A data-driven approach to reverse engineering customer engagement models: towards functional constructs

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 mostl...

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Bibliographic Details
Published in:PloS one 2014-07, Vol.9 (7), p.e102768-e102768
Main Authors: de Vries, Natalie Jane, Carlson, Jamie, Moscato, Pablo
Format: Article
Language:English
Subjects:
Communities
Computer applications
Consumer Behavior
Contingency tables
Digital media
Engineering
Engineering - methods
Feedback
Feedback loops
Human behavior
Humans
Internet
Models, Theoretical
Open source software
Physical Sciences
Regression analysis
Research and Analysis Methods
Residence Characteristics
Reverse engineering
Scholars
Social Media
Social networks
Social Sciences
Surveys and Questionnaires
User-Computer Interface
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0102768