Secondary Data, Literature Reviews, and Hypotheses Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin

3-2 Learning Objectives  Understand the nature and role of secondary data  Describe how to conduct a literature review  Identify sources of internal and external secondary data  Discuss conceptualization and its role in model development  Understand hypotheses and independent and dependent variables

3-3 Cisco’s Connect Online

3-4 Nature and Scope of Secondary Data InternalExternal

3-5 What is a Literature Review?  A literature review is a comprehensive examination of available information that is related to your research topic

3-6 Reasons for Conducting a Literature Review  Clarify the research problem and questions  Uncover existing studies  Suggest research hypotheses  Identify scales to measure variables and methods

3-7 Assessing Quality of Secondary Data Purpose Accuracy Consistency Credibility Methodology Bias

3-8 Descriptive Variables Sought in Secondary Data Research  Demographic dimensions  Employment characteristics  Economic data  Competitive characteristics  Supply characteristics  Regulations  International market characteristics

3-9 Sources of Internal Secondary Data  Sales invoices  Accounts receivable reports  Quarterly sales reports  Sales activity reports  Online registration  Customer letters/ comments  Mail-order forms  Credit applications  Warranty cards  Past studies  Sales person expense forms

3-10 Primary Sources of External Data Popular Sources Scholarly Sources Government Sources NAICS Guidebooks Commercial Sources

3-11 Seth Godin’s Blog

3-12 Google Scholar

3-13 Lexus Nexus

3-14 Secondary Data, U.S. Government  U.S. Census Data  U.S. Census Reports  U.S. Department of Commerce Data  Additional Reports

3-15 Syndicated Sources  Commercial vendors collect information and sell the reports  80%+ of firms said they purchase and use reports and spend 10 hours per week analyzing this information

3-16 Consumer Panels  Benefits  Lower cost than other methods  Rapid availability and timeliness  Accurate reporting of sensitive purchases  High level of specificity  Risks  Sampling error (low minority representation)  Turnover of panel members  Response bias (SDR)

3-17 NPD Group

3-18 Sample Media Panel Data Sources

3-19 Store Audits  Examination of how much of a particular product or brand has sold at retail level  Product sales in relation to competition  Effectiveness of shelf space/POP displays  Sales at various price points  Effectiveness of POS coupons  Direct sales by store type, location, etc

3-20 Components of a Conceptual Model  A variable is an observable item that is used as a measured on a questionnaire  A construct is an unobservable concept that is measured by a group of related variables  Relationships are associations between two or more variables  Independent variables are variables or constructs that predict or explain the outcome of interest  Dependent variables are variables or constructs that researchers seek to explain

3-21 Conceptualization  Conceptualization refers to the development of a model that shows variables and hypothesized or proposed relationships between variables

3-22 Process of Conceptualization  Identify variables for research  Specify hypotheses and relationships  Prepare a diagram that represents the relationships visually

3-23 Relationships Among Variables  Hypotheses can suggest negative or positive relationships  An association between two variables in which they increase or decrease together suggests a positive relationship  An association between two variables in which one increases while the other decreases describes a negative relationship

3-24 Exhibit 3.8 A Model of New Technology Adoption

3-25 Santa Fe Grill: Developing Research Questions and Hypotheses  What research questions should be examined?  What hypotheses should be tested?  Should the literature search be expanded? If so, how?

3-26 Hypotheses  A hypothesis is an empirically testable though yet unproven statement developed in order to explain phenomena  Types of hypotheses include  Null or Alternate  Nondirectional  Inverse (negative) directional  Direct (positive) directional

3-27 Parameters and Sample Statistics  A parameter is the true value of a variable, while a sample statistic is the value of a variable based on estimates from a sample

3-28 Examples: Null Hypotheses  There is no significant difference between the preferences toward specific banking method exhibited by white-collar customers and blue-collar customers.  No significant differences exist in requests for specific medical treatments from emergency walk- in clinics between users and nonusers of annual preventive maintenance health care programs.

3-29 Examples: Alternate Hypotheses, Nondirectional  There is a significant difference in the satisfaction levels reported for iPod users and those reported for Zune users  Significant differences exist between males and females exist in the hours spent playing online games

3-30 Examples: Alternate Hypotheses, Inverse  Students with high GPAs consume less alcohol than those with lower GPAs.  The more pressure to close sales perceived by salespeople, the fewer follow up, “relationship-building” sales calls made.

3-31 Examples: Alternate Hypotheses, Direct  Positive study habits are related positively to GPA.  Students with high GPAs and good overall study habits will exhibit high tendencies to participate in campus leadership opportunities.

3-32 Marketing Research in Action: Santa Fe Grill  Should the owners restate their questions? Why or why not? Suggest how the questions could be restated.  Develop a set of hypotheses that might be used to investigate the interrelationships between customer satisfaction, store image, and loyalty.