MR_Chap003

Value of Secondary Data and Literature Reviews

• Secondary Data Defined: Data gathered for a purpose other than the current research.

  • Internal Secondary Data: Collected within an organization for its operational needs.

  • External Secondary Data: Collected by outside organizations, enhancing its importance in marketing research.

• Retailers increasingly use external secondary data from multiple sources.

• Evolving role of secondary research analysts: from mere data collectors to active database creators and strategic advisors.

Conducting a Literature Review

• A literature review is a comprehensive examination of existing secondary information related to the research topic.

• Importance:

  • Provides background and context for the study.

  • Identifies existing information on the issue.

  • Defines important constructs pertinent to the study.

  • Keeps researchers updated on current thinking.

  • Suggests hypotheses for investigation.

  • Identifies measurement scales and methodologies previously used in research.

Evaluating Secondary Data Sources

• Researchers should establish criteria for evaluation:

  • Purpose: How does the data relate to current research objectives?

  • Accuracy: Ensure data is from credible sources and up to date.

  • Consistency: Corroborate findings with multiple sources.

  • Credibility: Assess the reliability of the data providers.

  • Methodology: Identify flaws that can affect the validity and reliability of the data.

  • Bias: Recognize potential motivations behind the reported data.

Secondary Data and the Marketing Research Process

• Secondary data can sometimes adequately address research problems directly.

• Categories of secondary data sources:

  • Demographic: Information on population trends and characteristics.

  • Economic: Data on income, businesses, and taxation.

  • Competitive: Information on competitors and market types.

  • Regulatory: Data on legal standards affecting businesses.

  • Social Media: Engagement metrics and tracking.

  • Digital Advertising: Metrics like conversion and purchase rates.

Internal Sources of Secondary Data

• Internal Data: More accessible and cost-effective.

  • Includes sales, accounting, and process data.

  • Can analyze:

    • Product performance.

    • Customer satisfaction metrics.

    • Distribution strategies.

    • Market segmentation insights.

External Sources of Secondary Data

• Popular Sources:

  • Online newspapers, academic journals, government databases (e.g., census data, NAICS), commercial sources.

• Syndicated Data:

  • Data collected and sold by research firms.

  • Methods include consumer panels and store audits, enhanced by optical-scanner tech.

  • Media panels gather data on media consumption.

Synthesizing Secondary Research for Literature Review

• Research inconsistencies necessitate scrutiny of methodology:

  • Factors contributing to discrepancies include the categories measured and sample methods.

  • Must be diligent in interpreting variable definitions and reported data accuracy.

Developing a Conceptual Model

• Literature reviews facilitate creating models summarizing the hypothesized relationships.

• Necessary for research questions needing complex relationships.

• Model Development Process: Identify information needs and data collection strategies based on research objectives.

Variables, Constructs, and Relationships

• Variables: Observable measures in research, directly measurable.

• Constructs: Abstract concepts that must be measured through sets of variables (e.g., service quality).

• Relationships:

  • Independent Variables: Predict outcomes of dependent variables.

  • Dependent Variables: Outcomes being explained.

• Literature reviews aid in identifying and defining these constructs effectively.

Developing Hypotheses and Drawing Conceptual Models

• Hypotheses Types:

  • Descriptive Hypotheses: Address specific issues.

  • Causal Hypotheses: Explore theoretical relationships.

• Conceptualization aids in diagramming theoretical models, elaborating hypotheses.

• Dedicated section in literature review to present the conceptual framework.

Hypothesis Testing

• A hypothesis is a tentative explanation of observations, predicting relationships between variables.

• Null Hypothesis (H0): Assumes no relationship exists.

• Alternative Hypothesis (H1): Posits a relationship.

• Distinction between population parameters and sample statistics.

• Validation: Failing to reject H0 does not confirm it as true; acceptance of H1 shifts the burden of proof.