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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

Natalie Jane de Vries1, Jamie Carlson2, Pablo Moscato1

  • 1The Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Newcastle, New South Wales, Australia; School of Electrical Engineering and Computer Science, Faculty of Engineering and Built Environment, The University of Newcastle, Newcastle, New South Wales, Australia.

Plos One
|July 19, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to model online customer engagement. It uses data-driven analysis to uncover functional constructs, offering a novel approach to understanding consumer behavior.

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Area of Science:

  • Computational Social Science
  • Marketing Analytics
  • Behavioral Economics

Background:

  • Online customer engagement is a key research area due to increased internet and social media use.
  • Existing research often relies on hypothesis-driven approaches, leading to debates on customer engagement constructs.
  • A data-driven perspective is needed to model complex online consumer behaviors.

Purpose of the Study:

  • To propose a novel, computational methodology for reverse engineering consumer behavior models.
  • To develop a data-driven approach for understanding online customer engagement.
  • To generalize the methodology for diverse human behavior studies using questionnaire data.

Main Methods:

  • The proposed methodology involves five stages: symbolic regression analysis, graph building, community detection, result evaluation, and analysis of feedback loops.
  • Community detection is employed to identify emergent 'functional constructs' from questionnaire items.
  • This approach infers constructs directly from data, bypassing traditional theory-driven assumptions.

Main Results:

  • The community detection method successfully partitioned questionnaire items into meaningful 'functional constructs'.
  • These data-derived constructs offer insights into online consumer behavior and engagement.
  • The results demonstrate the consistency and potential of the proposed data-driven modeling technique.

Conclusions:

  • The novel methodology provides a data-driven framework for modeling human behavior, specifically online customer engagement.
  • This approach allows for the inference of functional constructs directly from data, complementing existing theoretical models.
  • The proposed method offers a generalized and adaptable tool for future research in consumer behavior and related fields.