Inhalt

Abstract:

Description of course: In this course you will learn how to apply commonly used quantitative methods for analyzing Internet data. The course will focus on online consumer behavior, trust games and network analysis.  Specifically you will explore how individuals behave in an online shopping environment and what algorithms and techniques can be applied to examine their decision processes. This involves methods for identifying online consumers' opinions about products and services, techniques for assessing an online marketplace's trustworthiness, as well as algorithms for evaluation of online word-of-mouth effects on product sales.

The instructors will, where possible, use practical examples and examples of their research to illustrate quantitative methods in Internet Economy and their benefits to online retailers. Most algorithms are implemented in the programming language R. The course provides a brief introduction to using R for data analysis. Tests and practical assignments in R at the end of each chapter will help you to assess how well you covered the material.

Prior Knowledge: Basic knowledge of statistical reasoning will be useful, but not compulsory. No programming experience is required.

Gliederung:

1.Introduction  
  a) Definition Internet Economy
  b) Computational Methods in Internet Economy
  c) Computation Example with R

2. Consumer Behavior
  a) Modelling Purchase Decision Processes  
  b) Opinion Mining  
  c) Implementation in R  

3. Games in Internet Economy
  a) Foundations of Game Theory
  b) Reputation Mechanisms  
  c) Implementation in R  

4. Network Analyses
  a) Graphs and Networks  
  b) Link Analysis  
  c) Social Network Analysis  
  d) Implementation in R  

***Supplementary Chapter:  
Beginer's Guide to R: Introduction to Data Analysis in R

Detaillierter Inhalt:

The course starts with an introduction to the programming language R and provides an overview of the basic methods for data analysis and visualization.

Chapter 1. introduces the concept of Internet Economy and provides an overview of computational methods
- What is Internet Economy
- What are the differences between traditional and Internet Economy
- What analysis methods can be used for analyzing Internet data
- Example of a simple analysis in R

Chapter 2. analyzes Consumer Behavior in an Online Shopping Environment
a) Purchase Decision Processes of Online Consumers
- How consumer choices evolve in an online purchase process
- How consumers evaluate and compare product utilities
- What decision strategies they use  
- How they behave after purchase

b) Mining Online Consumer Opinions
- How consumer clickstream data can be used for analyzing and predicting their choices  
     Introduction to Markov Chains and Implementation in R

- What methods exist to analyze consumer opinions:
   Opinion Mining of Twitter, Implementation in R
   Sentiment Analysis with Bag-of-Words, Linear Regression and Support Vector Machines
   (Implementation in R)

Chapter 3. explores Trust Games in Internet Economy
a) Foundations of Game Theory
- How we define and classify games
- What games can be found in Internet Economy
- Implementation of game strategies in R

b) Reputation Mechanisms
- Examples of unfair behavior in the online environment
- What techniques can be implemented to identify and prevent unfair behavior
- Implementation of cheat identification and clustering of unfair users in R

Chapter 4. focuses on Network Analysis  
a)Graphs and Networks
- What are network and which networks do we have in Internet Economy
- Modeling networks as graphs
- Methods for analyzing graphs
- Examples of modeling and analyzing networks as graphs in R  

b) Link Analysis
- What is link analysis and why it is important to analyze links
- Algorithms for analyzing links in a network (Web Graph, PageRank, HITS) and their Implementation in R
- Analyzing product networks in R

c) Network Effects
- Definition of network effects (positive and negative, direct and indirect)
- What is Free-Riding and how to prevent it
- How to price network goods
- How to model propagation of product experience from one user to another (Implementation in R)
- How to create awareness about a new product using social networks

Lern-/Qualifikationsziele:

-

Lehrveranstaltungstyp:

Virtuelle Vorlesung

Interaktionsformen mit Betreuer/in:

E-Mail

Interaktionsformen mit Mitlernenden:

E-Mail, Chat, Foren

Kursdemo:

zur Kursdemo

Nutzung

Kurs ist konzipiert für:

Bachelorstudium Wirtschaftsinformatik, Wirtschaftswissenschaften

Formale Voraussetzungen:

Fortgeschrittenes Bachelorstudium

Erforderliche Vorkenntnisse:

Prior Knowledge: Basic knowledge of statistical reasoning will be useful, but not compulsory. No programming experience is required.

Hinweise zur Nutzung:

-

Kursumsetzung (verwendete Medien):

-

Erforderliche Technik:

-

Nutzungsentgelte:

für andere Personen als (reguläre) Studenten der vhb Trägerhochschulen nach Maßgabe der Benutzungs- und Entgeltordnung der vhb

Rechte hinsichtlich des Kursmaterials:

-

Verantwortlich

Anbieterhochschule:

Uni Passau

Anbieter:

Prof. Dr. Michael Scholz

Autoren:

Michael Scholz

Guido Schryen

Betreuer:

Olga Ivanova

Prüfung

Prüfungsangebot zur Lehrveranstaltung

Art der Prüfung:

schriftlicher Leistungsnachweis (Klausur)

Bemerkung:

–

Prüfer:

Prof. Dr. Michael Scholz

Prüfungsanmeldung erforderlich:

ja

Anmeldeverfahren:

Die Anmeldung zur Prüfung erfolgt über das vhb-Portal.

Prüfungsanmeldefrist:

01.03.2015 00:00 Uhr bis 31.05.2015 23:59 Uhr

Prüfungsabmeldefrist:

01.03.2015 00:00 Uhr bis 31.05.2015 23:59 Uhr

Kapazität:

–

Prüfungsdatum:

–

Prüfungszeitraum:

–

Prüfungsdauer:

–

Prüfungsort:

wird noch bekannt gegeben

Zuständiges Prüfungsamt:

Prüfungsamt der Heimathochschule

Zugelassene Hilfsmittel:

nicht programmierbarer Taschenrechner

Formale Voraussetzungen für die Prüfungsteilnahme:

Teilnahme am Online-Kurs

Inhaltliche Voraussetzungen für die Prüfungsteilnahme:

Kursinhalte

Zertifikat:

Ja (Je nach Anforderung der Heimathochschule bewerteter Schein)

Anerkennung:

–