How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service

TLDR

The study examines how big data analytics serve as a key organizational resource for digitally enabled service innovation. Through an exploratory case study of four firms, the authors develop a model showing that BDA’s flexible, reprogrammable capabilities—such as data sourcing, storage, event and behavior prediction, rule‑based actions, and visualization—enable both automated and human‑material service practices that support individualized service delivery. The paper outlines implications for research and practice.

Abstract

The article reports on an exploratory, multisite case study of four organizations from the insurance, banking, telecommunications, and e-commerce industries that implemented big data analytics (BDA) technologies to provide individualized service to their customers. Grounded in our analysis of these four cases, a theoretical model is developed that explains how the flexible and reprogrammable nature of BDA technologies provides features of sourcing, storage, event recognition and prediction, behavior recognition and prediction, rule-based actions, and visualization that afford (1) service automation and (2) BDA-enabled human-material service practices. The model highlights how material agency (in the case of service automation) and the interplay of human and material agencies (in the case of human-material service practices) enable service individualization, as organizations draw on a service-dominant logic. The article contributes to the literature on digitally enabled service innovation by highlighting how BDA technologies are generative digital technologies that provide a key organizational resource for service innovation. We discuss implications for research and practice.