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A Data-Driven Approach to Reverse Engineering Customer Engagement Models: Towards Functional Constructs
- Source :
- PLoS ONE, Vol 9, Iss 7, p e102768 (2014), PLoS ONE
- Publication Year :
- 2014
- Publisher :
- Public Library of Science (PLoS), 2014.
-
Abstract
- Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.
- Subjects :
- Optimization
Questionnaires
Customer engagement
Reverse engineering
Social Sciences
lcsh:Medicine
Surveys
Research and Analysis Methods
Human behavior
Bioinformatics
computer.software_genre
Feedback
Data-driven
User-Computer Interface
Engineering
Sociology
Residence Characteristics
Surveys and Questionnaires
Computational Techniques
Humans
Medicine
Social media
lcsh:Science
Consumer behaviour
Marketing
Internet
Survey Research
Multidisciplinary
Mathematical Modeling
business.industry
Simulation and Modeling
lcsh:R
Quantitative Analysis
Empirical Methods
Consumer Behavior
Models, Theoretical
Data science
Communications
Research Design
Physical Sciences
lcsh:Q
The Internet
Symbolic regression
business
Social Media
computer
Mathematics
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....fa42dfd0de62629e0c29861edab36e0e