Big data analytics capability can reshape competitive advantages for a service system. However, little is known about how to develop and operationalize a service system analytics capability (SSAC) model. Drawing on the resource based view (RBV), dynamic capability theory (DCT) and the emerging literature on big data analytics, this study develops and validates an SSAC model and frames its impact on competitive advantages using 251 survey data from service systems analytics managers in the U.S. Partial Least Squares (PLS)-Structural Equation Modeling (SEM) was used as a data analysis technique to develop and validate the hierarchical SSAC model. The main findings illuminate the varying importance of three primary dimensions (i.e., service system analytics management capability, technology capability and personnel capability) and various respective subdimensions (i.e., service system planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relationship knowledge) in developing overall analytics capabilities for a service system. The findings also confirm the strong mediating effects of three dynamic capabilities (i.e., market sensing, seizing and reconfiguring) in establishing competitive advantages. We critically discuss the implications of our findings for theory, methods and practice with limitations and future research directions.
Available for download on Monday, July 04, 2022