1. McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. CLARIFYING THE RESEARCH QUESTION THROUGH SECONDARY DATA AND EXPLORATION Chapter 5

2. 5-2 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.

3. 5-3 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

4. 5-4 Exploratory Phase Search Strategy Search Strategy Discovery/ Analysis Secondary Sources Individual Depth Interviews Expert Interview Group Discussions

5. 5-5 Integration of Secondary Data into the Research Process

6. 5-6 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

7. 5-7 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

8. 5-8 Whiteboard technology makes an easier discussion of symptoms relevant to the management-research question hierarchy

9. 5-9 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

10. 5-10 Integrating Secondary Data

11. 5-11 The U.S. Government is the world’s largest source of data

12. 5-12 Types of Information Sources Encyclopedias Directories Handbooks Types Indexes/ Bibliographies Dictionaries

13. 5-13 Evaluating Information Sources Authority Format Audience Evaluation Factors Evaluation Factors Purpose Scope

14. 5-14 The Evolution of Data Mining Evolutionary StepInvestigative QuestionEnabling TechnologiesCharacteristics Data collection (1960s) “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

15. 5-15 Data-Mining Process

16. 5-16 Business Research Process

17. 5-17 Stage 1: Clarifying the Research Question Management-research question hierarchy begins by identifying the management dilemma

18. 5-18 Management-Research Question Hierarchy

19. 5-19 SalePro’s Hierarchy

20. 5-20 Formulating the Research Question

21. 5-21 Types of Management Questions

22. 5-22 The Research Question Examine variables Examine variables Break questions down Fine-Tuning Evaluate hypotheses Determine necessary evidence Set scope of study Set scope of study

23. 5-23 Investigative Questions Performance Considerations Attitudinal Issues Behavioral Issues

24. 5-24 Gantt Chart MindWriter Project Plan

25. 5-25 Key Terms 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

26. McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. ADDITIONAL DISCUSSION OPPORTUNITIES Chapter 5

27. 5-27 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 Most have Facebook access to their blogs

28. 5-28 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

29. 5-29 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.”

30. 5-30 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.” “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

31. 5-31 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

32. 5-32 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 Draws attention to new, important trends Calculates sentiment Filters ‘noise’ User-determined analysis

33. 5-33 Snapshot: Online Professional Community Sponsored content website Shop-talk community Professional collaboration community

34. 5-34 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

35. 5-35 PulsePoint: Research Revelation 33 The percent of financial executives who have full confidence in their current risk strategies.

36. 5-36 Data Mining in Business Percent of Activity MarketingFinancial Analysis SalesCustomer Service Fraud Detection DistributionInsuranceNetwork Management

37. McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. CLARIFYING THE RESEARCH QUESTION THROUGH SECONDARY DATA AND EXPLORATION Chapter 5

38. 5-38 Photo Attributions