Transcript Chapter 4 - McGraw Hill Higher Education

Chapter 5
CLARIFYING THE RESEARCH QUESTION
THROUGH SECONDARY DATA AND EXPLORATION
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
Understand . . .
 The process of using exploratory research to
understand the management dilemma and work
through the stages of analysis necessary to
formulate the research question (and, ultimately,
investigative questions and measurement
questions).
 What is involved in internal data mining and how
internal data-mining techniques differ from
literature searches.
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Pull Quote
“It is critical to use serious business judgment
about what types of information could possibly
be useful and actionable for an organization.
We have seen enormous resources expended
on “data projects” that have no realistic
chance of payoff. Indiscriminately boiling a
data ocean seldom produces a breakthrough
nugget.”
Blaise Heltai, general partner,
NewVantage Partners
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Exploratory Phase Search Strategy
Discovery/ Analysis
Secondary Sources
Expert
Interview
Search
Strategy
Group
Discussions
Individual
Depth Interviews
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Integration of Secondary Data into the
Research Process
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Objectives of Secondary Searches
• Expand understanding of management
•
•
•
•
dilemma
Gather background information
Identify information to gather
Identify sources for and actual questions
Identify sources for and actual sample
frames
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Conducting a Literature Search
Define management dilemma
Consult books for relevant terms
Use terms to search
Locate/review secondary sources
Evaluate value of each source and content
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 Whiteboard technology makes an easier
discussion of symptoms relevant to the
management-research question hierarchy
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Levels of Information
Primary
Sources:
Memos
Letters
Interviews
Speeches
Laws
Internal records
Secondary
Sources:
Encyclopedias
Textbooks
Handbooks
Magazines
Newspapers
Newscasts
Tertiary
Sources:
Indexes
Bibliographies
Internet
search engines
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Integrating Secondary Data
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The
U.S. Government
is the world’s largest
source of data
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Types of Information Sources
Indexes/
Bibliographies
Directories
Dictionaries
Types
Handbooks
Encyclopedias
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Evaluating Information Sources
Purpose
Scope
Format
Evaluation
Factors
Audience
Authority
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The Evolution of Data Mining
Evolutionary Step
Investigative Question
Enabling Technologies
Characteristics
“What was my average
total revenue over the
last five years?”
Computers, tapes,
disks
Retrospective, static
data delivery
Data access (1980s)
“What were unit sales
in California last
December?”
Relational databases
(RDBMS), structured
query language (SQL),
ODBC
Retrospective, dynamic
data delivery at record
level
Data navigation
(1990s)
“What were unit sales
in California last
December? Drill down
to Sacramento.”
Online analytic
processing (OLAP),
multidimensional
databases, data
warehouses
Retrospective, dynamic
data delivery at
multiple levels
Data mining (2000)
“What’s likely to
happen to Sacramento
unit sales next month?
Why?”
Advanced algorithms,
multiprocessor
computers, massive
databases
Prospective, proactive
information delivery
Data collection
(1960s)
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Data-Mining Process
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Business
Research Process
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Stage 1: Clarifying the Research
Question
Management-research question hierarchy begins by
identifying the management dilemma
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Management-Research
Question Hierarchy
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SalePro’s Hierarchy
5-19
Formulating
the Research
Question
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Types of Management Questions
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The Research Question
Break
questions
down
Examine
variables
Determine
necessary
evidence
Fine-Tuning
Set
scope of
study
Evaluate
hypotheses
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Investigative Questions
Performance Considerations
Attitudinal Issues
Behavioral Issues
5-23
Gantt Chart
MindWriter Project Plan
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Key Terms
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Bibliography
Bibliographic Database
Data Mart
Data Mining
Data Visualization
Data Warehouse
Dictionary
Directory
Encyclopedia
Expert interview
Exploratory research
Handbook
Index
Individual depth interview
 Investigative questions
 Literature search
 Management question
 Measurement question
 Custom-designed
 Predesigned
 Primary sources
 Research questions
 Secondary sources
 Source evaluation
 Purpose
 Scope
 Authority
 Audience
 Format
 Tertiary sources
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Chapter 5
ADDITIONAL DISCUSSION OPPORTUNITIES
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
Snapshot: Blogs
Frequent chronological publication of personal thoughts &
web links
1 Billion blogs and growing
847 = average followers
61% are hobbyists
59% are men
79% have college degrees
Most have Facebook access
to their blogs
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Snapshot: Deception Line
Business intelligence is fertile ground.
Comprehensive literature search
Expert interviews
Former employee interviews
Monitor competitive publications
Attend presentations by executives
Share proprietary information
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Snapshot: Surfing the Deep Web
“ Although many popular search engines
boast about their ability to index information
on the Web, some of the Web’s information is
invisible to their searching spiders. The most
basic reason is that there are no links pointing
to a page that a search engine spider can
follow. Or, a page may be made up of data
types that search engines don’t index—
graphics, CGI scripts, or Macromedia Flash,
for example.”
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Snapshot: Cloud Affects Research
A computing environment where data and services reside
in scalable data centers accessible over the Internet.
“[The organization] pays only for [server] capacity that [it]
actually uses.”
“There’s no hardware to purchase, scale,
and maintain, no operating systems, database servers, or
application servers to install, no consultants and staff to
manage it all, and no need for upgrades.”
Data no longer reside on organizations servers
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Snapshot: Mining Feelings
Sentiment analysis and opinion mining: apply
computational treatment to opinion, sentiment, and
subjectivity in textual form.
Difficult comment analysis problems
False Negatives
Relative Sentiment
Compound Sentiment
Scoring Sentiment
Sentiment Modifiers
Conditional Sentiment
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Snapshot: Odin Text
“Most firms have a wealth of rich unstructured data within
their organization … that they need to understand.”
Monitors customer comments
Draws attention to new,
important trends
Calculates sentiment
Filters ‘noise’
User-determined analysis
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Snapshot: Online Professional Community
Sponsored content website
Shop-talk community
Professional collaboration community
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Research Thought Leaders
“Companies are certainly aware of data
mining, but most companies are not making
effective use of the data collected. They
are not so good at analyzing it or applying
these insights to the business.”
Gregory Piatetsky-Shapiro
president
Kdnuggets
5-34
PulsePoint: Research Revelation
33
The percent of financial executives
who have full confidence in their
current risk strategies.
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Percent of Activity
Data Mining in Business
Marketing
Financial
Analysis
Sales
Customer
Service
Fraud
Detection
Distribution Insurance
Network
Management
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Chapter 5
CLARIFYING THE RESEARCH QUESTION
THROUGH SECONDARY DATA AND EXPLORATION
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
Photo Attributions
Slide Source
7 Wavebreakmedia Ltd/Getty images
11 Courtesy of U.S. Census Bureau
23 DreamPictures/Blend Images LLC
27 Thinkstock/Jupiterimages
28 Steve Cole/Getty Images
29 Comstock Images/Jupiterimages
32 Courtesy of Anderson Analytics
33 Courtesy of 1to1Media
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