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EMAC 2024 Annual


Drivers of customer satisfaction and recommendation in B2B context: A moderated sequential-mediation model using text mining in user-generated content
(A2024-119341)

Published: May 28, 2024

AUTHORS

Swagato Chatterjee, Queen Mary University of London, UK; Prathamesh Kittur, Indian Institute of Technology Madras; Sehar Aejaz ., National Institute of Technology Srinagar; Shaza Kawoosa, National Institute of Technology Srinagar

ABSTRACT

This paper addresses the scarcity of research on user-generated content (UGC) in the business-to-business (B2B) context by exploring the drivers of customer outcomes in B2B relationships by analyzing 8245 customer reviews through text-mining and topic modeling. Applying the technology adoption model and the value-attitude-behavior model, the authors use partial least squares structural equation modeling (PLS-SEM) to create and validate a structural model. We found that perceived ease of use, perceived usefulness, and satisfaction sequentially mediate the relationship between product/service image and recommendation. Moreover, construal level moderates the serial-mediation relationship suggesting an increased importance of service image in low construal situations, characterized by long product-usage experience and/or low customer company size. This research contributes to the UGC and B2B literature, showcasing the practical application of UGC in B2B relationship marketing.