics of your course. You don’t have to make your entire course an analytics course overnight.

Teaching Opportunities - Big Data IS Accounting conference

Flipping my Course: Strategic Accounting Analytics  (Notes for the panel presentation on Teaching Opportunities - Big Data IS Accounting conference)

Bargaining power proxies based on text mining of financial reports

The paper "Financial reports based proxies for the bargaining power of buyers and suppliers" is available from the following SSRN link:
http://ssrn.com/abstract=2650793