A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

Gang Chen (), Shuaiyong Xiao (), Chenghong Zhang () and Huimin Zhao ()
Additional contact information
Gang Chen: School of Management, Zhejiang University, Hangzhou 310027 Zhejiang, Peoples Republic of China
Shuaiyong Xiao: School of Economics and Management, Tongji University, Shanghai 200092, Peoples Republic of China
Chenghong Zhang: School of Management, Fudan University, Shanghai 200433, Peoples Republic of China
Huimin Zhao: Sheldon B. Lubar College of Business, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211

Information Systems Research, 2023, vol. 34, issue 4, 1513-1532

Abstract: As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black-box deep learning methods empirically promise us advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black-box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes.

Keywords: customer response prediction; voice chat; theory-driven deep learning; dynamic satisfaction; personalized preference; cross-view attention (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/isre.2022.1196 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:34:y:2023:i:4:p:1513-1532

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().