title: A data-driven approach to reverse engineering customer engagement models: towards functional constructs.
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.
authors.affiliation: The 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.
authors.forename: Natalie Jane
authors.initials: NJ
authors.lastname: de Vries
authors.affiliation: Newcastle Business School, Faculty of Business and Law, The University of Newcastle, Newcastle, New South Wales, Australia.
authors.forename: Jamie
authors.initials: J
authors.lastname: Carlson
authors.affiliation: The 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.
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
mesh_terms: D003258:Consumer Behavior; D004738:Engineering / Q000379:methods*; D005246:Feedback; D006801:Humans; D020407:Internet; D008962:Models, Theoretical; D012111:Residence Characteristics; D061108:Social Media; D011795:Surveys and Questionnaires; D014584:User-Computer Interface