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Data-Driven Marketing Research Stream

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Data-Driven Marketing Research Stream

Data-Driven Marketing applies novel theories and tools to better understand human judgment, decision-making, and related processes.

Data-Driven Marketing Research Stream

Data-Driven Marketing applies novel theories and tools to better understand human judgment, decision-making, and related processes. This research stream focuses on developing new methods for testing marketing theories with structural equation modeling (SEM), emphasizing bridging design, and behavioral research using variance-based SEM. Research also incorporates important themes such as Digital Marketing and Consumer Neuroscience, providing additional innovative research and practice tools in marketing and consumer behavior.

The DdM research stream has the following Areas of expertise:

  • Digital Marketing;
  • Social Media;
  • Marketing Research;
  • Marketing Analytics;
  • Consumer Behavior;
  • Consumer Neurosciences;
  • Tourism Marketing.

Mission

Data-Driven Marketing aims to use multimethodological approaches combining behavioral manipulations with biometrics such as eye-tracking or skin conductance to inform the development of new hypotheses, which can be tested in future behavioral experiments. The focus will be placed on understanding consumer processes in digital environments and underlying neural mechanisms for information processing and decision-making, thus going beyond traditional views of marketing research and consumer psychology.

Objectives

  • Access recent experimental design advances in terms of digital marketing and neuromarketing;
  • Connect the research conducted at NOVA IMS with the needs of companies and public policy in terms of concrete applications in marketing;
  • Contribute to the development of specialized scientific and managerial employment in Portugal by providing access to cutting-edge knowledge in the field;
  • Organization of seminars and meetings with experts in the digital marketing and consumer neuromarketing fields;
  • Development of new variance-based SEM methodologies in order to continue bridging design and behavioral research.